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Informatics Practices ( IP ) Classes Near Me, Informatics Practices ( IP ) Tutor Near Me, Informatics Practices Tuition Near Me :

Informatics Practices ( IP ) Online / Home ( Tutor, Tuition Classes, Teacher, Lesson, Trainers, Tutorials, Coaching Classes, Private Tutor, Lectures, Coding Workshops, Hands On Coding Classes, Vocational Skills, Seminars, online Informatics Practices ( IP ) Tuition Classes near me offline, online Informatics Practices ( IP ) Tutors near me offline, online Informatics Practices ( IP ) Teachers near me offline, online Informatics Practices ( IP ) Online Teachers near me, offline Informatics Practices ( IP ) Home Teachers near me Greater Noida West, online Informatics Practices ( IP ) Online Tutors near me, offline Informatics Practices ( IP ) Home Tutors near me in Greater Noida West, online Informatics Practices ( IP ) Online Lessons near me, offline Informatics Practices ( IP ) Home Lessons near me Greater Noida West, online Informatics Practices ( IP ) Live Teachers near me, online Informatics Practices ( IP ) Online Tuition Classes near me ) for Class 9, Class 10, Class 11, Class 12, College Level Courses, Adult Recreational Learning Courses near me in Greater Noida, Greater Noida West, Delhi NCR, Gurgaon ( Gurugram ), Ghaziabad, Faridabad, Mumbai, Pune, Hyderabad, Bangalore ( Bengaluru ) and other major locations within India & Abroad ( British Columbia - BC, Manitoba, Canada - CA ) :


 

Informatics Practices ( IP ) - Introduction & Scope :

Informatics Practices ( IP ) is a research oriented discipline for learners of all levels and all ages. It derives most of it's pedagogy from the main specialisation of Computer Sciences ( CS ). Latter attributes itself to core research disciplines encompassing Distributed Databases, Python Coding & Python Programming, Internet of Things ( IOT ), Networking components, Information Technology ( IT ), Distributed OS Operating Systems, Computer Science ( CS ), and CCT Computers and Communications Technology. These research disciplines build upon each other to take the field of Informatics Practices or IP to next level.

 

Allied disciplines that are equally important for Informatics Practices or IP, from an application perspective are Data Science ( DS ), SAAS, IAAS, GT GeoSpatial systems, PAAS, MM Multi Media applications, Mass Media Studies, ML or Machine Learning , AI or Artificial Learning , Web Apps or Web Applications, DS or Data Sciences, Languages, Linguistics, ICT Information and Communications Technology, Translation Studies, Typography and Computer Applications, Software Development Life Cycle or SDLC based Tools and Platforms. 

 

The subject of Informatics Practices ( IP ) has significant ramifications and very close applications to almost all of the industrial sectors. Informatics practices or IP also has very similar repercussions for the wide ICT and internal CCT applications, as well as cross functional IT departments embedded within them. Informatics Practices IP strongly emphasises upon the data crunching capabilities, data processing efficiencies and data translation effectiveness strategies and practices.

 

With the ever increasing size of data like big data applications, it's quite pertinent to look for for more efficient and effective tools for the right, accurate and precise interpretations. 

 

Wise Turtle Academy understands well about the significance of the subject of Informatics Practices ( IP ) and, thus, rolls out related learning formats. Latter encompass, IP informatics practices home IP coaching class near me offline, online IP tutors near me online, online IP tutor near me online, informatics practices IP online tuition class near me Greater Noida West, informatics practices IP online teachers near me, home IP tutors near me offline Greater Noida, Gautam Budh Nagar, Uttar Pradesh, India and online IP classes near me Greater Noida West offline.

 

Raw data once meaningfully processed becomes Information ( by leveraging Information Technology IT , Computer Science CS , Programming - Java, Python , Go , R, Kotlin and others ) and upon further combination with other disciplines like Statistics, Mathematics, Science, Operations Research and other related specialisations, offer deep insights into various patterns and inter - dependencies among various participating entities. Thus, Informatics Practices ( IP ) is a critical research oriented discipline that needs to be deeply researched upon, as well as further developed.

Computational systems are the subject of informatics. The ACM Europe Council and Informatics Europe define informatics as computer science and computing as a profession, both of which have as their primary idea the transformation of information. In other nations, the term "informatics" is used in the context of library science with a different connotation, where it is synonymous with data storage and retrieval. 

 

Depending on local usage, the term "informatics" can refer to a variety of theoretical or practical subjects, including information systems, information science, information theory, information engineering, information technology, information processing, and more. The term informatics is used in Germany to describe modern computer science almost perfectly. As a result, "informatics" is typically translated as computer science or occasionally information and computer science in universities in continental Europe.

 

However, technical universities may translate it as CSE or computer science & engineering. The term "informatics" is, however, most frequently used in the US in relation to data science, library science, or its applications in healthcare or health informatics, where it first arose. This phrase refers to social computing according to the University of Washington. This phrase refers to both natural computation and neural computing in various nations. The Canadian government employs the phrase to describe operational entities that provide computer and network services to several departments. 

Even a lot of analytical research had been carried out around Informatics by many researchers. For instance, when the researchers created the Informatik - Anlage for the Quelle mail - order management, being one of the earliest commercial uses of data processing, in 1956, German informatician Karl Steinbuch and engineer Helmut Gröttrup created the term "Informatatik". Further research continued to go on for developing more complex distributed data processing information systems.

 

On similar lines and deeper research, "Informatics: Automatic Information Processing" is the title of a paper written by Steinbuch and published in April 1957. They leveraged the combination of mnemonics like Information + -ics. Latter is a morphology approach that "uses the accepted form for names of sciences, as conics, mathematics, coding, linguistics, optics, or matters of practice, as economics, politics, programming, algorithms, data analysis, information processing or tactics". In fact more multi functional domains are closely involved with data and the intelligible aspects of the information extracted.

 

Therefore, linguistically speaking, the meaning is easily extended to include both the science of information and the practise of information processing. Technical universities or the German equivalents of institutes of technology, typically translate the German word informatik to English as computer science or computer science & engineering. Thus, depending on the situation, the term "informatics" might also be rendered as "computer science," "scientific computing," or "information and computer technology."

Informatics Practices ( IP ) Teacher Near Me, Online IP Tuition Classes Near Me :

Apropos above reasoning, we at ​Wise Turtle Academy strive to provide good Quality "CBSE Class 12th Informatics Practices ( IP ) Online Tuition Classes, IP online Informatics Practices Tuition near me, online IP tutoring services near me at home offline, online IP tutoring near me online, online IP teachers near me online" in Greater Noida. These classes are successfully delivered through Online Tutoring, Home Tutoring and Classroom Tutoring modes to the students of schools and colleges in the areas of Greater Noida, Noida Extension and Greater Noida West.

Wise Turtle Academy - Geographical Presence & Scope :

Services' Coverage - International ( Overseas & Abroad ) :

Canada ( British Columbia ( BC ), Canada ( Ca ) & Manitoba ( University of Manitoba ), Canada ( Ca ) ), Australia, Mexico, London, Singapore, Hong-Kong, United States ( Florida, Carolina, New Jersey, Washington ), United Kingdom, Abu Dhabi, Sri Lanka, Bhutan, Nepal, Burma, Malaysia, Bangladesh, Dubai, Africa ( South Africa ), Netherlands, Denmark, Korea, Japan, Asia-Pacific ( APAC ), Americas ( AMER ), Europe, Warsaw, Poland, Russia, France, Germany, Spain, Greece, Belgium, Switzerland, 

India, United States, Brazil, United Kingdom, Russia, Vietnam, Turkey, South Korea, Philippines, Indonesia, France, Mexico, Spain, Germany, Argentina, Poland, Canada, Ukraine, Colombia, Taiwan, Egypt, Italy, Japan, Malaysia, Romania, Netherlands, Pakistan, South Africa, Bulgaria, Australia, Morocco, Lithuania, United Arab Emirates, Sweden, Chile, Peru, Singapore, Portugal, Unknown Region, Czechia, Serbia, Thailand, Saudi Arabia, Israel, Iraq, Hungary,

 

Ecuador, Algeria, Greece, Switzerland, Venezuela, Austria, Belgium, Georgia, Nepal, Macedonia, Hong Kong, Bangladesh, Sri Lanka, Kazakhstan, Jordan, Norway, Slovakia, Jamaica, Finland, Ireland, Azerbaijan, Trinidad & Tobago, Mongolia, New Zealand, Belarus, Latvia, Denmark, Dominican Republic, Kenya, Guatemala, Cambodia, Palestine, Tunisia, Costa Rica, Iran, Slovenia, Bolivia, Uzbekistan, Paraguay, Uruguay, Honduras, Panama, Croatia, Myanmar (Burma),

 

Kuwait, Qatar, Ghana, Haiti, China, Moldova, Bahamas, Luxembourg, Nicaragua, Libya, El Salvador, Bosnia & Herzegovina, Ethiopia, Guam, Cyprus, Syria, Albania, Nigeria, Armenia, Lebanon, Brunei, Angola, Botswana, Zimbabwe, Oman, Estonia, Côte d’Ivoire, Tanzania, Mauritius, Cameroon, Bahrain, Seychelles, Iceland, Guadeloupe, Montenegro, Maldives, Namibia, Comoros, Congo - Brazzaville, Martinique, Senegal, Afghanistan, Aruba, Turks & Caicos Islands,

 

Mozambique, Curaçao, St. Lucia, Yemen, Uganda, Zambia, Congo - Kinshasa, Malawi, Benin, Mauritania, Madagascar, Puerto Rico, and, other countries.

Services' Coverage - Greater Noida, Greater Noida West, Gautam Budh Nagar, Uttar Pradesh, UP, India
Core Areas :
  

Pari Chowk, Omaxe NRI City, Eldeco Greens, Unitech Habitat, Ace Infrastructure, Super Tech Czar Suites Omicron 1/2/3, IFS Society Villas Pari Chowk, SDS NRI Residency Pari Chowk, The Palms Pocket P 7, ATS Pristine, Jaypee Greens ( Sun Court 1, Crescent Court 3 ) Pari Chowk, Metro Line, Sectors Alpha 1, Alpha Commercial Belt, Beta 1, Mu 1, Alpha 2 Main Market, Mu 2, Ansals Golf Links, Eldeco Meadows, Mu 3, Paramount Golf Foreste Studio Apartments, Mu 4, Beta 2, Gama2, Shisham Estate Gama 1 ( Officer's Colony ), GautamBudhNagar, Uttar Pradesh, Kadamba Estate, Gamma 1 ( Pocket A Officer's Colony, Pocket B , C, D, E, F, G ), Omega 1, Eta 1, Gamma 2, Omega 2, Eta 2, Chi 1, Omega 3,Eta 3, Chi 2,

 

Omega 4, Chi 3, Eta 4, Xu 1, Phi 1, Xu 2, Phi 2, Xu 3, Sigma 1, Phi 3, Sigma 2, Jalvayu Vihar Society, Sigma 3, Army Welfare Housing Organisation AWHO Twin Towers Societies, CGEWHO, Gamma 2, Sigma 4, Zeta 1, Builder's Area P 1 2 3 4 5 6 7 8 9, Eachhaar, Pari Chowk, Cross Streets, Site A, Estates, Pockets, Blocks A B C D E F, LG Chowk, Gama 1, Pi 1, Gamma 1 ( Officers Colony ), Sigma 1, Mu 1, Chi 1, Tau 1, KP 1, Xu 1, Sigma 2, Mu 2, Pi 2, Gamma 2, Ecotech 1, Gama 2, Xu 2, Tau 2, Alpha 1, Site B, Chi 2, Sigma 3, Pi 3, Tau 3, Mu 3, Alpha 2, Beta 1, Xu 3, Chi 3, Beta 2, Xi 1, Sigma 4, Mu 4, Tau 4, Pi 4, Chi 4, Xu 4, Xi 2, KP 2, Phi 1, Xi 3, KP 3, Phi 2, Gautam Budh Nagar, Uttar Pradesh, Omicron 1 A, KP 4, Xi 4, Omicron 2,

 

Tau 5, KP 5, Pi 5, Xi 5, Chi 5, Mu 5, Omicron 3, Zeta 1, Zeta 2, Eta 1, Eta2, Delta 1, Ecotech 2, Delta 2, Knowledge Park 1, Delta 3, Knowledge Park 2, Delta 4, Knowledge Park 3, Delta 5, Omaxe Connaught Place Mall, Phi 3, NTPC Colony, Rampur Jagir Chowk, Alpha Commercial Belt, UPSIDC Site C, Surajpur, Uttar Pradesh, Site 1, Site 2, UPSIDC Site C, Site 3, Xi 6, Phi 4, Sigma 5, Site 4, Eldeco Greens, Site 5, Army Welfare Housing Organisation AWHO, Site D, Sharda Hospital, Sector 150, ATS Pristine Sector 150 Towers 1 2 3 4 5 6 7 8 9 10, Sector 144, Phi 5, Unitech Horizon, Ecotech 3, Chi 6, Sector 143, Unitech Habitat, Sector 27, Paramount Golf Foreste, Ansals Golf Links, Vrinda City, Sector Phi 4, Greater Noida, Uttar Pradesh,

 

Jaypee Greens ( Kaveri Gate, Narmada Gate - Star Courts 1 2 3 4 5 6 7 8 9 10 ), NTPC Society, Site E, ATS Paradiso, Phi 6, Knowledge Park 4, Swarna Nagari ( Pockets A B C D E F ), Chi 7, NRI City 1 2 3, GautamBudhNagar, Uttar Pradesh, Silver City 1 2, ACE Platinum, Ecotech 4, The Oasis, The Palms, Builder's Area P 1 2 3 4 5 6 7 8 9, Eachhaar, AVJ Heightss, Migsun Green Mansion, Shivalik Residency, Paramount Villas Society, MSX Alpha Homes, Phi 7, CGEWHO Greater Noida, Site F, Pi 6, Purvanchal Heights, Eldeco Meadows, Knowledge Park 5, Tughalpur Village, Sector 101, Kasna, Yamuna Expressway, Ecotech 5, Sector 31, Wipro Circle Chowk, Sector 32, Rail Colony, Sector 33, Greater Noida Expressway, Prateek Residency, Phi 4, 

 

Sector 34, Rail Society, Sector 35, Toy City, Sector 36, Pi 7, Noida Extension ( Sector 1 Arihant Arden ), Sector 37, Jewar, sector 38, Rail Vihar, sector 39, Jal Vayu Vihar, Sector 40, ATS Paradioso, Ecotech 6, Sector 41, Tilapta Village, Ashiana Orchids, Sector 42, Site G, Tilapta Circle Chowk, Gautam Budh Nagar, Uttar Pradesh, Sector 43, Sector 45, Knowledge Park 6, Ecotech 7, Sectors ( 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150 ), Sector 76 Metro Station, Ecotech 8,

 

Site H, Sector 78 ( Aditya Urban Casa Towers A B C D E F G H I, Hyde Park, Civitech Stadia, Alistair Meadows, Amrapali Princely Estate, Mahagun Mirabella, Sports City, Mahagun Moderne ), ​Swarna Nagari ( Pockets A, B, C, D, E, F ), Phi 4, Zeta 2, Zeta 3, HIG Apartment Omicron 1, Chi 4, Xi 4, Zeta 4, Pi 1, Omicron 2, Ashiana Orchids, Pi 3, MSX Alpha Homes, Rail Vihar, Omicron 3, Pi 4, Swarna Nagri, Knowledge Park 1 ( KP 1 ) KP I, Pi 5, Rampur Jagir, Advocate's Colony, The King's Reserve, Purvanchal Heights, Site 1, Ecotech 1, Site 2, Ecotech 2, Site 3, Ecotech 3, Site 4, ATS Paradioso, Ecotech 4, Site A, The Oasis, IFS Villas, Shivalik Residency, Site B, Oasis Venetia Heights, UPSIDC Site C, Uttar Pradesh, Stellar Mi Legacy,

 

Site D, Silver City 1 2 3, Migsun Green Mansion, Site E, DesignArch, Site 5, Eachhaar, Site 6, Makora, Site 7, Knowledge Park 2 ( KP 2 ) KP II, Rail Colony, Jal Vayu Vihar,  Cherry County, Knowledge Park 3 ( KP 3 ) KP III, Ashirwaad Apartments, Tughalpur Village, NTPC Colony, The Palms, Jagat Farm, Kulesra, Officer's Colony, NRI City 1 2 3, NRI Colony , LG Chowk, Surajpur, Kasna Village, The Oasis, AVJ Heightss, ACE Platinum, Alpha Homes ), Greater Noida West ( Gaur City I - 1, Gaur City II - 2, Gaur Chowk ), Pari Chowk, LG Chowk, Gamma 1, Gama 1, Gamma 2, Gama 2, Alpha 1, Xu 1, Mu 1, Pi 1, Chi 1, Sigma 1, Blocks A B C D E F G H I J K L M, Ecotech 1, Eachhaar, Alpha 2, Beta 1, Beta 2, Xi 1, Xi 2, Phi 1, Phi 2,

 

Omicron 1, Omicron 2 A, Omicron 3, Zeta 1, Zeta 2, Eta 1, Eta2, Delta 1, Delta 2, Knowledge Park 1, Knowledge Park 2, Knowledge Park 3, Omaxe Connaught Place Mall, Rampur Jagir Chowk, Alpha Commercial Belt, Surajpur, Sharda Hospital, Sector 150, ATS Pristine Sector 150, Alpha 2, Xu 2, Mu 2, Pi 2, Chi 2, Sigma 2, Surajpur Site 2, Tau 2, KP 2, Omega 2, Sector 144, Sector 143, Sector 27, AWHO, CGEWHO, Swarna Nagari ( Pockets A B C D E F G H I J K L ), Tughalpur Village, Kasna, Greater Noida Expressway, Sector 31, Sector 32, Sector 33, NRI City, Blocks, Sectors, Silver City 1 2 3 4 5, Sector 34, Alpha 3, Xu 3, Mu 3, Pi 3, Chi 3, Sigma 3, Surajpur Site 3, Tau 3, KP 3, Omega 3, Delta 3, Sector 35, Rampur, Sector 36,

 

Alpha 4, Xu 4, Mu 4, Pi 4, Chi 4, Sigma 4, Surajpur Site 4, Tau 4, KP 4, Omega 4, Delta 4, Sector 37, sector 38, sector 39, Sector 76, Ace Platinum, The King's Reserve, Sector 78 ( Aditya Urban Casa Towers A B C D E F G H I, Hyde Park, UPSIDC Site B, Surajpur, Greater Noida, Uttar Pradesh, Block C, Amrapali Princely Estate, Civitech Stadia, Mahagun Mirabella, Mahagun Moderne ), Migsun Mansion Greens, Shivalik, Jaypee Greens ( Narmada Gate Star Court Tower 1 2 3 4 5 6 7 8 9 10 ), Paramount Golf Foreste, Purvanchal Heights, AVJ Heights, MSX Alpha Homes, Rail Vihar, Rail Colony, Jal Vihar, Judge Society, IFS Villas, Xu 4, Pi 2, Ashirvad Apartments, Sector 40, IRDO Colony Apartment, Builder's Area P 1 2 3 4 5 6 7 8 9,

 

Alpha 5, Xu 5, Mu 5, Pi 5, Chi 5, Xi 5, DesignArch, Shivalik, Makora, Tilapta, AVJ Heights, Sigma 5, Surajpur Site 5, Tau 5, KP 5, Omega 5, Delta 5, Sector 41, Sector 42, Noida Extension ( Sector 1 Arihant Arden ), Sector 43, Ace Platina, Ace Platinum, Sector 45, Greater Noida West ( Gaur Chowk, Gaur City 1, Gaur City 2, Saya Zoin ), Surajpur Site 1, Tau 1, KP 1, Omega 1, Delta 1, Media Village Sas Ltd, Phi 4,

Peripheral Areas - Inner :
Migsun Green Mansion, Stellar MI Legacy, Ratan Vihar, Defence Empire I, Tilpata Golchakkar, Devla, Village Tilpatta, UPSIDC Site C, Site F, Site G, Site H, Site I, Site J, Site K, Site L, Site M, Surajpur Industrial Area, Tata Enclave, Anand Ashray Complex, Chorisia Speciosa Estate, NHPC Society, CGEWHO Project, Kendriya Vihar, Nirman Kunj CPWD Society, Unitech Heights, Vrinda City Apartment, Shri Kripa Kunj, Icon Apartments, Adarsh Vihar Society, Ottoman Turkish Baklava, Green Noida Vertical Garden, Purvanchal Royal City Phase 1, Earthcon Casa Grande 2, Sampada Livia, Express Park View 1 2 Apartments, Durva Greens, Chuharpur Market, Lohiya Enclave, Red Building, Lal Building, Omicron 1A Kali Building,

 

Omicron 1B, Omicron 1C, Omicron 1D, Possession Office, Omaxe Orchid Avenue, Omaxe Society, EWS Society, Stellar MI Citihomes, Ropan, Eldeco Residency Greens, Cassia Estate Society, 

BSF Housing Society, Aichhar, Parsvnath Estate, Swarn Nagri ( Pockets A B C D E F G H I J K L ), Ambey Bharti Apartment, LG Cooperative Housing Society, Jamia Apartment, Khushboo Apartments, Khushboo Sahkari Awas Samiti, Sun Twilight Villas, Jyoti Kiran Society, White House Apartments, Unitech Cascades Apartment, Tower 1, Tower 15, Tower 18, Plumeria Garden Estate, Fairway Apartment Tower D, Nambardar Residence, Ajju Prajapati Town, Anjana Welfare Society, Paradise Dream City, Defence Empire 2, Eden Golf Group,

 

Victory Enclave, Shyam Enclave, Mahamaya Enclave, Royal Paradise, Prithvi Greens, Bhoomi Greens Phase 2, Shiv Enclave, Kartik Nagar, Himalaya Hi Tech City, Ajnara City, Galactic City, Ace City, Ace Divino, ATS Destinaire, Arihant Ambar, Flora Heritage, The Palm Valley, Akshardham Colony, Noor Colony, Vidyapati Nagar, Jalpura, Jamia Nagar Colony, Tusiana Village, Tusyana, Supertech, Amrapali West, Udyog Vihar, Brahmpur Rajraula, Nawada, Parsvnath Privilege, Parsvnath Edens.

Peripheral Areas - Outer :

NTPC Society, Nirman Vihar, Techoma Estate, Cassia Fistula Estate, Rasoolpur Rai, Jaitpur Village, Ekanki Enclave ( Block G ), Vimal Sadan Society, Cassia Sigma, Sigma Group Housing Society, Chorosia Estate, Grand Forte, BSNL Society, Kyampur, Ecotech Extension 1, Migsun Ultimo Sun 3, Omaxe Palm Greens, KKS Homes, Ghodi Bachheda, Ghori Bachhera, MamaPikin Suya House, Ebony Estate, Austonia Estate, ATS Paradiso, Lagerstroemia Estate, Cassia Nodosa Estate, Chakrasia Estate, Himsagar Apartment, Mitra Enclave, Surajpur Site 4, Godrej Golf Links, Ansal Golf Links 1, Surajpur Site 1, Surajpur Site 2, Surajpur Site 3, Surajpur Site 4, Surajpur Site 5, Surajpur Site 6,

 

Block F, Block H, Block I, Block J, Block K, Block L, Block M, 1st Cross Street, 2nd Cross Street, 3rd Cross Street, 4th Cross Street, 5th Cross Street, 6th Cross Street, 7th Cross Street, 8th Cross Street, 9th Cross Street, 10th Cross Street, Service Road, First Avenue, Second Avenue, Third Avenue, Fourth Avenue, Fifth Avenue,1st Avenue, 2nd Avenue, 3rd Avenue, 4th Avenue, 5th Avenue, 6th Avenue, 7th Avenue, 8th Avenue, 9th Avenue, 10th Avenue, ATS Dolce, Amrapali Grand Apartment, Migsun Wynn, SKA Metro Ville, Cluster ETA 2, 1st Cross Avenue, 2nd Cross Avenue, 3rd Cross Avenue, 4th Cross Avenue, 5th Cross Avenue, 6th Cross Avenue, 7th Cross Avenue, Block B, Block C,

8th Cross Avenue, 9th Cross Avenue, 10th Cross Avenue, 11th Cross Avenue, Main Road, Tilpata Karanwas, Luharli, Ajayabpur, Eachachhar, Accher, Habibpur, Gujarpur, Jhatta, Gulavali, Malakpur, Judge Society, Amit Nagar, NTPC Anandam Society,  Purvanchal Silver City 2, Unitech Horizon, Alistonia Estate, Block A, Sector 34, Gulistanpur Village, Gulmohar Estate, Theta 1, Theta 2, Theta 3, Theta 4, Theta 5, Delhi Police Housing Society, Gaur Atulyam, ​Eldeco Mystic Greens, Palash Estate, Pocket 4, Khadar Ke Marhiya, Dadha, Sadar Tehsil, GNIDA BHS 16 Housing Scheme, Bironda, Haier Industrial Park, Sakipur, Makora, Tugalpur, NSG Society, Gurjinder Vihar,  Block D, Block E,

Services' Coverage - Pan India ( Across India  - Nationally ) :

Faridabad, Ghaziabad, Delhi ( Kalkaji, Okhla ( Phase - 1, 2, 3 ), Nehru Place, Hauz Khas, South Delhi ( East of Kailash, Kailash Hills, Lajpat Nagar, Okhla Phase 1, Okhla Phase 2, Okhla Phase 3, Srinivaspuri, Kalkaji, Nehru Place, Sant Nagar ), North Delhi, West Delhi, East Delhi ), Gurgaon, Pune, Mumbai, Bangalore, Hyderabad, Andhra Pradesh, Secunderabad, Ahmedabad, Alwar ( Rajasthan ), Jaipur ( Rajasthan ), Bhiwadi ( Rajasthan ), Gurugram, New Delhi,

 

Greater Noida West, Faridabad, Ghaziabad, Gurgaon, Gurugram, Delhi, Noida, Telangana, Bhopal, Gandhinagar, Lucknow, Shimla, Mangalore, Chennai, Noida ( viz., ATS Pristine, Sector 150, Sector 144, Sector 148, Sector 125, Sector 76 Metro Station, Aditya Urban Casa Towers A B C D E F G H I J K L, Sector 78, Mahagun Moderne, Sports City, Hyde Park, Amrapali Princely Estate, Mahagun Mirabella, Civitech Stadia, Sectors 76, 77, 79, 80, 70, 71, 72, 73, 74, 75 ), and, other nearby locations.

Several more local areas within the scope of Greater Noida, Greater Noida West and Noida Extension are covered under our tutoring services. If your residential areas near Greater Noida, Noida Extension, Noida, &, Greater Noida West, don't show up in the above mentioned lists of prominent areas, please do connect with us directly. We are available round the clock over various communication channels viz., WhatsApp, E - Mails, Calls and even text Messages. One can leave us messages through our Contact Us web page based visitors' forms 

 

We will certainly strive to clarify and respond to all your queries around learning delivery and support assistance. We will definitely connect at the earliest to understand, as well as match your requirements against our services. We assure you of confidentiality, consistency and integrity during all our engagements. We believe in building robust long term relationships with an eye towards social welfare and social upliftment through our small, as well as reasonable contributions.

IP Informatics Practices - Historical Leanings :

Historically, in 1962, Philippe Dreyfus introduced the French word informatique. Walter F. Bauer ( 1924 – 2015 ) and companions, who co – founded the software company Informatics Inc., independently proposed it in the same month. Though the new discipline's name immediately became well - known throughout Europe, the United States did not adopt it. Information processing, algorithms, computation, information, algorithmic processes, computational processes, and computational systems are among the ideas that most definitions of informatics claim to be at the core of. 

When computers were first used in healthcare in the 1950s, the term informatics was first used in the United States. Early professionals who were curious about the topic quickly discovered that there were no formal education programmes, and none developed until the late 1960s. Only archive science, a minor subset of informatics, was mentioned when the word was first introduced. As a result, professional development was crucial to the advancement of health informatics.

One of the prominent researchers, viz., Imhoff et al. ( 2001 ) defined healthcare informatics as the generation, handling, communication, storage, retrieval, management, analysis, discovery, and synthesis of data information and knowledge in the broad context of healthcare. Healthcare informatics is not just the application of computer technology to problems in the healthcare industry. 

 

Additionally, they claimed that the main objective of health informatics can be separated into the following: to offer solutions for issues in processing data, information, and knowledge. to research general data processing theories and knowledge in the medical and healthcare fields.

The word "health informatics" swiftly took on several different names throughout the United States, including "nursing informatics," "public health informatics," and "medical informatics." Later on, words like "business informatics," "forest informatics," "legal informatics," and others were coined to describe the use of computers in diverse sectors. These disciplines still mostly refer to informatics in terms of library science.

The term "informatics" usually refers to information science when used in connection with library science, for as in the subjects of irrigation informatics or geoinformatics. In 1966, A.I. Mikhailov, R.S. Gilyarevskii, and A.I. Chernyi used the term "informatics" for the first time in Russia, defining it as a scientific field that examines the composition and characteristics of scientific information.

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The International Neuro - informatics Coordinating Facility also employed this phrase in this context. However, this phrase is sometimes used by scientists to refer to the study of information processing rather than data management. When used to refer to "the cognitive, information processing, and communication tasks of medical practise, education, and research, including information science and the technology to support these tasks," the term "informatics" first gained popularity in the English - speaking world.

 

These substances are widely used today and can be classified as several branches of "applied informatics". K.K. Kolin presented a definition of informatics as a fundamental science that investigates information processes in nature, society, and technical systems around the beginning of the 1990s. By the University of Edinburgh in 1994, informatics was given a broad definition, defining it as "the study of the structure, algorithms, behaviour, and interactions of natural and artificial computational systems."

 

This prompted the creation of a single School of Informatics in 2002 after the institutes of computer science, artificial intelligence, and cognitive science were combined. Scottish Informatics and Computer Science Alliance now has the support of more than a dozen nearby universities. This term has also been accepted by a few non - European institutions of higher learning, like Kyoto University School of Informatics. Cognitive informatics is a phrase that gained popularity in 2003 thanks to Yingxu Wang.

 

Informatics Practices ( IP ) Lesson Near Me, IP Online Informatics Practices Teacher Near Me :
Wise Turtle Academy has very good experience delivering "CBSE Class 11th Informatics Practices ( IP ) Home Tutors, IP online teacher near me, UPSIDC Site B, Surajpur, Greater Noida, Uttar Pradesh, informatics practices online IP lesson near me online" that relate to English medium School Boards, including, Central Board of Secondary Education - CBSE, Indian Certificate of Secondary Education - ICSE, National Institute of Open Schooling - NIOS, Indira Gandhi National Open University - IGNOU, Rajasthan Board of Secondary & Senior Secondary Education - RBSE, Uttar Pradesh Board of Secondary Education UP Board and other States' Boards.

 

Wise Turtle Academy even caters to unconventional modes of formal education that are well recognised by our legal constitution. They comprise of Private Patrachaar Candidates, Private Candidates, Intermediate Board Students, Matriculation Board Students, Engineering Polytechnic institutions, Engineering Diploma Boards, Technical and Non Technical Distance Education Bodies, Non - Engineering Polytechnics and Correspondence Course Students. We also offer quality oriented, contemporary, educational & learning support through tailor made Informatics Practices or IP Study Notes,  Informatics Practices or IP Solved Assignments & Informatics Practices or IP Home Work Help.

Informatics Practices or IP is still an evolving discipline. Originally, being an offshoot of the discipline of CS or Computer Sciences, it has been cast aside to cater to its own focal area. With the research within the computing space accelerating at a breakneck pace, voluminous coding and algorithmic constructs are being discovered. All these discovered research constructs have considerable industry applications that may well encompass, as well as strongly leverage upon the related data and information space. Latter has deeper connotations with the practices built around the application specific treatment of information.

 

The future for this practice is bright and at this very moment widening drastically. Latter is owing to radical technological upgrades, encompassing ICT or Information and Communication Technologies, CCT or Computer and Communication Technologies, MM or Multimedia studies, Mass Media Studies, TCA or Typography and Communication Applications, AI or Artificial Intelligence Space, ML or Machine Learning Models, LIS or Library Information Systems, IT or Information Technology Space, Web Apps or Web Application Tools, GeoSpatial Scanning Tools, ET or Electronics Technology Developments, ET or Electrical Technology Developments, and much more. 

 

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Information serves as the third essential component for modelling the world, after matter and energy. The primary topics of cognitive informatics are the natural intelligence of the brain and internal information processing systems. In Russia in 2006, the idea of information science as the basis for both natural and artificial systems was once more advanced. Decoding the Universe, a highly significant book, was released in 2007. 

 

According to a 2007 article by Peter Denning, a former president of the Association for Computing Machinery, Computer science is no longer referred to by its previous definition, which involved the study of computer related phenomena. The study of both organic and synthetic information processing is known as computing. A new Computer Science and Informatics unit of assessment ( UoA ) with the following scope is part of the UK Funding Councils' 2008 Research Assessment Exercise. 

 

By implementing, organising, and employing computer hardware, software, and other resources, the UoA covers the study of techniques for gathering, storing, processing, communicating, and reasoning about information, as well as the function of interactivity in natural and artificial systems. Rigid application of analysis, experimentation, and design are characteristics of the subjects. The Informatics Forum's construction was finished in 2008. The founding of the MIT Schwarzman College of Computing took place in 2018. The construction of it is anticipated to be finished in 2021.

Evolutionary informatics is a brand-new discipline that was inspired by the idea of intelligent design. Evolutionary informatics, according to Evolutionary Informatics Lab, focuses on the incorporation, transformation, and export of information by evolving systems. The important book "Introduction To Evolutionary Informatics" was released in 2017. The pseudoscientific hypothesis of intelligent design ( ID ), which claims to be "an evidence based scientific theory about life's origins," is used to support the existence of God.

 

Certain characteristics of the world and of living things, according to proponents, "are best explained by an intelligent cause, not an undirected process like natural selection." ID is a type of creationism that lacks empirical evidence, gives no testable or tenable theories, and is therefore not science. The Discovery Institute is a Christian, politically conservative think tank with American roots, and its main ID proponents are affiliated with it.

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Although it has previously been mentioned in theological debates of the argument from design, the word "intelligent design" first appeared in its current usage as a derogatory epithet for creationism in 1989's Of Pandas and People, a creationist textbook aimed for high school biology classrooms. Following the 1987 Supreme Court ruling in Edwards v. Aguillard, which prohibited the teaching of creation science in public schools on the basis of the Constitution, the term was changed into early draughts of the book.

 

This led to directly replacing references to creation science and creationism. The Discovery Institute backed the intelligent design movement ( IDM ) , which started pushing for its inclusion in biology textbooks in public schools in the middle of the 1990s. It eventually led to the Kitzmiller v. Dover Area School District trial in 2005, which determined that intelligent design was not science, that it "cannot uncouple itself from its creationist, and thus religious, antecedents".

 

Thus, the public school district's promotion of it in ways violated the Establishment Clause of the First Amendment to the Constitution of United  States. According to ID, many biological and informational characteristics of living things are too complicated to be the outcome of natural selection. ID makes two key arguments against evolutionary explanations, viz., irreducible complexity and specified complexity.

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Following is the general Central Board of Secondary Education - CBSE / NCERT prescribed outline of the syllabus for subject Informatics Practices ( IP ) - Class 11th ( Class XI - 11 ) :

Unit 1: Introduction to Computer System
Introduction to computers and computing: evolution of computing devices,

components of a computer system and their interconnections, Input / Output devices.
Computer Memory:

Units of memory,

Types of memory – primary and secondary,

data deletion, its recovery and related security concerns.
Software:

Purpose and Types – system and application software, generic and specific purpose software.


Unit 2: Introduction to Python
Basics of Python programming,

Python interpreter - interactive and script mode,

the structure of a program, indentation, identifiers,

keywords, constants, variables, types of operators,

precedence of operators, datatypes,

mutable and immutable data types,

statements, expressions, evaluation of expressions,

comments, input and output statements,

data type conversion, debugging,

control statements: if - else, for loop
Lists: list operations - creating, initializing,

traversing and manipulating lists, list methods and built-in
functions.: len(), list(), append(), extend(), insert(), count(),

find(), remove(), pop(), reverse(), sort(), sorted(), min(), max(), sum()
Dictionary: concept of key-value pair,

creating, initializing, traversing,

updating and deleting elements,
dictionary methods and built-in functions: len(), dict(), keys(),

values(), items(), get(), update(), clear(), del()


Unit 3: Data Handling using NumPy
Data and its purpose, importance of data,

structured and unstructured data, data processing cycle,

basic statistical methods for understanding data - mean,

median, mode, standard deviation and variance.
Introduction to NumPy library, NumPy arrays and their advantage,

NumPy attributes, creation of NumPy arrays;

from lists using np.array(), np.zeros(), np.ones(), np.arange(),

indexing, slicing, and iteration;
concatenating and splitting array;
Arithmetic operations on one dimensional and two dimensional arrays.
Calculating max, min, count, sum, mean,

median, mode, standard deviation,

variance on NumPy arrays.


Unit 4: Database concepts and the Structured Query Language
Database Concepts: Introduction to database concepts and its need,

Database Management System. 
Relational data model: concept of attribute, domain,

tuple, relation, candidate key, primary key, alternate key, foreign key.
Structured Query Language: Data Definition Language,

Data Query Language and Data Manipulation Language,

Introduction to MySQL: Creating a database, using database,

showing tables using MySQL,
Data Types : char, varchar, int, float, date
Data Definition Commands: CREATE, DROP, ALTER (Add and Remove primary key, attribute).
Data Query Commands: SELECT-FROM- WHERE, LIKE, BETWEEN, IN, ORDER BY, using arithmetic,
logical, relational operators and NULL values in queries, Distinct clause
Data Manipulation Commands: INSERT, UPDATE, DELETE.


Unit 5: Introduction to the Emerging Trends
Artificial Intelligence AI, Machine Learning ML,

Natural Language Processing, Immersive experience ( AR, VR ),
Robotics, Big data and its characteristics,

Internet of Things ( IoT ), Sensors, Smart cities,

Cloud Computing and Cloud Services ( SaaS, IaaS, PaaS );

Grid Computing, Block chain technology. 

Practicals :

Programming in Python
1.To find average and grade for given marks.
2.To find the sale price of an item with a given cost and discount (%).
3.To calculate perimeter/circumference and area of shapes such as triangle, rectangle, square and circle.
4.To calculate Simple and Compound interest.
5.To calculate profit-loss for a given Cost and Sell Price.
6.To calculate EMI for Amount, Period and Interest.
7.To calculate tax - GST / Income Tax.
8.To find the largest and smallest numbers in a list.
9.To find the third largest/smallest number in a list.
10. To find the sum of squares of the first 100 natural numbers.
11. To print the first ‘n’ multiples of a given number.
12. To count the number of vowels in a user entered string.
13. To print the words starting with a particular alphabet in a user entered string.
14. To print the number of occurrences of a given alphabet in a given string.
15. Create a dictionary to store names of states and their capitals.
16. Create a dictionary of students to store names and marks obtained in 5 subjects.
17. To print the highest and lowest values in the dictionary.


Numpy Program
18. To create an array of 1D containing numeric values 0 to 9.
19. To create a NumPy array with all values as 0.
20. To extract values at odd numbered positions from a NumPy array.
21. To create a 1-D array having 12 elements usinf arange(). Now, convert this array into a 2-D array with size 4X3.
22. To perform basic arithmetic operations on 1D and 2D array .
6.3 Data Management: SQL Commands
23. To create a database
24. To create a student table with the student id, class, section, gender, name, dob, and marks as
attributes where the student id is the primary key.
25. To insert the details of at least 10 students in the above table.
26. To delete the details of a particular student in the above table.
27. To increase marks by 5% for those students who have Rno more than 20.
28. To display the entire content of the table.
29. To display Rno, Name and Marks of those students who are scoring marks more than 50.
30. To find the average of marks from the student table.
31. To find the number of students, who are from section ‘A’.
32. To add a new column email in the above table with appropriate data type.
33. To add the email ids of each student in the previously created email column.
34. To display the information of all the students, whose name starts with ‘AN’ (Examples: ANAND, ANGAD,..)
35. To display Rno, Name, DOB of those students who are born between ‘2005- 01-01’ and ‘2005-12-31’.
36. To display Rno, Name, DOB, Marks, Email of those male students in ascending order of their names.
37. To display Rno, Gender, Name, DOB, Marks, Email in descending order of their marks.
38. To display the unique section available in the table.
 

Reference :
NCERT Informatics Practices - Text book for class - XI

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Numerous cases that are said to be outside the scope of evolutionary explanation have been refuted by thorough scientific investigation. Although ID proponents acknowledge that they have not yet developed a scientific theory, they aim to question the methodological naturalism present in contemporary research. An analogy between natural systems and human creations is put out by ID as a strong argument against evolution, evoking the religious argument from design in support of the existence of God.

The complex traits, as defined by ID, are subsequently inferred by analogy by ID proponents to be proof of design. The idea that evidence against evolution qualifies as evidence for design creates a false dichotomy in the minds of ID opponents. In America, there was no significant religious debate on evolution in 1910, but in the 1920s, the fundamentalist modernist theological debate gave rise to fundamentalist Christian hostility to the teaching of evolution and the beginnings of modern creationism.

As a result, until the 1960s, evolution instruction in American public schools was essentially discontinued. When evolution was subsequently reinstated in the curriculum, there were a number of court challenges in which attempts were made to have creationism taught alongside evolution in science classes. Creation science was pushed by Young Earth Creationists or YECs as "an alternative scientific explanation of the world in which we live."

This usually cited the justification for complexity in nature as the argument from design, purportedly proving the existence of God. The teleological argument, commonly referred to as the "argument from intelligent design," or the "argument from design," has long been advanced by theologians. The argument from design can be summarised succinctly as follows. "Wherever complex design exists, there must have been a designer; nature is complex so nature must have had an intelligent designer."

This statement illustrates the argument from design's unscientific, circular, and illogical reasoning. It was outlined as a syllogism in Thomas Aquinas' sixth demonstration of God's existence. Examples of sophisticated design in organisms were provided in William Paley's Natural Theology in 1802. His interpretation of the watchmaker analogy said that just as it is purportedly obvious that a watch has been made by a craftsman, so too is it that the intricacy and adaptation found in nature must have been designed.

 

The perfection and variety of these patterns, he continued, apparently demonstrate the omnipotence of the creator, who can only be the Christian deity. While Paley's natural theology was open to deistic design through God given laws, intelligent design seeks scientific confirmation of repeated, allegedly miraculous interventions in the history of life. Both "creation science" and intelligent design are based on Paley's religious argument from design.

 

"Creation science" even included the bacterial flagellum as a prelude to the irreducible complexity claims of intelligent design. In the US, attempts to teach "creation science" in schools resulted in court decisions declaring that it is religious in origin and cannot be taught in scientific classes in public schools. Although it uses similar justifications to "creation science" and is similarly presented as science, intelligent design stays away from direct allusions to the Bible when discussing issues like the biblical flood tale or estimating the age of the Earth. 

 

According to Barbara Forrest, the creationist chemist Charles B. Thaxton and two other authors' book The Mystery of Life's Origin: Reassessing Current Theories, which was released by Jon A. Buell's Foundation for Thought and Ethics, marked the beginning of the intelligent design movement. In a review of this book that was published in March 1986, Stephen C. Meyer discussed how information theory would imply that the messages sent by DNA in cells exhibit "specified complexity" and were therefore likely the work of an intelligent actor.

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Furthermore, he claimed that naturalism's "foundational assumptions" underlie science and are just as much a matter of faith as "creation theory" itself. In November of that same year, Thaxton characterised his justification as a more complex version of Paley's argument from design. Thaxton claimed that his intelligent cause theory was agreeable with both philosophical naturalism and supernaturalism during a 1988 symposium he organised called "Sources of Information Content in DNA." 

 

Although proponents of the concept have claimed that the Christian God is the intelligent creator, intelligent design avoids naming or identifying the intelligent designer, just stating that one or more must exist. The issue of whether this lack of specificity regarding the identity of the designer in public discussions is a true characteristic of the concept or simply a stance taken to avoid alienating those who would separate religion from the teaching of science has been a subject of intense debate between supporters and detractors of intelligent design.

 

According to the Kitzmiller v. Dover Area School District court decision, the latter is true. Discussion of the theological "teleological argument" or "argument from design" and its concept of "intelligent design" has frequently referenced the theistic Creator God since the Middle Ages. While denying that ID is natural theology and downplaying its religious roots, ID proponents nevertheless position ID as bolstering the case for the presence of God despite having chosen this controversial name for their suggested alternative to evolutionary explanations. 

 

While proponents of intelligent design have cited earlier instances of the term that they claim were not creationist and religiously driven, they have been unable to demonstrate that these usages had any influence on individuals who first used the term in the intelligent design movement. Young Earth Creationist publications used variations of the phrase. For example, Percival Davis and his co - author Percival Davis alluded to the "design according to which basic organisms were created" in their 1967 book.

 

The Creation of Life: A Cybernetic Approach to Evolution was written by A. E. Wilder - Smith in 1970. The book supported Paley's design argument with computer calculations of the genetic sequences' improbability, which he claimed could not be explained by evolution but required "the abhorred necessity of divine intelligent activity behind nature" and that "the same problem would be expected to beset the relationship between the designer behind nature and the intelligently designed part of nature known as man."

 

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Dean H. Kenyon defended creation science by claiming that "biomolecular systems require intelligent design and engineering know-how," referencing Wilder-Smith, in a 1984 paper as well as in his affidavit to Edwards v. Aguillard. In Origins: Two Models: Evolution, Creation ( 1976 ) and Origins: Creation or Evolution ( 1988 ), creationist Richard B. Bliss used the term "creative design" and claimed that "while evolutionists are trying to find non-intelligent ways for life to occur, the creationist insists that an intelligent design must have been there in the first place."

 

Of Pandas and People, a book co - written by Davis and Kenyon, contained the first systematic usage of the word, which was defined in a glossary and claimed to be different from creationism. After the United States Supreme Court ruled in the case of Edwards v. Aguillard in June 1987 that it is unconstitutional for a state to require the teaching of creationism in public school science curricula, the term "intelligent design" became more widely used to refer to a field of study. 

 

According to a Discovery Institute article, Pandas editor Charles B. Thaxton learned the phrase from a NASA scientist and thought, "That's just what I need, it's a good engineering term." Over a hundred instances of the word "creation," including "creationism" and "Creation Science," were changed in two successive draughts of the book in 1987 to "intelligent design," while "creationists" were changed to "design proponents" or, in one instance, "cdesign proponentsists."  

 

In Tacoma, Washington, Thaxton organised a meeting with the theme "Sources of Information Content in DNA" in June 1988. At the conference, Stephen C. Meyer was there, and he subsequently said, "The term intelligent design came up". Thaxton made the decision to call his new creationist movement "intelligent design" in December 1988. Intelligent Design is a debatable theory with a lot of criticisms that go on related to it's implementation and applications.

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Following is the general Central Board of Secondary Education - CBSE / NCERT prescribed outline of the syllabus for subject Informatics Practices ( IP ) - Class 12th ( Class XII - 12 ) :

Unit 1: Data Handling using Pandas and Data Visualization
Data Handling using Pandas -I
Introduction to Python libraries- Pandas, Matplotlib.
Data structures in Pandas - Series and data frames.
Series: Creation of series from ndarray, dictionary,

scalar value; mathematical operations; series attributes,
head and tail functions; selection, indexing and slicing.
Data Frames: creation of data frames from dictionary of series,

list of dictionaries, text / CSV files, display,
iteration. Operations on rows and columns: add add ( insert / append) ,

select, delete ( drop column and row ),
rename, Head and Tail functions, indexing using labels,

Boolean indexing; joining, merging and concatenation of data frames.
Importing / Exporting Data between CSV files and Data Frames. ( for practical only )


Data handling using Pandas – II
Descriptive Statistics : max, min, count, sum, mean, median,

mode, quartile, Standard deviation, variance.
Data Frame operations: Aggregation, group by, Sorting,

Deleting and Renaming Index, Pivoting.
Handling missing values – dropping and filling.
Importing / Exporting Data between MySQL database and Pandas.
Data Visualization : Purpose of plotting,

drawing and saving of plots using Matplotlib (line plot, bar graph,
histogram, pie chart, frequency polygon, box plot and scatter plot).
Customizing plots: color, style (dashed, dotted), width;

adding label, title, and legend in plots.


Unit 2: Database Query using SQL
Math functions: POWER (), ROUND (), MOD ().
Text functions: UCASE () / UPPER (), LCASE () / LOWER (),

MID () / SUBSTRING () / SUBSTR (), LENGTH (),
LEFT (), RIGHT (), INSTR (), LTRIM (), RTRIM (), TRIM ().
Date Functions: NOW (), DATE (), MONTH (), MONTHNAME (), YEAR (), DAY (), DAYNAME ().
Aggregate Functions: MAX (), MIN (), AVG (), SUM (), COUNT (); using COUNT (*).
Querying and manipulating data using Group by, Having, Order by.
Operations on Relations - Union, Intersection, Minus, Cartesian Product,

JOIN ( Cartesian Join, Equi Join, Natural Join )

Unit 3: Introduction to Computer Networks
Introduction to networks, Types of network: LAN, MAN, WAN.
Network Devices: modem, hub, switch, repeater, router, gateway
Network Topologies: Star, Bus, Tree, Mesh.
Introduction to Internet, URL, WWW, and, its applications - Web, email, Chat, VoIP.
Website: Introduction, difference between a website and webpage,

static vs dynamic web page, web server and hosting of a website.
Web Browsers: Introduction, commonly used browsers,

browser settings, add - ons and plug - ins, cookies.


Unit 4: Societal Impacts
Digital footprint, net, and communication etiquettes,

data protection, intellectual property rights ( IPR ),
plagiarism, licensing and copyright, free and open source software ( FOSS ),

cybercrime and cyber laws, hacking, phishing, cyber bullying, overview of Indian IT Act.
E - waste: hazards and management.
Awareness about health concerns related to the usage of technology. 

Practical :
Data Handling

1. Create a pandas series from a dictionary of values and an ndarray
2. Given a Series, print all the elements that are above the 75th percentile.
3. Create a Data Frame quarterly sales where each row contains the item category, item name, and
expenditure. Group the rows by the category, and print the total expenditure per category.
4. Create a data frame based on ecommerce data and generate descriptive statistics ( mean, median, mode, quartile, and variance )
5. Create a data frame for examination result and display row labels, column labels data types of each column and the dimensions
6. Filter out rows based on different criteria such as duplicate rows..
7. Find the sum of each column, or find the column with the lowest mean.
8. Locate the 3 largest values in a data frame.
9. Subtract the mean of a row from each element of the row in a Data Frame.
10. Replace all negative values in a data frame with a 0.
11. Replace all missing values in a data frame with a 999.
12. Importing and exporting data between pandas and CSV file
13. Importing and exporting data between pandas and MySQL database


Visualization 
14. Given the school result data, analyse the performance of the students on different parameters, e.g subject wise or class wise.
15. For the Data frames created above, analyze and plot appropriate charts with title and legend.
16. Take data of your interest from an open source ( e.g. data.gov.in ), aggregate and summarize it. Then
plot it using different plotting functions of the Matplotlib library.

 

Data Management

17. Create a student table with the student id, name, and marks as attributes where the student id is the
primary key.
18. Insert the details of a new student in the above table.
19. Delete the details of a particular student in the above table.
20. Use the select command to get the details of the students with marks more than 80.
21. Create a new table ( order ID, customer Name, and order Date ) by joining two tables ( order ID,
customer ID, and order Date ) and ( customer ID, customer Name, contact Name, country ).
22. Create a foreign key in one of the two tables mentioned above
23. Find the min, max, sum, and average of the marks in a student marks table.
24. Find the total number of customers from each country in the table ( customer ID, customer Name,
country ) using group by.
25. Create a new table ( name, date of birth ) by joining two tables ( student id, name ) and ( student id,
date of birth ).
26. Write a SQL query to order the ( student ID, marks ) table in descending order of the marks.
7.4 Introduction to Computer Networks
27. Download, install and configure browser.


Reference :
NCERT Informatics Practices - Text book for class - XII
 

The 1989 book Of Pandas and People was the first to systematically use the terms "intelligent design," "design proponents," and "design theory," defining the term intelligent design in a glossary, and representing it as distinct from creationism. It also included all of the current arguments for ID. As a result, it marks the beginning of the current intelligent design trend. The phrase "intelligent design" was the most well-known among the roughly fifteen new concepts it added to the creationist lexicon in order to debate evolution without resorting to religious rhetoric. 

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According to both its publisher Jon A. Buell and William A. Dembski in his expert witness report for Kitzmiller v. Dover Area School District, it was the first place the term "intelligent design" appeared in its primary contemporary use. The book has drawn criticism from the National Centre for Science Education ( NCSE ) for offering all of the fundamental justifications put forth by supporters of intelligent design and for being actively marketed for use in public classrooms before any studies had been conducted to back up these justifications.

Michael Ruse, a philosopher of science, believes that the information is "worthless and dishonest" despite the fact that it is presented as a textbook. It was regarded as a political tool by an attorney for the American Civil Liberties Union that was intended for children who did not "know science or understand the controversy over evolution and creationism." Kevin Padian, one of the scientists who created the scientific curriculum used in California classrooms, criticised it for having "sub - text", "intolerance for honest science" and "incompetence." 

 

Although he had previously discussed the idea in his contributions to the 1993 updated version of Of Pandas and People, biochemist Michael Behe coined the phrase "irreducible complexity" in his 1996 book Darwin's Black Box. It is described by Behe as "a single system that is composed of several well-matched, interacting parts that contribute to the basic function, wherein the removal of any one of the parts effectively results in the system ceasing to function." 

 

Behe uses the example of a mousetrap to explain this idea. The base, the catch, the spring, and the hammer are the key components that interact to make a mousetrap functional. All of these components must be present for the mousetrap to operate. The mousetrap loses its effectiveness if even one piece is removed. Natural selection cannot produce irreducibly complex systems, according to proponents of intelligent design, because the selective function is only present when all the components are put together.

Behe asserted that the E. coli bacterial flagellum, the blood clotting cascade, cilia, and the adaptive immune system are examples of biological mechanisms that are irreducibly complicated. The irreducible complexity argument has its detractors since it presupposes that a system's necessary components have always been present and could not have been successively added. They contend that as other factors change, something that is first only advantageous may eventually turn out to be necessary.

 

Furthermore, they contend that rather of introducing new components, evolution frequently advances by changing existing ones or eliminating them from a system. This is also referred to as the "scaffolding objection" because scaffolding can support a "irreducibly complex" edifice until it is finished and stable enough to stand on its own. Behe has admitted to employing "sloppy prose" and that his "argument against Darwinism does not add up to a logical proof".

 

The judge in the Dover trial stated that "Professor Behe's claim of irreducible complexity has been refuted in peer-reviewed research papers and has been rejected by the scientific community at large." Irreducible complexity has remained a common justification for intelligent design. When asserting that the messages carried by DNA in the cell were specified by intelligence and had to have come from an intelligent agency, physical chemist and creationist Charles B. Thaxton coined the phrase "specified complexity" from information theory in 1986.

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Mathematician, philosopher, and theologian William A. Dembski created the "specified complexity" idea of intelligent design in the 1990s. According to Dembski, when an object demonstrates specified complexity (i.e., is both complicated and "specified" at the same time), one can assume that it was intentionally created rather than the outcome of random natural processes. He offers the subsequent examples: "Only one alphabetic letter is defined, and it is not complicated. 

Without being stated, a long statement made up of random letters is difficult. Shakespeare's sonnets are intricate and precise. He claims that the  characteristics of living things, particularly the "patterns" of chemical sequences in useful biological compounds like DNA, may be described in a similar manner. The idea of specified complexity was put forth by William A. Dembski. 

 

Dembski describes complex specified information or CSI as anything that has a probability of appearing by natural chance of less than 1 in 10150. Complex specified information cannot occur naturally because Dembski has defined it in this way, hence the true question is whether CSI genuinely exists in nature, according to critics who claim that this turns the argument into a tautology. 

 

Dembski's specified complexity / CSI argument has lost favour with the scientific and mathematical community for its conceptual soundness. As Dembski claims, it has not yet been demonstrated that specified complexity has several applications in other domains. Dembski's "explanatory filter" is described by John Wilkins and Wesley R. Elsberry as eliminative since it excludes explanations in a particular order, first regularity, then chance, and lastly reverting to design.

 

They contend that this process is unreliable as a model for scientific reasoning because it is prone to drawing erroneous conclusions due to the asymmetrical manner it handles the various explanations. In The God Delusion ( 2006 ), Richard Dawkins, another opponent of intelligent design, claims that allowing for an intelligent creator to account for improbable complexity just delays the issue because such a designer would have to be at least as complicated.

The use of selective evolution to design specific electronic, aeronautical, and automotive systems that are thought to be problems too complex for human "intelligent designers" is evidence that other scientists believe evolution through selection is a better explanation for the observed complexity. Outside of biology, broader teleological arguments have also been periodically used by proponents of intelligent design, most notably one that focuses on the fine - tuning of universal constants. 

 

Latter are thought to be responsible for the existence of matter and life rather than only being the result of chance. These include the values of the fundamental physical constants, the ratios of the masses of the fundamental particles, and the strength of the nuclear, electromagnetic, and gravitational forces in relation to one another.

 

Guillermo Gonzalez, an advocate of intelligent design and fellow at the Centre for Science and Culture, asserts that if even one of these parameters were slightly off, the universe would have undergone a profound change, rendering the formation of many chemical elements and other features of the universe, such as galaxies, impossible. Therefore, according to supporters, an intelligent designer of life was required to make sure the necessary components were available in order to achieve that specific result.

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Most scientists have commented that the evidence currently available does not provide strong support for these claims. Both intelligent design and the weak form of the anthropic principle, according to Victor J. Stenger and other detractors, are fundamentally a tautology. In his opinion, these arguments amount to the assertion that life is possible because the universe is capable of supporting life. The assertion that a universe capable of supporting life has been criticised for lacking imagination in supposing the impossibility of any other types of existence. 

If circumstances were different, life as we currently know it might not exist, but another type of life might take its place. Many of the cited variables, according to some critics, appear to be connected to one another, and calculations by physicists and mathematicians indicate that the birth of a universe like ours is highly likely. Modern proponents of intelligent design formulate their arguments in secular terms and purposefully exclude naming the intelligent agency or agents they propose.

 

Even if they do not explicitly claim God is the designer, it is frequently assumed that God must have intervened in some way because only a god could have done so. Aliens might meet these characteristics, according to Dembski's theory in The Design Inference from 1998. The author of Of Pandas and People suggests using SETI as an example of how science might benefit from intelligent design.

 

The Ralian UFO religion was given as a concrete example of an extraterrestrial intelligent designer perspective that "makes many of the same bad arguments against evolutionary theory as creationists" by scientific philosopher Robert T. Pennock in 2000. The universe, however, clearly exhibits characteristics of having been designed, according to the official account of intelligent design. Dembski acknowledges the dilemma and comes to the conclusion that "no intelligent agent who is strictly physical could have presided over the origin of the universe or the origin of life."

The main proponents have said in remarks to their supporters that they think the creator is exclusively the Christian God and not any other religion. Beyond the question of whether intelligent design falls under the purview of science, some detractors contend that the design hypothesis, regardless of its standing in the scientific community, appears to be improbable given the evidence at hand.

 

Jerry Coyne, for instance, questions why a designer "would give us a pathway for making vitamin C, but then destroy it by disabling one of its enzymes", similar to a pseudogene, and why a designer "would not stock oceanic islands with reptiles, mammals, amphibians, and freshwater fish, despite such islands' suitability for such species." Coyne also cites "the flora and fauna on those islands resemble that of the nearest mainland, even when the environments are very different" as more proof that the species there did not naturally evolve there.

 

Behe had previously claimed in Darwin's Black Box that since we are unable to comprehend the designer's intentions, such questions cannot be conclusively answered. For instance, strange designs "have been placed there by the designer for a reason, for artistic reasons, for variety, to show off, for some as yetvundetected practical purpose, or for some unguessable reason, or they might not".

 

In response, Coyne states that, "Either life resulted not from intelligent design, but from evolution; or the intelligent designer is a cosmic prankster who designed everything to make it look as though it had evolved". Paul Nelson and other proponents of intelligent design argue that we simply don't grasp how excellent the design is, which sidesteps the issue of nature's poor design. Behe credits Paley as an influence, although he disagrees with Paley's view of a flawless creation.

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He suggests that designers don't always create the greatest designs they are capable of. Behe contends that the designer may have a variety of reasons for not prioritising engineering quality, similar to a parent who does not want to lavish their child with expensive toys. Another issue with the argument from imperfection, according to him, is that it heavily relies on a psychoanalysis of the unknown designer. However, unless the designer expressly discloses what those reasons are, it is practically impossible to determine why they would or would not do something.

 

Intelligent design cannot be experimentally tested due to its reliance on the unexplainable intentions of the designer. Former UC Berkeley law professor, author, and proponent of intelligent design Phillip E. Johnson advances the fundamental definition that the designer creates for a purpose, citing his belief that AIDS was created to punish immorality and is not caused by HIV. However, such motivations cannot be verified by scientific means. 

 

Insisting on the necessity of a complexity designer also begs the question, "What designed the designer? " According to proponents of intelligent design, the issue is unrelated to or outside the purview of intelligent design. Contrary to this, Richard Wein claims that "scientific explanations frequently raise fresh unresolved concerns. These queries aren't unimportant, nevertheless, when determining the worth of an explanation, they must be weighed against the enhancements to our comprehension that the explanation offers. 

 

It is merely asking more questions to invoke an ill - defined creature to explain the origin of other beings, including ourselves. As troublesome as the question the explanation is meant to address is the new question it raises. According to Richard Dawkins, the claim that the creator does not require an explanation is a thoughtless cliche. The question "What designed the designer?" leads to an infinite regression, from which supporters of intelligent design can only escape by turning to religious creationism or logical inconsistency in the lack of observable, quantifiable proof. 

 

The creationism of the 1980s has directly led to the intelligent design movement. The scientific and academic institutions, as well as a federal court in the United States, consider intelligent design to be either a subset of creationism or its direct offspring, closely related to traditional creationism. It is known as "intelligent design creationism" by several authors. 

 

The Centre for Science and Culture, which was founded in 1996 as the creationist branch of the Discovery Institute to advance a theological agenda asking for significant social, cultural, and political changes, serves as the movement's focal point. Although efforts have been made to promote intelligent design in other nations, the Discovery Institute's intelligent design campaigns have been conducted exclusively in the United States. 

 

Movement founders claim that naturalism and scientific orthodoxy's weaknesses are shown by intelligent design. Supporters of intelligent design argue that science shouldn't be restricted to naturalism and shouldn't necessitate the acceptance of a naturalistic philosophy that flatly rejects any explanation that incorporates a supernatural cause. The movement's overarching objective is to "reverse the stifling dominance of the materialist worldview" embodied by the theory of evolution in favour of "a science consonant with Christian and theistic convictions."

 

According to Phillip E. Johnson, intelligent design seeks to legitimise creationism as a valid scientific theory. Fellows or employees of the Discovery Institute and its Centre for Science and Culture include all prominent proponents of intelligent design. The Discovery Institute, which steers the movement and implements its wedge strategy while running its "teach the controversy" campaign and other related programmes, is largely responsible for the notions of intelligent design and the movement that surrounds them. 

 

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Leading proponents of intelligent design have said contradictory things about it. They claim that intelligent design is not religious when speaking to the general public, but when speaking to conservative Christian supporters, they claim that the Bible is the basis for intelligent design. The Institute declares its commitment to evangelism and Christianity while acknowledging the need for support. Along with concentrating on key opinion leaders, we also want to develop a broad base of support among Christians, who make up the majority of our electorate.

 

The main way we'll accomplish this is through apologetics seminars. We want to "popularise" our views in the larger culture while also energising and arming believers with fresh scientific proofs that uphold their faith. As a matter of policy, the Discovery Institute obfuscates its objective, according to Barbara Forrest, a specialist who has written extensively about the movement. The activities of the movement, according to her writing, "betray an aggressive, systematic agenda for promoting not only intelligent design creationism, but the religious worldview that underpins it." 

 

The majority of leading proponents of intelligent design are publicly devout Christians who have stated that they believe the designer suggested by intelligent design is the Christian conception of God, despite the fact that the movement's arguments for intelligent design are couched in secular terms and purposefully avoid positing the identity of the designer. Evangelical Protestants Stuart Burgess, Phillip E. Johnson, William A. Dembski, and Stephen C. Meyer, Catholic Michael Behe, Young Earth Creationist Paul Nelson, and Unificationist Jonathan Wells make up the group.

 

Those who oppose Christianity include the Jewish David Klinghoffer, the Agnostics Michael Denton and David Berlinski, and the Muslim Pakistani-Canadian Muzaffar Iqbal. According to Phillip E. Johnson, the first step to ultimately reinstating the Christian idea of God as the designer is to create ambiguity by using secular terminology in arguments that are carefully constructed to avoid overtones of theistic creationism.

 

Johnson makes it clear that supporters of intelligent design should hide their motivations for believing in religion in order to prevent intelligent design from being seen "as just another way of packaging the Christian evangelical message."

 

Johnson emphasises that "removing the Bible from the conversation is the first thing that needs to be done." The idea is that after we have distinguished between scientific reality and materialist prejudice, it will be appropriate to examine the biblical questions. This is not to imply that they are irrelevant. In The Design Inference, William A. Dembski outlines the method of covering up the religious motivations behind intelligent design. Dembski suggests that the designer might have been a god or a "alien life force" in this work.

 

Yet, he writes otherwise in his 1999 book Intelligent Design: The Bridge Between Science and Theology. Even if its proponents are completely ignorant of Christ, science cannot function without him. Undoubtedly, one can follow the practical implications of a scientific hypothesis without turning to Christ. However, the conceptual foundation of the philosophy can only be found in Christ in the end. ID is a component of God's general revelation, according to Dembski. 

 

In my personal experience, I've discovered that intelligent design opens the way for individuals to come to Christ in addition to freeing us from this ideology or materialism, which stifles the human spirit. The Bible's Gospel of John is cited as the basis for intelligent design by Johnson and Dembski. Such assertions, according to Barbara Forrest, show that prominent proponents of intelligent design do not only view the theory as a scientific one with implications that correspond with their personal religious beliefs, but rather as a theory that is fundamentally religious in origin.

 

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According to her, the Christian Reconstructionist movement and the prominent proponents of intelligent design have a tight relationship. She outlines the relationships that current and former Discovery Institute Fellows Phillip E. Johnson, Charles B. Thaxton, Michael Behe, Richard Weikart, Jonathan Wells, and Francis J. Beckwith have with important Christian Reconstructionist organisations, as well as the extent of funding that Howard Ahmanson, Jr., a prominent Reconstructionist, provided the Institute. 

 

On the community of scientists and other academics, such a model has little to no positive influence. The moment is ideal for a direct strategy and a single jump into the starting blocks of conflict. Such a leap would be to provide a creation model that is founded on the Bible and is verified by science. The two biggest YEC groups in the world have also made an effort to set themselves apart from the intelligent design movement.

 

ID "will not work, even if well-intentioned and persuasively argued," declared Henry M. Morris of the Institute for Creation Research ( ICR ), in 1999. It has been tried many times before with disastrous results, and it will fail again today. Because it is not the Biblical approach, it won't work. Morris asserts that for the evidence of intelligent design to be significant and long lasting, it must either be followed by or be presented in conjunction with a solid defence of true Biblical creationism.

 

In 2002, Carl Wieland, who was then with Answers in Genesis or AiG, criticised design proponents who, despite their good intentions, "'left the Bible out of it'" and unknowingly contributed to the modern rejection of the Bible. Wieland said that "AiG's major strategy' is to boldly, but humbly, call the church back to its Biblical foundations, so we neither count ourselves a part of this movement nor campaign against it." 

 

The scientific community is unanimous in its belief that intelligent design is not science and has no business being taught in the science classes. According to the U.S. National Academy of Sciences, "creationism, intelligent design, and other claims of supernatural intervention in the origin of life or of species are not science because they are not testable by the methods of science."

 

It is known as pseudoscience according to the American Association for the Advancement of Science and the National Science Teachers Association of the United States.  Others in the scientific community have criticised its strategies, charging the ID movement with fabricating false claims against evolution, with spreading falsehoods and disinformation about science, and with marginalising those who teach it. More recently, in September 2012, Bill Nye issued a warning that creationist ideologies pose a threat to American advances and science education. 

 

The Discovery Institute advertised "A Scientific Dissent From Darwinism" in 2001, claiming that the following scientists had signed a statement expressing doubt. We don't believe that natural selection and random mutation can fully explain the complexity of life. It is important to encourage careful analysis of the Darwinian theory's supporting data.

 

Although the vague statement did not rule out other known evolutionary mechanisms and the majority of the signatories were not experts in the area, the Institute claimed starting in 2004 that the rising number of signatures showed scientists' growing scepticism of evolution. 

 

The assertion that evolution lacks widespread scientific backing was a prominent part of Discovery Institute initiatives to promote intelligent design as having scientific validity, and Institute members continued to use the list at least until 2011. Scientists organised Project Steve, which attracted more signatures named Steve or variants than the Institute's petition, and a counter-petition, "A Scientific Support for Darwinism," which swiftly attracted comparable numbers of signatories, as part of an attempt to refute these assertions. 

 

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Prior to the Kitzmiller v. Dover School District ruling in December 2005, a number of polls were carried out to ascertain the amount of support for intelligent design among various populations. A Harris poll conducted in 2005 found that 10% of American respondents believed that humans were "so complex that they required a powerful force or intelligent being to help create them." More people support the idea, according to Zogby polls commissioned by the Discovery Institute, but these polls have significant flaws.

 

Latter include a low response rate ( 248 out of 16,000 ), being conducted for a group that has expressed interest in the poll's results, and having leading questions. When asked about their opinions on the origin and evolution of humans, 38% of adults in the United States responded in the affirmative, which was noted as being at the lowest level in 35 years, according to the 2017 Gallup creationism survey. 

 

Previously, a series of Gallup polls conducted in the United States from 1982 through 2014 on "Evolution, Creationism, and Intelligent Design" found support for the ideas of "God created human beings in roughly their present form at one time within the last 10,000 years or so" ranging from 40% to 47% and "human beings have developed over millions of years from less advanced forms of life, but God guided the process" between 31% and 40%. The surveys also recorded responses to a number of additional in - depth inquiries. 

 

There have been claims that supporters of ID have experienced prejudice, including being denied tenure or receiving harsh online criticism. 2008 release of the documentary Expelled: No Intelligence Allowed features five of these incidents, presented by host Ben Stein. In a "scientific conspiracy to keep God out of the nation's laboratories and classrooms," according to the movie, the mainstream scientific establishment suppresses academics who think there is proof of intelligent design in nature or challenge the evidence for evolution.

 

Alternative explanations for the alleged persecution were discovered after an investigation of these complaints. Although it does not provide a comprehensive definition of the term or make an attempt to explain it in a scientifically sound manner, the movie portrays intelligent design as being driven by science rather than religion. Expelled explores it as a political issue in addition to briefly addressing issues of intractable complexity.

 

The movie portrays evolution as a scientific idea that led to fascism, the Holocaust, communism, atheism, and eugenics. Expelled has been shown to legislators privately as part of the Discovery Institute's campaign for legislation promoting academic freedom.  Churches and Christian organisations could only attend review screenings, and during a special pre-release screening, one of the interview subjects, PZ Myers, was denied entry.

 

The Anti - Defamation League has condemned the film's claim that evolution theory contributed to the Holocaust, and the American Association for the Advancement of Science has called the movie dishonest and divisive propaganda intended to introduce religious concepts into science classrooms in public schools. The movie has interviews with academics and scientists who agreed to participate despite the topic and title of the movie being misrepresented.

 

Michael Shermer, a sceptic, calls it "surreal" to continuously get the same question devoid of context. In order to exclude God and the Bible from the debate, intelligent design proponents express their theory in the language of science as if it were a scientific hypothesis. An accepted definition of a scientific hypothesis is as follows. 

 

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Consistent, in its postulated entities or explanations be parsimonious, Useful ( describes and clarifies observed phenomena and has prediction potential ), empirically testable and falsifiable ( perhaps possible to be supported or refuted by research or observation ), based on numerous observations, frequently in the form of repeated, carefully monitored experiments, dynamic ( changed in light of observations that contradict it ) and correctable, progressive ( improves on earlier theories ), provisional or tentative ( available to experimental verification and makes no claims as to its accuracy ). 

 

Any theory, hypothesis, or speculation must satisfy the majority, and ideally all, of these requirements in order to be regarded as scientific. Lesser conditions must be completed for something to be considered scientific in any meaningful sense; the less criteria must be met, the less scientific it is. Common arguments against classifying intelligent design as science include its lack of consistency, violation of the parsimony principle, lack of scientific utility, inability to be falsified, impossibility to test experimentally, and incapacity to be dynamic, progressive, or provisional. 

 

Intelligent design proponents aim to alter this fundamental tenet of science by doing away with "methodological naturalism" and substituting it with what the movement's founder, Phillip E. Johnson, calls "theistic realism." Supporters of intelligent design contend that scientific explanations fall short in explaining some events and that supernatural explanations offer an easy - to - understand account of how the cosmos and life on Earth first came to be.

 

Many proponents of intelligent design consider "scientism" to be a religion in and of itself, one that encourages materialism and secularism in an effort to eradicate theism from public life. They see their work in advancing intelligent design as a means of restoring religion's traditional place at the centre of education and other public spheres. The God explanation is the least parsimonious, hence in accordance with Occam's razor, it cannot be a scientific explanation, it has been said.

 

Methodological naturalism, on the other hand, is not an assumption of science but rather the outcome of science well done. Intelligent design has been resisted as legitimate science due to a failure to adhere to the rules of scientific discourse and a failure to produce work that can bear criticism. The intelligent design movement has not released any supporting peer - reviewed research or data, nor has it published a properly peer-reviewed article in support of ID in a scientific journal. 

 

The publisher of the lone paper that supported intelligent design and was published in a peer-reviewed scientific journal swiftly withdrew it because it violated the rules of that journal's peer review. A number of intelligent design articles, according to the Discovery Institute, have been published in peer-reviewed journals. However, opponents of intelligent design, who are mainly scientists, dispute this assertion.

 

They claim that supporters of the theory have established their own journals with peer review that lacks objectivity and rigour and are composed only of intelligent design supporters. The fact that the term "intelligent design" assumes the existence of an observable intelligence, a concept without a generally accepted definition in science and raises more objections. Intelligent design proponents make the assumption that intelligence's traits can be observed without mentioning the standards by which intelligence should be measured.

 

The essential components that enable intelligent design as a legitimate science, according to detractors, are undermined by the radical differences between the design detection methods advocated by intelligent design proponents and conventional design detection. In addition to denying the distinction between natural and artificial design, which enables scientists to compare complex designed artefacts against the background of the types of complexity found in nature, intelligent design proponents argue that searching for a designer without knowing that designer's capabilities, parameters, or intentions would be futile. 

 

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The main concern of a sizeable percentage of the American populace is whether or not traditional evolutionary biology is compatible with faith in God and the Bible, as well as how this topic is taught in schools. In science classes taught in American public high schools, the Discovery Institute's "teach the controversy" campaign supports intelligent design while attacking evolution. The scientific community and organisations that support science education have retorted that there is no scientific debate about the veracity of evolution and that the debate only pertains to politics and religion.

Many of the arguments put forth by supporters of intelligent design, according to Eugenie C. Scott, Glenn Branch, and other detractors, stem from ignorance. In the argument from ignorance, it is falsely said that the absence of evidence in support of one viewpoint proves the validity of another. Since there is no natural explanation for some particular features of evolution, we presume intelligent cause, according to Scott and Branch, who claim that intelligent design is an argument from ignorance.

 

They assert that the majority of scientists would respond that the unexplained is not unexplainable and that saying "we don't know yet" is a better response than pointing to an explanation that is not supported by science. Michael Behe's demands, in particular, for ever more thorough explanations of the historical evolution of molecular systems, seem to presume a false dichotomy, where either evolution or design is the appropriate explanation and each apparent failure of evolution becomes a triumph for design.

 

The apparently unique ideas put forth by supporters of intelligent design, according to Scott and Branch, have not formed the foundation for any useful scientific investigation. Judge John E. Jones III stated in his summary of the Kitzmiller trial that "ID is fundamentally predicated upon a false dichotomy, namely that to the extent that evolutionary theory is disproven, ID is confirmed." 

 

The McLean v. Arkansas ( 1982 ) trial ruled that the same defence of creationism made use of "contrived dualism," the erroneous premise of a "two model approach," was invalid. Behe's irreducible complexity argument advances dispositive defences of creationism while offering no persuasive evidence in support of evolution. As has been the case with a number of other examples offered as alleged illustrations of irreducible complexity, it does not permit for scientific explanations to be discovered in the future. 

 

Many supporters of intelligent design maintain that a religious element is not necessary to support their assertions. However, the assertions of intelligent design inevitably pose a number of philosophical and theological questions. Advocates of intelligent design make an effort to show, by scientific evidence, that characteristics like irreducible complexity and specified complexity could not have developed through natural processes and instead required repeated direct miraculous interventions by a Designer, typically a Christian view of God.

 

In contrast to theistic evolution to which even Charles Darwin was open, they reject the idea that a Designer could have created the universe by simply initiating the natural laws. The distinctiveness of intelligent design is that it claims both designed laws and ongoing miracle interventions. This is in contrast to other major religious traditions that believe in a created universe and that God's interactions and impacts do not operate in a manner similar to natural causes.

 

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In the Roman Catholic tradition, the difference between primary, natural causes and ultimate philosophical explanations is made with great care. There may be other theological ramifications to the idea of direct miraculous intervention. Even while such a Designer is capable of intervening for other reasons outside alleviating pain, some believe this suggests the Designer is not omnibenevolent. For those who think the Creator's work was both perfect and finished, recurrent interventions suggest that the original plan was not flawless and complete. 

 

Those who believe in intelligent design contend that we have either failed to comprehend the perfection of the design, for instance, by claiming that vestigial organs serve unknown purposes or that designers may not always produce the best designs possible due to unknown motivations. In an article published in The Tablet in August 2005, Jesuit astronomer and director of the Vatican Observatory George Coyne outlined religious justifications for embracing evolution and asserted that "Intelligent design isn't science even though it purports to be.

 

Intelligent design ought to be covered while discussing religion or cultural history, not when it comes to science, if you want to teach it in schools. The God - of - the - gaps argument, which takes the following form, has also been used to criticise intelligent design. The body of scientific knowledge is incomplete. The void is filled by acts of God or an intelligent designer, which establishes their deity and verifies their existence. 

 

The theological equivalent of an argument from ignorance is a God - of - the - gaps argument. This form of argument is characterised in part by the fact that it just provides explanations for unanswered problems that are ultimately open to further inquiry and are frequently supernatural in nature. 

 

Scientists have noted that, despite being astounding and incorporating mathematical constructs far beyond any practical value, the astronomy of the earliest civilizations was misguided and of little significance to the advancement of science because they neglected to look more closely into the forces that propelled the celestial bodies across the sky. Though not yet as a properly defined experimental discipline, science was initially practised by the Greek civilisation.

 

It was an effort to rationalise the universe of natural experience without turning to divine intervention. Any reference to an intelligent creator is specifically disregarded in this historically driven definition of science due to the potential paralysing impact it could have on that advancement. The first direct lawsuit filed in federal court in the United States against a public school district that demanded the teaching of intelligent design as an alternative to evolution was Kitzmiller v. Dover Area School District.

 

The plaintiffs were successful in their claim that intelligent design is a variation on creationism and that the school board's policy was therefore in violation of the First Amendment's Establishment Clause. A statement that the school board mandated be read aloud in ninth-grade science classes when evolution was taught led eleven parents of kids in Dover, Pennsylvania, to sue the Dover Area School District. Americans United for Separation of Church and State ( AU ), Pepper Hamilton LLP, and the American Civil Liberties Union ( ACLU ) all represented the plaintiffs. 

 

Consultants for the plaintiffs were provided by the National Centre for Science Education. Thomas More Law Centre was in charge of defending the accused. From September 26 to November 4, 2005, Judge John E. Jones III presided over a bench trial for the lawsuit. The plaintiffs' expert witnesses included John F. Haught, Barbara Forrest, Kevin Padian, Brian Alters, Robert T. Pennock, and Kenneth R. Miller. The defence called three experts as witnesses: Michael Behe, Steve Fuller, and Scott Minnich. 

 

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The Dover mandate was declared illegal on December 20, 2005, and intelligent design was forbade from being taught in Pennsylvania's Middle District public school science classes. Judge Jones' 139 page findings of fact and decision were published along with his order. Eight members of the Dover school board who supported the requirement for teaching intelligent design in science classes were defeated in an election on November 8, 2005, by challengers who opposed the requirement.

 

The current president of the school board declared that the board would not appeal the decision. Judge Jones condemned the "Teach the Controversy" approach in his fact - finding by saying the following. Furthermore, proponents of ID have pushed for the issue to be taught in science classes rather than ID itself, hoping to dodge the rigorous examination that we have now decided it cannot withstand. This strategy is, at best, deceptive, and, at worst, a canard. 

The IDM seeks to spark a revolution that would replace evolutionary theory with ID rather than to promote critical thought. Judge Jones admitted in his judgement that he expected his decision to be criticised, noting the following. People who don't agree with our conclusion are likely to label it the work of an activist judge. If so, they would have been mistaken because it is clear that this Court is not active.

 

Instead, this issue was brought to our attention as a result of the activism of an uninformed faction on a school board who, with the help of a major public interest law firm looking for a constitutional test case on ID, convinced the Board to enact a foolish and ultimately unconstitutional policy. When viewed in light of the factual background that has now been completely revealed via this trial, the Board's decision's astounding absurdity becomes clear. 

 

The Dover Area School District's teachers, parents, and children deserved more than to be drawn into this legal tangle, with all of the time, money, and energy it will consume. Jones foresaw what Associate Director John G. West of the Centre for Science and Culture said. The Dover judgement is an attempt by an activist federal court to stop the dissemination of a scientific notion and even to suppress criticism of Darwinian evolution by governmental imposed censorship rather than open debate.

 

It won't work. He misrepresents intelligent design and the goals of the scientists who study it to the fullest extent possible. He has combined the perspective of the Discovery Institute with that of the Dover school board. The judge is described as "a Republican and a churchgoer" by newspapers. The judgement has been scrutinised for errors and implications, including by supporters of intelligent design hoping to prevent more legal setbacks. 

 

The Montana Law Review published three articles in its winter 2007 issue. David K. DeWolf, John G. West, and Casey Luskin, all of the Discovery Institute, argued in the first that intelligent design is a valid scientific theory, the Jones court should not have addressed the issue of whether it was a scientific theory, and the Kitzmiller decision will have no impact at all on the advancement and adoption of intelligent design as an alternative to traditional evolutionary theory. 

 

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In the second, Peter H. Irons responded, claiming that the decision was very well-reasoned and signals the end of attempts by proponents of intelligent design to teach creationism in public schools. In the third, DeWolf et al. addressed Irons' comments. However, some school boards have given up on "teach the controversy" plans for intelligent design due to concern of a similar lawsuit. Since 2001, a number of anti - evolution proposals have been presented in state and federal legislatures around the country, many of which are essentially based on text created by the Discovery Institute for the Santorum Amendment.

 

Their goal is to expose more students to papers and films created by opponents of evolution who favour intelligent design. They have been portrayed as advocating for "academic freedom" on the grounds that college professors, students, and teachers need to be protected because doing so exposes them to intimidation and retaliation when they debate scientific objections to evolution. The law's opponents have noted that there are no convincing scientific arguments against evolution, and a Florida investigation into claims of intimidation and retaliation found no proof of such behaviour. 

 

With the exception of Louisiana's Louisiana Science Education Act, which was passed in 2008, the vast majority of the measures have failed. Creation science and intelligent design should not be taught in science classes, according to the American Academy of Religion's Guidelines for Teaching About Religion in K–12 Public Schools in the United States, which stated that "creation science and intelligent design represent world views that fall outside of the realm of science, which is defined as ( and limited to ) a method of inquiry based on gathering observable and measurable evidence subject to scrutiny."  

 

These worldviews, along with others, "that speculate about the origins of life represent another important and pertinent form of human inquiry that is appropriately studied in literature or social science courses," though. To avoid elevating one viewpoint as more valid than others, such research must instead incorporate a diversity of worldviews that represent various religious and philosophical orientations. 

 

The dangers of creationism in education was a report released in June 2007 by the Committee on Culture, Science, and Education of the Council of Europe. It states that "creationism in any of its forms, such as 'intelligent design,' is not based on facts, does not use any scientific reasoning, and its contents are pathetically inadequate for science classes."

 

It referred to intelligent design as "anti-science" and as involving "blatant scientific fraud" and "intellectual deception" that "blurs the nature, objectives, and limits of science" and connected it and other types of creationism to denialism in describing the risks posed to education by teaching creationism. The Council of Europe's Parliamentary Assembly passed a resolution on October 4 calling for schools to "resist presentation of creationist ideas in any discipline other than religion," including "intelligent design," which it referred to as "the latest, more refined version of creationism," "presented in a more subtle way."

 

It is emphasised in the resolution that the report's goal is to "warn against certain tendencies to pass off a belief as science," not to contest or contest a belief. There are numerous faith schools in the United Kingdom that instruct students in the tenets of specific denominations. Public education in the country also includes religious instruction. The Department for Education and Skills ( DfES ) stated that "Neither creationism nor intelligent design are taught as a subject in schools, and are not specified in the science curriculum" ( part of the National Curriculum, which does not apply ).

 

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This was in response to revelations that a group called Truth in Science had distributed DVDs produced by Illustra Media featuring Discovery Institute fellows arguing for design in nature and claimed they were being used by 59 schools. However, the DfES left the door open for it to be discussed in religious education in relation to various beliefs, as part of a syllabus set by a local Standing Advisory Council on Religious Education.

 

The DfES later stated that "Intelligent design is not a recognised scientific theory; therefore, it is not included in the science curriculum." A model unit on "Religious Education" was developed by the Qualifications and Curriculum Authority in 2006 so that students could study about both religious and nonreligious perspectives on creationism, intelligent design, and evolution through natural selection. 

 

Creationism and intelligent design should not be taught as science, the UK government ruled in response to an online petition on June 25, 2007, while instructors would still be obligated to reply to students' inquiries within the conventional framework of accepted scientific theories. On September 18, 2007, the English government issued comprehensive "Creationism teaching guidance" for use in classrooms. It claims that "Intelligent design lies wholly outside of science", has no underlying scientific principles or explanations, and is not acknowledged by the scientific community as a whole. 

 

Creationism and intelligent design should not be taught as science, but "any questions about creationism and intelligent design which arise in science lessons, for example as a result of media coverage, could provide the opportunity to explain or explore why they are not considered to be scientific theories and, in the right context, why evolution is considered to be a scientific theory" ( Creationism and Intelligent Design in Science Lessons ). However, "teachers of subjects like RE, history or citizenship may deal with creationism and intelligent design in their lessons." 

 

The British Centre for Science Education lobbying group has been active in government lobbying in the UK with the aim of "countering creationism within the UK." The Department for Education in Northern Ireland claims that the curriculum allows for the teaching of alternative theories. The Democratic Unionist Party (DUP), which is associated with conservative Christians, has been pushing for the inclusion of intelligent design in scientific curricula. 

 

David Simpson, a former DUP member of parliament, has asked the education minister for reassurance that students won't lose marks if they respond to science questions with creationist or intelligent design responses. The British Centre for Science Education is a lobbying organisation that works with the UK government on issues related to "countering creationism within the UK." According to the Department for Education in Northern Ireland, the curriculum offers the chance to teach alternative theories.

 

A push to teach intelligent design in science schools has been launched by the Democratic Unionist Party ( DUP ), which has ties to conservative Christianity. Former DUP MP David Simpson has asked the education minister for reassurances that students will not lose marks if they respond to science questions with creationism or intelligent design arguments. 

 

A DUP request to write to post-primary schools inquiring about their plans to provide teaching materials in respect to "creation, intelligent design, and other theories of origin" was approved by Lisburn city council in 2007. In 2005, plans by Dutch Minister of Education Maria van der Hoeven to "stimulate an academic debate" on the issue sparked a fierce outcry from the general population. Ronald Plasterk, a "molecular geneticist, ardent atheist, and opponent of intelligent design," succeeded her following the 2006 elections. 

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Mieke Van Hecke, the director general of the Flemish Secretariat of Catholic Education or VSKO, responded to the situation in the Netherlands by stating that, "Catholic scientists have long accepted the theory of evolution and that intelligent design and creationism have no place in Flemish Catholic schools. The objective and mission of science is to propose new ideas; it is not the responsibility of politics to do so. Australia and the UK have somewhat similar attitudes towards intelligent design.

Brendan Nelson, the Australian Minister for Education, scientific, and Training, brought up the idea of teaching intelligent design in scientific schools in 2005. The minister immediately agreed after the uproar from the general public that religion or philosophy classes are the best places to teach intelligent design, if it were to be taught at all. The Unlocking the Mystery of Life (2002) DVD was delivered to Australian secondary schools by the Campus Crusade for Christ branch in Australia.

 

One of the top private schools in Australia, The King's School, led by Tim Hawkes, supported the use of the DVD in the classroom at the sole discretion of the instructors and principals. Famous Pakistani - Canadian Muslim Muzaffar Iqbal signed the petition "A Scientific Dissent From Darwinism" from the Discovery Institute. Muslims have historically regarded intelligent design theories as respectable intellectual positions, and numerous intelligent design works have been translated into Turkish.

 

Istanbul's local government sponsored public gatherings in 2007 that promoted intelligent design, and a meeting in May 2007 featured David Berlinski of the Discovery Institute as its keynote speaker. Rethinking Darwin : A Vedic Study of Darwinism and Intelligent Design is a book on intelligent design that was published in 2011 by the Bhaktivedanta Book Trust of the International Society for Krishna Consciousness ( ISKCON ). Along with creationists who believe in Hinduism, Leif A. Jensen and Michael Cremo, the book also includes arguments for intelligent design from William A. Dembski, Jonathan Wells and Michael Behe.

Information Processing  - Brief Overview :

Information processing refers to any modification or processing of information that may be seen by an observer. Therefore, it is a process that encompasses everything that occurs or changes in the cosmos, from a rock falling or changing position to a text file printing from a digital computer system. In the latter situation, a text file's manner of presentation is being changed from bytes to glyphs by an information processor or the printer. In the past, computers didn't have their own intellect. Instead, they ran programmes that were stored in memory.

Information processing is a method used in cognitive psychology to study how people think and how they process information in a manner similar to that of computers, per Shannon & Weaver, 1963. After World War II, in the 1940s and 1950s, it first appeared per Sternberg & Sternberg, 2012. The method views cognition as fundamentally computational, with the mind serving as the software and the brain as the hardware.

The computational theory of mind in philosophy and the information processing approach in psychology share close ties, albeit they are not the same per Horst, 2011. It is also related to cognitivism in psychology and functionalism in philosophy, though they are not the same. Both vertical and horizontal information processing can be centralised or decentralised and distributed. In the middle of the 1980s, a method of horizontally distributed processing known as connectionism gained popularity.

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The connectionist network consists of many nodes, and it functions through a "priming effect," which occurs when a "prime node activates a connected node". On the other hand, "unlike in semantic networks, it is not a single node that has a specific meaning, but rather the knowledge is represented in a combination of differently activated nodes". There are a number of hypotheses and models that have been put up to explain how we process information.

Because everyone has a distinct capacity for processing information, they all reach different levels of information overload under the same information load, per Eppler and Mengis, 2004. ​The three elements of creativity, analysis, and practicality make up the three parts of Sternberg's theory of intelligence, per Sternberg & Sternberg, 2012. Being analytical can assist a person in determining whether or not an idea is good. Creativity is the capacity to generate novel, original ideas.

 

According to Sternberg & Sternberg in 2012, "Practical skills are used to implement the ideas and persuade others of their value." Processing of information and cognition are at the centre of Sternberg's theory. According to Sternberg's theory, information processing is divided into three parts, viz., meta components, performance components, and knowledge acquisition components, per Sternberg & Sternberg, 2012. Lower order executive functions are affected by these processes as they ascend up the executive hierarchy. 

 

While performance components carry out the instructions of the meta components, knowledge acquisition components learn how to solve the problems, and meta components are used to design and evaluate problems, per Sternberg & Sternberg, 2012. Putting this theory into practise can be done through engaging in creative endeavours. Making a choice about what to draw comes first, followed by a plan and a sketch. While this is happening, the process is being watched to see if the desired result is being produced.

All of these actions fall under the processing of the meta component, and the art is the performance component. The element of learning that involves developing one's sketching abilities. "The sciences concerned with gathering, manipulating, storing, retrieving, and classifying recorded information" is how information processing has been defined. According to the Atkinson - Shiffrin memory model, often known as the multi - store model, information must go through three phases of mental processing before it can be securely ingrained in memory, viz., sensory memory, short term memory, and long term memory. 

 

Working memory models is one instance of this. The central executive, the phonologic loop, the episodic buffer, the visuospatial sketchpad, the verbal information, long term memory, and the visual information are all included in this, per Sternberg & Sternberg, 2012. The central executive functions as the brain's secretary. It chooses what requires focus and how to react. The three different subsections are then led by the central executive. The phonological loop, subvocal rehearsal, and phonological storage make up the first group. 

 

Together, these areas help people comprehend words, store information in their memories, and then retain those memories. Verbal information storage is the end consequence. The visuospatial sketchpad, which is the next subsection, is used to store visual images. Although the storing capacity is limited, it helps people comprehend visual inputs. There is also an episodic buffer. Information can be taken by this area and stored in long - term memory.

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Additionally, it has the capacity to incorporate data from the visuospatial sketchpad and phonological loop into long-term memory to create "a unitary episodic representation", per Sternberg & Sternberg, 2012. The sensory register processes information from the five senses, viz., visual, aural, tactile, olfactory, and taste, in order for these to function. All of these have been present since birth and are capable of handling simultaneous processing, such as tasting, smelling, and seeing food.

In general, when a method of pattern recognition is formed, learning gains result. The sensory register is quite capacious and has a quick behavioural response of around 1 to 3 seconds. The capacity of the sensory store and working memory is constrained in this concept. relatively little information can be stored in the sensory store for relatively short periods of time. This behaviour is extremely comparable to having a flash used when taking a picture. After the flash fires, it appears to be present for a brief period of time. 

The problem is that it vanishes quickly and leaves no trace, per Sternberg & Sternberg, 2012. Though it still has a little capacity, short term memory can store information for slightly longer periods of time. In accordance with Linden in 2007, "The capacity of STM had first been estimated at "seven plus or minus two" items, per Miller 1956, which reflects the observation from neuropsychological testing that the average digit span of healthy persons is about seven.

However, it became clear that these large numbers of objects can only be remembered if they are categorised into so called chunks using conceptual or perceptual links between different stimuli. Before it leaves the subject's thoughts, it lasts for 5 to 20 seconds. This frequently happens when names of new acquaintances are mentioned. Additionally, information based on images or meaning is retained here, but without rehearsal or repetition, it deteriorates.

 

The capacity of long term memory, however, may be infinite, and its endurance is virtually limitless, per Sternberg & Sternberg, 2012. Although sometimes challenging to obtain, it contains all of the knowledge acquired up to this point. The information could slip one's mind or seem to be on the tip of one's tongue. Jean Piaget offered what is known as the Piaget's Cognitive Development Theory as an alternative perspective on how people process information, per Presnell, 1999.

 

Piaget built his framework around growth and development. He distinguished four stages between various age ranges, each with its own specific cognitive process and sort of knowledge. The sensorimotor from birth to two years, preoperational from two to six years, concrete operational from six to eleven years, and formal operational from eleven years and older phases are the four stages. In the sensorimotor stage, infants and toddlers process information from their senses and react to it with reflexes.

 

Children learn through imitation at the preoperational stage and are unable to understand the perspectives of others. In the concrete operational level, logic based problem solving skills and the capacity to take a variety of circumstances into account are still growing. The formal operational stage is the final one, where preadolescents and adolescents start to comprehend abstract ideas and start to gain the ability to formulate arguments and counter arguments. 

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Adolescence is also marked by a number of adjustments in the biological, cognitive, and social spheres. It is vital to note that the limbic system and prefrontal cortex of the brain both go through significant alterations in the cognitive domain. When performing complex cognitive tasks like planning, coming up with goals and plans, making intuitive decisions, and metacognition or thinking about thinking, the prefrontal cortex of the brain is active. This is in line with the final step of formal operations proposed by Piaget, per McLeod, 2010.

 

Between early adulthood and adolescence, the prefrontal cortex is fully developed. Based on variations in the amounts of neurotransmitters such dopamine and emotions, the limbic system is the area of the brain that modifies reward sensitivity. In summary, our cognitive capacities change as we grow and progress through life. We are better equipped to plan ahead, analyse and understand abstract ideas, and accurately weigh the pros and cons of a given situation as adults as opposed to adolescents or children. 

 

Information processing in computing broadly refers to the application of algorithms to data transformation or the characteristic function of computers. The International Federation for Information Processing or IFIP is a large organisation of computing professionals. It is essentially the same as the terms "data processing" or "computation," but with a broader meaning. 

Health Informatics - A Peek :

The study, processing, and analysis of patient data from many sources and modalities, including electronic health records, results of diagnostic tests, and medical scans, is the goal of the scientific and engineering field of health informatics. Computational methods can be used to solve a huge variety of issues in the health area. The study of the conception, creation, and implementation of computer technologies to enhance healthcare falls under the umbrella of the multidisciplinary area known as "health informatics."

 

The involved disciplines combine the fields of medicine and computing, particularly computer engineering, software engineering, information engineering, bioinformatics, bio-inspired computing, theoretical computer science, information systems, data science, information technology, autonomic computing, and, behaviour informatics. Medical informatics research at academic institutions focuses on developing embedded systems based medical devices and using AI in healthcare applications.

 

In certain nations, applying library science to data administration in hospitals is also referred to as informatics. Clinical informatics is a specialisation of various medical specialties, and "clinical informaticians" are qualified health and social care professionals. According to Jan van Bemmel, medical informatics is the theoretical and applied elements of information processing and communication based on expertise and knowledge gained from processes in medicine and health care.

 

Applications range from image processing that can be utilised in radiography to the 2D Fourier transform, which can be used to remove extraneous data from an X-ray scan. Six high level domains of core competencies have been recognised for clinical informaticians by the Faculty of Clinical Informatics. Information technologies, systems, and the practise of health and wellbeing decision-making, working with data and analytical techniques, and enabling organisational and human change. 

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Let's talk about the resources for supporting practitioners. The following is accomplished by clinical informaticians using their understanding of patient care together with informatics principles, methodologies, and technologies. Analyse the information and knowledge requirements of patients, medical providers, and patient families. Describe, assess, and improve clinical procedures. Construct, put into action, and improve clinical decision support systems. 


Take the initiative or take part in the development, management, management, installation, modification, management, evaluation, and ongoing improvement of clinical information systems. To create health informatics tools that support patient care that is safe, effective, timely, patient-centered, and equitable, clinicians work with other health care and IT professionals. Computer scientists make up a large portion of clinical informaticists. "Why Doctors Hate Their Computers" illustrates the frustrations that many medical professionals have. 

 

Telehealth is the delivery of medical services and knowledge via electronic communication and information technology. It enables remote admissions as well as long-distance patient and clinician interaction, care, guidance, and reminders. Sometimes the term "telemedicine" is used synonymously or in a narrower meaning to refer to remote healthcare services like diagnosis and monitoring. Medical personnel can remotely monitor a patient utilising a variety of technology gadgets through remote monitoring, commonly referred to as self-monitoring or testing.

 

Managing chronic diseases or particular ailments like heart disease, diabetes mellitus, or asthma are the main uses of this technique. These services can offer patients better satisfaction and health outcomes that are comparable to those of conventional in-person patient consultations. They may also be more affordable. E - rehabilitation, also known as telerehabilitation, is the delivery of rehabilitation services over the Internet and telecommunications networks.

 

Clinical evaluation of the patient's functional abilities in his or her environment and clinical therapy are the two broad categories into which the majority of services can be divided. Neuropsychology, speech - language pathology, audiology, occupational therapy, and physical therapy are a few rehabilitation practice areas that have looked into telerehabilitation. People who are unable to travel to a clinic due to a medical condition or the length of the trip can receive therapy through telerehabilitation.

 

Telerehabilitation also enables rehabilitation specialists to conduct a clinical consultation via the internet. Edward H. Shortliffe, an American biomedical informatician, was a pioneer in the use of artificial intelligence to healthcare. This topic focuses on using artificial intelligence and machine - learning algorithms to simulate human cognition in the analysis, interpretation, and comprehension of challenging medical and healthcare data. Artificial intelligence or AI is the capacity of computer algorithms to estimate results based only on input data.

 

Applications of artificial intelligence or AI programmes include the establishment of treatment protocols, medication discovery, personalised medicine, and patient monitoring and care. Clinical decision support systems are the primary area of industrial concentration for the application of AI in the healthcare sector. Machine learning algorithms evolve as more data is gathered and make it possible for responses and solutions to be more reliable. Many businesses are looking into the potential applications of big data in the healthcare sector.

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The "data assessment, storage, management, and analysis technologies" sectors, which are all essential components of the healthcare industry, are where many businesses look into the market opportunities. Examples of significant businesses that have contributed to AI algorithms for application in healthcare include the following. Cleveland Clinic and Memorial Sloan Kettering Cancer Centre are developing IBM's Watson Oncology. IBM also collaborates with Johnson & Johnson and CVS Health. It is on the analysis of scientific publications to uncover new linkages for medication development and on the use of AI in the treatment of chronic diseases. 

 

An initiative called Health Empowerment by Analytics, Learning and Semantics (HEALS), a collaboration between IBM and Rensselaer Polytechnic Institute, was launched in May 2017 to investigate how AI technology may improve healthcare. With the help of the Knight Cancer Institute at Oregon Health & Science University and Microsoft, the Hanover project forecasts the most successful cancer drug treatments for patients by examining medical studies. Analysing the evolution of tumours using medical images and creating programmable cells are two other initiatives.


The UK National Health Service is using Google's DeepMind platform to identify specific health concerns using information gathered from a mobile app. To develop computer vision algorithms to find malignant tissues, a second initiative with the NHS analyses medical photos gathered from NHS patients. Tencent is developing a number of medical services and systems. These include Tencent, WeChat Intelligent Healthcare, and AI Medical Innovation System (AIMIS), a diagnostic medical imaging service powered by artificial intelligence.

 

Doctorwork Lumiata, a firm that utilises AI to identify at-risk patients and provide treatment options, recently received investment from Intel Capital, the venture capital arm of Intel. Deep learning software was created by Kheiron Medical to identify breast cancer in mammography. Qure.ai, developed by Fractal Analytics, focuses on utilising deep learning and AI to advance radiology and expedite the interpretation of diagnostic x-rays. A new generation of neuroprosthetics developed by Neuralink intricately integrate with the brain's thousands of neuronal networks.

 

Their technique enables a precise surgical robot to replace a piece of the patient's skull with a chip, about the size of a quarter, to prevent unintentional harm. Applications that employ AI to provide medical consultation based on user medical histories and general medical knowledge include Babylon Health's GP at Hand, Ada Health, AliHealth Doctor You, KareXpert, and Your.MD. Users enter their symptoms through the app, which compares them against a database of ailments using speech recognition.

 

After that, Babylon provides a suggested course of action while taking the user's medical history into account. Seven business model models have been successfully used by healthcare entrepreneurs to provide AI solutions to the market. Depending on the value created for the intended user ( e.g., patient focus versus healthcare provider and payer focus ) and value capture mechanisms ( e.g., sharing information or bringing stakeholders together ), these archetypes will differ.

 

The "Xiao Man" service robot from IFlytek uses artificial intelligence to recognise its registered users and offer tailored recommendations in the medical field. In the area of medical imaging, it also functions. Companies like UBTECH ( "Cruzr" ) and Softbank Robotics ( "Pepper" ) are also producing robots with a similar design. A WhatsApp chatbot created by the Indian startup Haptik recently responds to inquiries on the deadly coronavirus in India.

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Large IT corporations like Apple, Google, Amazon, and Baidu all have their own AI research sections, as well as millions of dollars set aside for the acquisition of smaller AI-based companies, as the market for AI is continually developing. Machine learning in healthcare is also starting to be used by many automakers in their vehicles. Companies like BMW, GE, Tesla, Toyota, and Volvo are all engaged in new research projects to figure out how to gather information on a driver's vital statistics to make sure they are alert, focused on the road, and not impaired by drugs or their emotions.

 

The COACH project is one example of a computational health informatics initiative. Using informatics techniques, the field of clinical research informatics or CRI seeks to increase the effectiveness of clinical research. In order to streamline ethical approvals and renewals in the US, where the local institutional review board is responsible, CRI is tackling issues such as building data warehouses of health care data that can be used for research, supporting data collection in clinical trials with the use of electronic data capture systems, and maintaining repositories of previous clinical trial data de - identified.

 

CRI is a very new area in informatics, and it has experienced growing pains like any other emerging field. The ability of statisticians and computer system architects to collaborate with the clinical research team in the design of a system and a lack of financing to assist the creation of a new system are some problems CRI encounters. In order to develop a system that is user - friendly for the research team and complies with the system requirements of the computer team, researchers and the informatics team struggle to coordinate plans and ideas.

 

The CRI's development may be hampered by a lack of financing. Many research-based organisations struggle to secure funding for their projects, let alone invest that money in an informatics system that won't increase their revenue or enhance the research's findings, per Embi, 2009. Clinical research informatics heavily relies on the capacity to combine data from many clinical trials. Improvements in secondary use of information gathered in prior human clinical trials were made as a result of initiatives like PhenX and the Patient-Reported Outcomes Measurement Information System.

 

The goal of CDE projects, for instance, is to make it possible for clinical trial designers to use standardised research tools as electronic case report forms. Initiatives that allow researchers who want to re-use this data to download de - identified patient level clinical study data are concurrent efforts to standardising how data is collected. These platforms include Project Data Sphere, dbGaP, ImmPort, and Clinical Study Data Request, as examples. Informatics problems with data formats for sharing results, viz., basic CSV files, FDA a pproved formats, like the CDISC Study Data Tabulation Model, are significant obstacles in the field of clinical research informatics.

 

CRI provides financial support for a variety of clinical research initiatives, including the following benefits. Improved and more effective data acquisition and collection. Enhanced clinical trial enrollment. Optimum protocol development and effective administration. Data storage, transport, processing, and analysis. Patient recruitment and management. Adverse event reporting regulatory compliance. Also, the databases holding data from clinical trials that have been finished for secondary analyses. 

 

Utilising integrated data repositories is among the core components of biomedical and translational research. According to a survey done in 2010, a "integrated data repository" ( IDR ) is a data warehouse that incorporates several clinical data sources to facilitate queries for a variety of research like purposes. Complex systems known as integrated data repositories were created to address a wide range of issues, including identity management, confidentiality protection, the comparability of data from many sources on a semantic and syntactic level, and most importantly convenient and flexible querying.

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Large data sets were produced as a result of the development of clinical informatics, which included the integration of electronic health record data with other data such as genetic data. Clinical data marts, operational data stores ( ODSs ), clinical data warehouses ( CDWs ), and clinical registries are examples of several types of data repositories. operational data stores created for extraction, transferring, and loading prior to building a warehouse or data mart. Although clinical registry collections have been around for a while, their information is often disease - specific and outdated.

 

Clinical data warehouses and clinical data stores are thought to be quick and dependable. Although these sizable integrated archives have had a considerable impact on clinical research, there are still obstacles and challenges. One significant issue is the requirement for institutional review board ( IRB ) ethical approval prior to any research analysis intended for publication. Some research resources are available without IRB permission. 

 

For instance, CDWs that contain patient data who have passed away have been de-identified, and their use is not subject to IRB permission. Data quality is another difficulty. Methods that correct for bias rely on the recording of a whole health record, such as propensity score matching techniques. Finding problems with data quality is made easier by tools that look at the quality of the data, e.g., point out missing data. 

 

Translational Bioinformatics - A Brief :

When the human genome sequence was published in 2000, a relatively new area called translational bioinformatics ( TBI ) emerged. The AMIA website contains a lengthy explanation of TBI that is frequently utilised. TBI could be explained more simply as the collecting of enormous volumes of biological and genetic health-related data and their conversion into therapeutic entities that are uniquely personalised. Currently, the TBI field can be divided into four main themes, which are briefly discussed below. 

 

Electronic health records are gathered into clinical big data, which is then used to innovate. To improve patient outcomes, it is suggested that practice - based medicine and the evidence - based approach currently used in medicine be combined. According to Darren Schutle, CEO of the California-based cognitive computing company Apixio, gathering, merging, and analysing data from diverse medical records will allow for better patient care.

 

Additionally, the combination of comparable profiles can act as a foundation for personalised medicine by indicating what approaches to treatment are effective and ineffective for a given ailment, per Marr, 2016. 

 

Genomic medicine in health care helps to determine whether genes are involved in undiscovered or uncommon conditions or syndromes, genomic data are used. Oncology is the field where the use of genomics is currently most active. During oncological therapy processes, the discovery of cancer's genomic sequencing may help define the causes of drug sensitivity and resistance. Omics for medication repurposing and discovery. Repurposing a medicine is an interesting concept.

 

It enables pharmaceutical companies to sell a drug that has already received FDA approval for one condition or disease to treat a second ailment or disease. The discovery of "molecular signatures in disease and compare those to signatures observed in cells" suggests that a medicine may be able to treat or at least lessen the symptoms of a disease. Individual genomic testing or Direct - to - consumer ( DTC ) genetic testing is provided by a number of businesses in the US. 23andMe is the name of the business that does the majority of testing.

 

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Numerous ethical, legal, and societal issues are raised by the use of genetic testing in healthcare. One of the key challenges is whether the healthcare professionals are prepared to use genomic data provided by the patient while still providing high - quality, unbiased care, despite their extensive understanding of genomics. The recorded instances of integrating such information into a delivery system for health care demonstrated both positive and negative effects on the overall outcomes connected to health care. 

 

Medical signal processing is a crucial area of information engineering in the medical field. It refers to the creation, examination, and application of signals, which can be in the form of sound, visual, electrical, or biological signals, among others. Medical image computing and imaging informatics develop computational and mathematical techniques to address issues relating to the use of medical images in clinical treatment and biological research. These domains aim to derive knowledge or information that is therapeutically pertinent from computer analyses of medical pictures.

 

Image segmentation, image - based physiological modelling, and other methods can all be divided into numerous broad groups. A robot utilised in the medical sciences is known as a medical robot. Among them are surgical robots. In the majority of tele - manipulators, the "effector" on the opposite side is controlled by the surgeon's activators on the opposite side. The following categories of medical robots exist. 

 

Surgical robots that either enable remote surgery where a human surgeon is not physically present with the patient or allow surgical operations to be carried out with greater precision than an unaided human surgeon. Robots for rehabilitation that help and assist the lives of the old, infirm, or those whose body parts are dysfunctional and affect their ability to move. These robots are also utilised for therapy and other related processes like training.
Biorobots are a class of robots created to mimic human and animal cognition. 

 

Remote medical personnel may move, look around, interact, and participate thanks to telepresence robots. Pharmacy automation includes robotic devices that prepare sterile IV admixtures in a hospital pharmacy or dispense oral solids in a retail pharmacy. Robot companions are able to develop emotional connections with their users, keeping them company and alerting them to health issues. Disinfection robot typically uses pulsed ultraviolet light and is capable of quickly disinfecting an entire room. In order to combat the Ebola virus sickness, they are utilised. 

 

With the advent of computers in the early 1950s, computer technology in medicine was first used on a global scale. The first professional organisation for informatics in Germany was founded by Gustav Wagner in 1949. Reference is made to the prehistory, history, and future of medical and health information technology. In France, Germany, Belgium, and the Netherlands, specialised university departments and informatics training programmes got their start in the 1960s. In Poland and the US, medical informatics research units first appeared in the 1970s.

 

Since that time, the U.S. and the European Union have worked to build high - quality health informatics research, teaching, and infrastructure. Medical computing, biomedical computing, medical computer science, medical computer technology, medical electronic data processing, medical automatic data processing, medical information processing, medical information science, medical software engineering, and medical computer technology were some of the early names for health informatics. 

 

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Although the field of health informatics is still developing and far from being a fully developed profession, work by the UK Council of Health Informatics Professions, a voluntary registration body, has identified eight key constituencies within the field: information management, knowledge management, portfolio or program or project management, ICT, education and research, clinical informatics, health records, especially service and business - related, and health informatics service management.

 

Professionals from the NHS, academia, and commercial service and solution suppliers are all represented in these constituencies. The International Medical Informatics Association ( IMIA ) has been the most significant international coordinating organisation since the 1970s. As it relates to information technology in domains that are relevant to health, health informatics law deals with changing and occasionally complex legal issues. It deals with the privacy, moral, and practical concerns that usually come up when using technological tools, information, and media in the delivery of healthcare.

 

All issues including information technology, healthcare, and the exchange of information are covered under health informatics law. It focuses on the conditions under which information and records are shared with other disciplines or sectors that support and improve patient care. Providers must develop security measures to guarantee the security of patient information because many health care systems are working to make patient records more easily accessible to them online. 

 

They must be able to guarantee the privacy, the accuracy, and the security of the data involving people, processes, and technology. It is crucial that this component of their private information will also be safeguarded using encryption because there is a chance that payments will be made through this system. Technology is increasingly being used in healthcare settings, and this trend is predicted to continue.

 

Electronic health records or the EHRs, computerised charting, and other types of health information technology systems were implemented by various healthcare facilities in order to provide patient care. The risk of potential breaches in patients' confidentiality and privacy rose with the rise in popularity of health information technology systems and the expansion of the amount of health information that may be communicated and moved electronically.

 

This worry led to the adoption of stringent regulations by both legislators and specific facilities to guarantee patient confidentiality and privacy. The Health Insurance Portability and Accountability Act of 1996, or HIPAA, is one of the federal laws designed to protect patients' health information like medical records, billing information, treatment plans, etc., and to ensure patients' privacy. Patient authority and autonomy over their own health records are granted by HIPAA. 

 

Additionally, the U.S. Department of Health & Human Services states that this law permits patients to read their own medical records, obtain copies of their own records, ask for any inaccurate information to be corrected, find out who has access to their records, and ask for restrictions on who may and cannot view or access their records. One of the first journals devoted to health informatics was Computers and Biomedical Research, which was first published in 1967.

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Other early publications were the American Medical Association's Computers and Medicine, Gallagher Printing's Journal of Clinical Computing, Plenum Press' Journal of Medical Systems, and Springer - Verlag's MD Computing. The first nursing specific journal, currently known as Computers Informatics Nursing or CIN, was first published by Lippincott in 1984 under the name Journal Computers in Nursing. About 235 informatics journals were available in the National Library of Medicine's or NLM journal catalogue as of September 7, 2016. 

 

According to the Journal Citation Reports for 2018, the Journal of Medical Internet Research with impact factor of 4.945, JMIR mHealth and uHealth with 4.301, and the Journal of the American Medical Informatics Association with 4.292 are the top three journals in the field of medical informatics. Clinical informatics is one of many medical subspecialties in the United States.

 

For instance, the American Board of Pathology offers certification in clinical informatics to pathologists who have completed 24 months of associated training, while the American Board of Preventive Medicine offers clinical informatics certification to practitioners of preventive medicine. The development of MD only physician certification in clinical informatics was announced in October 2011 by the American Board of Medical Specialties or the ABMS, the body in charge of specialty MD certification in the United States.

 

The American Board of Preventive Medicine or ABPM administered the first exam for board certification in the clinical informatics subspecialty in October 2013. 432 candidates passed, becoming the first class of clinical informatics Diplomates in 2014. Physicians who want to earn a clinical informatics board certification might do so through fellowship programmes. Doctors must have graduated from a medical school in the US, Canada, or another country that has been approved by the ABPM. 

 

They must also complete a primary residency programme in a medical specialty like internal medicine or one of the 24 sub specialties recognised by the ABMS and be qualified to get a medical licence in the state where their fellowship programme is located. The 24 - month fellowship programme is divided into informatics rotations, didactic methods, research, and clinical practice in the fellows' primary field of expertise. 

 

Bio Informatics - A Peek :

An interdisciplinary discipline of study called bioinformatics creates techniques and software tools for comprehending biological data, particularly when the data sets are big and complicated. To analyse and understand biological data, bioinformatics employs biology, chemistry, physics, computer science, computer programming, information engineering, mathematics, and statistics. Computational biology is the process of analysing and interpreting data that comes after. 

 

Computer simulation investigations of biological inquiries have utilised computational, statistical, and programming methodologies. They include reusing specialised analysis "pipelines," especially in the area of genomics, where genes and single nucleotide polymorphisms or SNPs are identified. These pipelines are used to learn more about the genetic underpinnings of disease, particular adaptations, desirable traits, especially in agricultural species, or population level variations. 

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Proteomics, a branch of bioinformatics, studies the organising principles of nucleic acid and protein sequences. Large amounts of raw data can be used to derive valuable results via image and signal processing. It supports the sequencing and annotation of genomes and their reported mutations in the field of genetics. Text mining of biological literature and the creation of biological and gene ontologies for the organisation and querying of biological data are examples of bioinformatics.

 

It also contributes to the study of the control and expression of genes and proteins. The comparison, analysis, and interpretation of genetic and genomic data, as well as a broader understanding of the evolutionary elements of molecular biology, are made easier with the use of bioinformatics tools. It aids in the analysis and cataloguing of the biological networks and pathways that are a crucial component of systems biology on a more integrative level. It helps in the simulation and modelling of DNA, RNA, proteins, and biomolecular interactions in structural biology. 

 

Paulien Hogeweg and Ben Hesper initially used the word "bioinformatics" to describe the study of information processes in biotic systems in 1970. According to this definition, bioinformatics is a subfield of biochemistry, which is the study of chemical reactions in biological systems. Analysis of biological data, particularly DNA, RNA, and protein sequences, was a component of computational biology and bioinformatics. Beginning in the middle of the 1990s, the area of bioinformatics grew rapidly, partly due to the Human Genome Project and to the quick development of DNA sequencing technology. 

 

Writing and operating software programmes that use algorithms from graph theory, artificial intelligence, soft computing, data mining, image processing, and computer simulation are required when analysing biological data to provide useful information. Theoretical underpinnings including discrete mathematics, control theory, system theory, information theory, and statistics are only a few examples of those on which the algorithms are dependent. 

 

Since the completion of the Human Genome Project, there has been a significant improvement in both speed and cost. Some labs are now able to sequence over 100,000 billion bases year, and a complete genome may now be read for $1,000 or less. After Frederick Sanger discovered the sequence of insulin in the early 1950s, protein sequences were widely available, and computers became crucial in molecular biology. Manual comparison of several sequences proved to be inefficient.

 

One of the first protein sequence databases was created by Margaret Oakley Dayhoff, a pioneer in the field who also developed techniques for sequence alignment and molecular evolution. Her work was initially published as books. Elvin A. Kabat, who invented biological sequence analysis in 1970 with his extensive quantities of antibody sequences published online with Tai Te Wu between 1980 and 1991, was another early bioinformatics pioneer. 

 

Bacteriophage MS2 and X174 were subjected to new DNA sequencing techniques in the 1970s, and the expanded nucleotide sequences were later parsed using informational and statistical algorithms. These investigations proved the idea that bioinformatics will be illuminating by showing that well known properties, such as the coding segments and the triplet code, are exposed in simple statistical analysis. To create a complete picture of these activities, raw biological data must be combined in order to understand how normal cellular functions are altered in various disease states.

 

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The analysis and interpretation of diverse sorts of data is presently the most urgent challenge in the field of bioinformatics as a result of how the subject has developed. Protein domains, protein structures, and nucleotide and amino acid sequences are also included in this. Within bioinformatics and computational biology, significant subfields include the following. Creating and implementing computer programmes to effectively organise and use different forms of information.


The creation of novel mathematical formulas and statistical techniques to analyse interactions between individuals in massive data sets. There are techniques for, among other things, identifying a gene within a sequence, predicting the structure and / or function of a protein, and grouping protein sequences into families of related sequences. Bioinformatics' main objective is to improve our understanding of biological processes. Its emphasis on creating and using computationally expensive strategies to accomplish this goal distinguishes it from other approaches.

 

Pattern recognition, data mining, machine learning techniques, and visualisation are some examples. Sequence alignment, gene discovery, genome assembly, drug design, drug discovery, protein structure alignment, protein structure prediction, gene expression prediction, protein - protein interaction prediction, genome wide association studies, modelling of evolution, and cell division or mitosis are some of the major research initiatives in this area. 

 

In order to address theoretical and applied issues related to the management and interpretation of biological data, bioinformatics involves developing databases, algorithms, computational and statistical approaches, and theory. Rapid advancements in genomic and other molecular research tools, along with advancements in information technology, have produced a vast volume of molecular biology related knowledge during the past few decades.

 

These mathematical and computer techniques used to comprehend biological processes are referred to as bioinformatics. Mapping and analysing DNA and protein sequences, matching DNA and protein sequences for comparison, and building and displaying 3 - D models of protein structures are typical bioinformatics tasks. Since the 1977 sequencing of the bacteriophage Phage -X174, hundreds of organisms' DNA sequences have been deciphered and compiled in databases.

 

This sequencing data is analysed to identify regulatory sequences, structural motifs, repetitive sequences, RNA genes, genes encoding proteins, and regulatory sequences. When using molecular systematics to create phylogenetic trees, gene comparisons between species or within a species can reveal relationships between species or similarities in protein functions. Manually analysing DNA sequences is becoming unfeasible due to the enormous volume of data.

 

Computer tools like BLAST are frequently used to search sequences, which as of 2008, came from over 260,000 different creatures and had over 190 billion nucleotides. Sequences must first be collected from a data storage bank, such as Genbank, before analysis can begin. DNA sequencing is still a challenging issue since the raw data may be noisy or subject to interference from weak signals. For the many experimental DNA sequencing techniques, base calling algorithms have been created. 

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The majority of DNA sequencing methods yield brief sequence fragments that must be put together to form whole gene or genome sequences. According to the sequencing technology, the shotgun sequencing method, used by The Institute for Genomic Research or TIGR to sequence the first bacterial genome, Haemophilus influenzae, produces the sequences of tens of thousands of tiny DNA fragments, each between 35 and 900 nucleotides long.

 

By correctly aligning the ends of these segments, a genome assembly programme can be used to recreate the entire genome. Shotgun sequencing produces sequence data quickly, but for bigger genomes, assembling the fragments can be challenging. The assembly of the pieces for a genome the size of the human genome may take many days of CPU time on large - memory, multiprocessor systems, and the final assembly typically has many gaps that need to be filled in later.

 

Instead of chain - termination or chemical degradation methods, shotgun sequencing is the preferred approach for almost all genomes sequenced, and genome assembly algorithms are a crucial area of bioinformatics study. Annotation in genomics is the process of identifying the start and stop points of genes and other biological characteristics in a DNA sequence that has been sequenced. The majority of genomes are too big to manually annotate.

 

Genome annotation has emerged as the new bioinformatics bottleneck as the rate of genome sequencing continues to outpace that of genome annotation. The three levels of genome annotation are nucleotide, protein, and process levels. One of the main functions of nucleotide-level annotation is gene discovery. Combining ab initio gene prediction with sequence comparison to expressed sequence databases and other animals can be effective for complicated genomes.

 

The integration of the genome sequence with additional genetic and physical mappings of the genome is also made possible by nucleotide - level annotation. Assigning function to the protein products of the genome is the main goal of protein - level annotation. For this kind of annotation, databases of protein sequences, functional domains, and motifs are utilised. A new genome sequence typically has about 50% of the predicted proteins with no clear function. 

 

Process - level annotation aims to comprehend how genes and their offspring interact with cellular and organismal physiology. The inconsistent use of terminology by various model systems has been a barrier to process-level annotation. The Gene Ontology Consortium is assisting with the solution to this issue. The Institute for Genomic Research, which carried out the first extensive sequencing and study of the genome of a free-living organism, the bacterium Haemophilus influenzae, released the first description of a thorough annotation system in 1995.

 

To determine the first functional assignments, the system recognises the genes encoding all proteins, transfer RNAs, and ribosomal RNAs. The protein - coding genes of Haemophilus influenzae can be found using the GeneMark programme, which is constantly evolving and getting better. The National Human Genome Research Institute created the ENCODE project in order to carry out the objectives that the Human Genome Project left unfinished after it was shut down in 2003. 

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This project is a collaborative data collection of the functional components of the human genome using genomic tiling arrays and next - generation DNA - sequencing technologies, which can automatically generate large amounts of data at a significantly lower cost per base while maintaining the same accuracy or base call error and fidelity or assembly error. While sequence similarity and hence homology is the main factor utilised in genome annotation, other features of sequences can also be used to infer the function of genes.

Brendan Nelson, the Australian Minister for Education, scientific, and Training, brought up the idea of teaching intelligent design in scientific schools in 2005. The minister immediately agreed after the uproar from the general public that religion or philosophy classes are the best places to teach intelligent design, if it were to be taught at all. The Unlocking the Mystery of Life (2002) DVD was delivered to Australian secondary schools by the Campus Crusade for Christ branch in Australia.

 

One of the top private schools in Australia, The King's School, led by Tim Hawkes, supported the use of the DVD in the classroom at the sole discretion of the instructors and principals. Famous Pakistani - Canadian Muslim Muzaffar Iqbal signed the petition "A Scientific Dissent From Darwinism" from the Discovery Institute. Muslims have historically regarded intelligent design theories as respectable intellectual positions, and numerous intelligent design works have been translated into Turkish.

 

Istanbul's local government sponsored public gatherings in 2007 that promoted intelligent design, and a meeting in May 2007 featured David Berlinski of the Discovery Institute as its keynote speaker. Rethinking Darwin : A Vedic Study of Darwinism and Intelligent Design is a book on intelligent design that was published in 2011 by the Bhaktivedanta Book Trust of the International Society for Krishna Consciousness ( ISKCON ). Along with creationists who believe in Hinduism, Leif A. Jensen and Michael Cremo, the book also includes arguments for intelligent design from William A. Dembski, Jonathan Wells and Michael Behe.

Information Processing  - Brief Overview :

Information processing refers to any modification or processing of information that may be seen by an observer. Therefore, it is a process that encompasses everything that occurs or changes in the cosmos, from a rock falling or changing position to a text file printing from a digital computer system. In the latter situation, a text file's manner of presentation is being changed from bytes to glyphs by an information processor or the printer. In the past, computers didn't have their own intellect. Instead, they ran programmes that were stored in memory.

Information processing is a method used in cognitive psychology to study how people think and how they process information in a manner similar to that of computers, per Shannon & Weaver, 1963. After World War II, in the 1940s and 1950s, it first appeared per Sternberg & Sternberg, 2012. The method views cognition as fundamentally computational, with the mind serving as the software and the brain as the hardware.

The computational theory of mind in philosophy and the information processing approach in psychology share close ties, albeit they are not the same per Horst, 2011. It is also related to cognitivism in psychology and functionalism in philosophy, though they are not the same. Both vertical and horizontal information processing can be centralised or decentralised and distributed. In the middle of the 1980s, a method of horizontally distributed processing known as connectionism gained popularity.

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The connectionist network consists of many nodes, and it functions through a "priming effect," which occurs when a "prime node activates a connected node". On the other hand, "unlike in semantic networks, it is not a single node that has a specific meaning, but rather the knowledge is represented in a combination of differently activated nodes". There are a number of hypotheses and models that have been put up to explain how we process information.

Because everyone has a distinct capacity for processing information, they all reach different levels of information overload under the same information load, per Eppler and Mengis, 2004. ​The three elements of creativity, analysis, and practicality make up the three parts of Sternberg's theory of intelligence, per Sternberg & Sternberg, 2012. Being analytical can assist a person in determining whether or not an idea is good. Creativity is the capacity to generate novel, original ideas.

 

According to Sternberg & Sternberg in 2012, "Practical skills are used to implement the ideas and persuade others of their value." Processing of information and cognition are at the centre of Sternberg's theory. According to Sternberg's theory, information processing is divided into three parts, viz., meta components, performance components, and knowledge acquisition components, per Sternberg & Sternberg, 2012. Lower order executive functions are affected by these processes as they ascend up the executive hierarchy. 

 

While performance components carry out the instructions of the meta components, knowledge acquisition components learn how to solve the problems, and meta components are used to design and evaluate problems, per Sternberg & Sternberg, 2012. Putting this theory into practise can be done through engaging in creative endeavours. Making a choice about what to draw comes first, followed by a plan and a sketch. While this is happening, the process is being watched to see if the desired result is being produced.

All of these actions fall under the processing of the meta component, and the art is the performance component. The element of learning that involves developing one's sketching abilities. "The sciences concerned with gathering, manipulating, storing, retrieving, and classifying recorded information" is how information processing has been defined. According to the Atkinson - Shiffrin memory model, often known as the multi - store model, information must go through three phases of mental processing before it can be securely ingrained in memory, viz., sensory memory, short term memory, and long term memory. 

 

Working memory models is one instance of this. The central executive, the phonologic loop, the episodic buffer, the visuospatial sketchpad, the verbal information, long term memory, and the visual information are all included in this, per Sternberg & Sternberg, 2012. The central executive functions as the brain's secretary. It chooses what requires focus and how to react. The three different subsections are then led by the central executive. The phonological loop, subvocal rehearsal, and phonological storage make up the first group. 

 

Together, these areas help people comprehend words, store information in their memories, and then retain those memories. Verbal information storage is the end consequence. The visuospatial sketchpad, which is the next subsection, is used to store visual images. Although the storing capacity is limited, it helps people comprehend visual inputs. There is also an episodic buffer. Information can be taken by this area and stored in long - term memory.

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Additionally, it has the capacity to incorporate data from the visuospatial sketchpad and phonological loop into long-term memory to create "a unitary episodic representation", per Sternberg & Sternberg, 2012. The sensory register processes information from the five senses, viz., visual, aural, tactile, olfactory, and taste, in order for these to function. All of these have been present since birth and are capable of handling simultaneous processing, such as tasting, smelling, and seeing food.

In general, when a method of pattern recognition is formed, learning gains result. The sensory register is quite capacious and has a quick behavioural response of around 1 to 3 seconds. The capacity of the sensory store and working memory is constrained in this concept. relatively little information can be stored in the sensory store for relatively short periods of time. This behaviour is extremely comparable to having a flash used when taking a picture. After the flash fires, it appears to be present for a brief period of time. 

The problem is that it vanishes quickly and leaves no trace, per Sternberg & Sternberg, 2012. Though it still has a little capacity, short term memory can store information for slightly longer periods of time. In accordance with Linden in 2007, "The capacity of STM had first been estimated at "seven plus or minus two" items, per Miller 1956, which reflects the observation from neuropsychological testing that the average digit span of healthy persons is about seven.

However, it became clear that these large numbers of objects can only be remembered if they are categorised into so called chunks using conceptual or perceptual links between different stimuli. Before it leaves the subject's thoughts, it lasts for 5 to 20 seconds. This frequently happens when names of new acquaintances are mentioned. Additionally, information based on images or meaning is retained here, but without rehearsal or repetition, it deteriorates.

 

The capacity of long term memory, however, may be infinite, and its endurance is virtually limitless, per Sternberg & Sternberg, 2012. Although sometimes challenging to obtain, it contains all of the knowledge acquired up to this point. The information could slip one's mind or seem to be on the tip of one's tongue. Jean Piaget offered what is known as the Piaget's Cognitive Development Theory as an alternative perspective on how people process information, per Presnell, 1999.

 

Piaget built his framework around growth and development. He distinguished four stages between various age ranges, each with its own specific cognitive process and sort of knowledge. The sensorimotor from birth to two years, preoperational from two to six years, concrete operational from six to eleven years, and formal operational from eleven years and older phases are the four stages. In the sensorimotor stage, infants and toddlers process information from their senses and react to it with reflexes.

 

Children learn through imitation at the preoperational stage and are unable to understand the perspectives of others. In the concrete operational level, logic based problem solving skills and the capacity to take a variety of circumstances into account are still growing. The formal operational stage is the final one, where preadolescents and adolescents start to comprehend abstract ideas and start to gain the ability to formulate arguments and counter arguments. 

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Adolescence is also marked by a number of adjustments in the biological, cognitive, and social spheres. It is vital to note that the limbic system and prefrontal cortex of the brain both go through significant alterations in the cognitive domain. When performing complex cognitive tasks like planning, coming up with goals and plans, making intuitive decisions, and metacognition or thinking about thinking, the prefrontal cortex of the brain is active. This is in line with the final step of formal operations proposed by Piaget, per McLeod, 2010.

 

Between early adulthood and adolescence, the prefrontal cortex is fully developed. Based on variations in the amounts of neurotransmitters such dopamine and emotions, the limbic system is the area of the brain that modifies reward sensitivity. In summary, our cognitive capacities change as we grow and progress through life. We are better equipped to plan ahead, analyse and understand abstract ideas, and accurately weigh the pros and cons of a given situation as adults as opposed to adolescents or children. 

 

Information processing in computing broadly refers to the application of algorithms to data transformation or the characteristic function of computers. The International Federation for Information Processing or IFIP is a large organisation of computing professionals. It is essentially the same as the terms "data processing" or "computation," but with a broader meaning. 

Health Informatics - A Peek :

The study, processing, and analysis of patient data from many sources and modalities, including electronic health records, results of diagnostic tests, and medical scans, is the goal of the scientific and engineering field of health informatics. Computational methods can be used to solve a huge variety of issues in the health area. The study of the conception, creation, and implementation of computer technologies to enhance healthcare falls under the umbrella of the multidisciplinary area known as "health informatics."

 

The involved disciplines combine the fields of medicine and computing, particularly computer engineering, software engineering, information engineering, bioinformatics, bio-inspired computing, theoretical computer science, information systems, data science, information technology, autonomic computing, and, behaviour informatics. Medical informatics research at academic institutions focuses on developing embedded systems based medical devices and using AI in healthcare applications.

 

In certain nations, applying library science to data administration in hospitals is also referred to as informatics. Clinical informatics is a specialisation of various medical specialties, and "clinical informaticians" are qualified health and social care professionals. According to Jan van Bemmel, medical informatics is the theoretical and applied elements of information processing and communication based on expertise and knowledge gained from processes in medicine and health care.

 

Applications range from image processing that can be utilised in radiography to the 2D Fourier transform, which can be used to remove extraneous data from an X-ray scan. Six high level domains of core competencies have been recognised for clinical informaticians by the Faculty of Clinical Informatics. Information technologies, systems, and the practise of health and wellbeing decision-making, working with data and analytical techniques, and enabling organisational and human change. 

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Let's talk about the resources for supporting practitioners. The following is accomplished by clinical informaticians using their understanding of patient care together with informatics principles, methodologies, and technologies. Analyse the information and knowledge requirements of patients, medical providers, and patient families. Describe, assess, and improve clinical procedures. Construct, put into action, and improve clinical decision support systems. 


Take the initiative or take part in the development, management, management, installation, modification, management, evaluation, and ongoing improvement of clinical information systems. To create health informatics tools that support patient care that is safe, effective, timely, patient-centered, and equitable, clinicians work with other health care and IT professionals. Computer scientists make up a large portion of clinical informaticists. "Why Doctors Hate Their Computers" illustrates the frustrations that many medical professionals have. 

 

Telehealth is the delivery of medical services and knowledge via electronic communication and information technology. It enables remote admissions as well as long-distance patient and clinician interaction, care, guidance, and reminders. Sometimes the term "telemedicine" is used synonymously or in a narrower meaning to refer to remote healthcare services like diagnosis and monitoring. Medical personnel can remotely monitor a patient utilising a variety of technology gadgets through remote monitoring, commonly referred to as self-monitoring or testing.

 

Managing chronic diseases or particular ailments like heart disease, diabetes mellitus, or asthma are the main uses of this technique. These services can offer patients better satisfaction and health outcomes that are comparable to those of conventional in-person patient consultations. They may also be more affordable. E - rehabilitation, also known as telerehabilitation, is the delivery of rehabilitation services over the Internet and telecommunications networks.

 

Clinical evaluation of the patient's functional abilities in his or her environment and clinical therapy are the two broad categories into which the majority of services can be divided. Neuropsychology, speech - language pathology, audiology, occupational therapy, and physical therapy are a few rehabilitation practice areas that have looked into telerehabilitation. People who are unable to travel to a clinic due to a medical condition or the length of the trip can receive therapy through telerehabilitation.

 

Telerehabilitation also enables rehabilitation specialists to conduct a clinical consultation via the internet. Edward H. Shortliffe, an American biomedical informatician, was a pioneer in the use of artificial intelligence to healthcare. This topic focuses on using artificial intelligence and machine - learning algorithms to simulate human cognition in the analysis, interpretation, and comprehension of challenging medical and healthcare data. Artificial intelligence or AI is the capacity of computer algorithms to estimate results based only on input data.

 

Applications of artificial intelligence or AI programmes include the establishment of treatment protocols, medication discovery, personalised medicine, and patient monitoring and care. Clinical decision support systems are the primary area of industrial concentration for the application of AI in the healthcare sector. Machine learning algorithms evolve as more data is gathered and make it possible for responses and solutions to be more reliable. Many businesses are looking into the potential applications of big data in the healthcare sector.

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The "data assessment, storage, management, and analysis technologies" sectors, which are all essential components of the healthcare industry, are where many businesses look into the market opportunities. Examples of significant businesses that have contributed to AI algorithms for application in healthcare include the following. Cleveland Clinic and Memorial Sloan Kettering Cancer Centre are developing IBM's Watson Oncology. IBM also collaborates with Johnson & Johnson and CVS Health. It is on the analysis of scientific publications to uncover new linkages for medication development and on the use of AI in the treatment of chronic diseases. 

 

An initiative called Health Empowerment by Analytics, Learning and Semantics (HEALS), a collaboration between IBM and Rensselaer Polytechnic Institute, was launched in May 2017 to investigate how AI technology may improve healthcare. With the help of the Knight Cancer Institute at Oregon Health & Science University and Microsoft, the Hanover project forecasts the most successful cancer drug treatments for patients by examining medical studies. Analysing the evolution of tumours using medical images and creating programmable cells are two other initiatives.


The UK National Health Service is using Google's DeepMind platform to identify specific health concerns using information gathered from a mobile app. To develop computer vision algorithms to find malignant tissues, a second initiative with the NHS analyses medical photos gathered from NHS patients. Tencent is developing a number of medical services and systems. These include Tencent, WeChat Intelligent Healthcare, and AI Medical Innovation System (AIMIS), a diagnostic medical imaging service powered by artificial intelligence.

 

Doctorwork Lumiata, a firm that utilises AI to identify at-risk patients and provide treatment options, recently received investment from Intel Capital, the venture capital arm of Intel. Deep learning software was created by Kheiron Medical to identify breast cancer in mammography. Qure.ai, developed by Fractal Analytics, focuses on utilising deep learning and AI to advance radiology and expedite the interpretation of diagnostic x-rays. A new generation of neuroprosthetics developed by Neuralink intricately integrate with the brain's thousands of neuronal networks.

 

Their technique enables a precise surgical robot to replace a piece of the patient's skull with a chip, about the size of a quarter, to prevent unintentional harm. Applications that employ AI to provide medical consultation based on user medical histories and general medical knowledge include Babylon Health's GP at Hand, Ada Health, AliHealth Doctor You, KareXpert, and Your.MD. Users enter their symptoms through the app, which compares them against a database of ailments using speech recognition.

 

After that, Babylon provides a suggested course of action while taking the user's medical history into account. Seven business model models have been successfully used by healthcare entrepreneurs to provide AI solutions to the market. Depending on the value created for the intended user ( e.g., patient focus versus healthcare provider and payer focus ) and value capture mechanisms ( e.g., sharing information or bringing stakeholders together ), these archetypes will differ.

 

The "Xiao Man" service robot from IFlytek uses artificial intelligence to recognise its registered users and offer tailored recommendations in the medical field. In the area of medical imaging, it also functions. Companies like UBTECH ( "Cruzr" ) and Softbank Robotics ( "Pepper" ) are also producing robots with a similar design. A WhatsApp chatbot created by the Indian startup Haptik recently responds to inquiries on the deadly coronavirus in India.

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Large IT corporations like Apple, Google, Amazon, and Baidu all have their own AI research sections, as well as millions of dollars set aside for the acquisition of smaller AI-based companies, as the market for AI is continually developing. Machine learning in healthcare is also starting to be used by many automakers in their vehicles. Companies like BMW, GE, Tesla, Toyota, and Volvo are all engaged in new research projects to figure out how to gather information on a driver's vital statistics to make sure they are alert, focused on the road, and not impaired by drugs or their emotions.

 

The COACH project is one example of a computational health informatics initiative. Using informatics techniques, the field of clinical research informatics or CRI seeks to increase the effectiveness of clinical research. In order to streamline ethical approvals and renewals in the US, where the local institutional review board is responsible, CRI is tackling issues such as building data warehouses of health care data that can be used for research, supporting data collection in clinical trials with the use of electronic data capture systems, and maintaining repositories of previous clinical trial data de - identified.

 

CRI is a very new area in informatics, and it has experienced growing pains like any other emerging field. The ability of statisticians and computer system architects to collaborate with the clinical research team in the design of a system and a lack of financing to assist the creation of a new system are some problems CRI encounters. In order to develop a system that is user - friendly for the research team and complies with the system requirements of the computer team, researchers and the informatics team struggle to coordinate plans and ideas.

 

The CRI's development may be hampered by a lack of financing. Many research-based organisations struggle to secure funding for their projects, let alone invest that money in an informatics system that won't increase their revenue or enhance the research's findings, per Embi, 2009. Clinical research informatics heavily relies on the capacity to combine data from many clinical trials. Improvements in secondary use of information gathered in prior human clinical trials were made as a result of initiatives like PhenX and the Patient-Reported Outcomes Measurement Information System.

 

The goal of CDE projects, for instance, is to make it possible for clinical trial designers to use standardised research tools as electronic case report forms. Initiatives that allow researchers who want to re-use this data to download de - identified patient level clinical study data are concurrent efforts to standardising how data is collected. These platforms include Project Data Sphere, dbGaP, ImmPort, and Clinical Study Data Request, as examples. Informatics problems with data formats for sharing results, viz., basic CSV files, FDA a pproved formats, like the CDISC Study Data Tabulation Model, are significant obstacles in the field of clinical research informatics.

 

CRI provides financial support for a variety of clinical research initiatives, including the following benefits. Improved and more effective data acquisition and collection. Enhanced clinical trial enrollment. Optimum protocol development and effective administration. Data storage, transport, processing, and analysis. Patient recruitment and management. Adverse event reporting regulatory compliance. Also, the databases holding data from clinical trials that have been finished for secondary analyses. 

 

Utilising integrated data repositories is among the core components of biomedical and translational research. According to a survey done in 2010, a "integrated data repository" ( IDR ) is a data warehouse that incorporates several clinical data sources to facilitate queries for a variety of research like purposes. Complex systems known as integrated data repositories were created to address a wide range of issues, including identity management, confidentiality protection, the comparability of data from many sources on a semantic and syntactic level, and most importantly convenient and flexible querying.

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Large data sets were produced as a result of the development of clinical informatics, which included the integration of electronic health record data with other data such as genetic data. Clinical data marts, operational data stores ( ODSs ), clinical data warehouses ( CDWs ), and clinical registries are examples of several types of data repositories. operational data stores created for extraction, transferring, and loading prior to building a warehouse or data mart. Although clinical registry collections have been around for a while, their information is often disease - specific and outdated.

 

Clinical data warehouses and clinical data stores are thought to be quick and dependable. Although these sizable integrated archives have had a considerable impact on clinical research, there are still obstacles and challenges. One significant issue is the requirement for institutional review board ( IRB ) ethical approval prior to any research analysis intended for publication. Some research resources are available without IRB permission. 

 

For instance, CDWs that contain patient data who have passed away have been de-identified, and their use is not subject to IRB permission. Data quality is another difficulty. Methods that correct for bias rely on the recording of a whole health record, such as propensity score matching techniques. Finding problems with data quality is made easier by tools that look at the quality of the data, e.g., point out missing data. 

 

Translational Bioinformatics - A Brief :

When the human genome sequence was published in 2000, a relatively new area called translational bioinformatics ( TBI ) emerged. The AMIA website contains a lengthy explanation of TBI that is frequently utilised. TBI could be explained more simply as the collecting of enormous volumes of biological and genetic health-related data and their conversion into therapeutic entities that are uniquely personalised. Currently, the TBI field can be divided into four main themes, which are briefly discussed below. 

 

Electronic health records are gathered into clinical big data, which is then used to innovate. To improve patient outcomes, it is suggested that practice - based medicine and the evidence - based approach currently used in medicine be combined. According to Darren Schutle, CEO of the California-based cognitive computing company Apixio, gathering, merging, and analysing data from diverse medical records will allow for better patient care.

 

Additionally, the combination of comparable profiles can act as a foundation for personalised medicine by indicating what approaches to treatment are effective and ineffective for a given ailment, per Marr, 2016. 

 

Genomic medicine in health care helps to determine whether genes are involved in undiscovered or uncommon conditions or syndromes, genomic data are used. Oncology is the field where the use of genomics is currently most active. During oncological therapy processes, the discovery of cancer's genomic sequencing may help define the causes of drug sensitivity and resistance. Omics for medication repurposing and discovery. Repurposing a medicine is an interesting concept.

 

It enables pharmaceutical companies to sell a drug that has already received FDA approval for one condition or disease to treat a second ailment or disease. The discovery of "molecular signatures in disease and compare those to signatures observed in cells" suggests that a medicine may be able to treat or at least lessen the symptoms of a disease. Individual genomic testing or Direct - to - consumer ( DTC ) genetic testing is provided by a number of businesses in the US. 23andMe is the name of the business that does the majority of testing.

 

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Numerous ethical, legal, and societal issues are raised by the use of genetic testing in healthcare. One of the key challenges is whether the healthcare professionals are prepared to use genomic data provided by the patient while still providing high - quality, unbiased care, despite their extensive understanding of genomics. The recorded instances of integrating such information into a delivery system for health care demonstrated both positive and negative effects on the overall outcomes connected to health care. 

 

Medical signal processing is a crucial area of information engineering in the medical field. It refers to the creation, examination, and application of signals, which can be in the form of sound, visual, electrical, or biological signals, among others. Medical image computing and imaging informatics develop computational and mathematical techniques to address issues relating to the use of medical images in clinical treatment and biological research. These domains aim to derive knowledge or information that is therapeutically pertinent from computer analyses of medical pictures.

 

Image segmentation, image - based physiological modelling, and other methods can all be divided into numerous broad groups. A robot utilised in the medical sciences is known as a medical robot. Among them are surgical robots. In the majority of tele - manipulators, the "effector" on the opposite side is controlled by the surgeon's activators on the opposite side. The following categories of medical robots exist. 

 

Surgical robots that either enable remote surgery where a human surgeon is not physically present with the patient or allow surgical operations to be carried out with greater precision than an unaided human surgeon. Robots for rehabilitation that help and assist the lives of the old, infirm, or those whose body parts are dysfunctional and affect their ability to move. These robots are also utilised for therapy and other related processes like training.
Biorobots are a class of robots created to mimic human and animal cognition. 

 

Remote medical personnel may move, look around, interact, and participate thanks to telepresence robots. Pharmacy automation includes robotic devices that prepare sterile IV admixtures in a hospital pharmacy or dispense oral solids in a retail pharmacy. Robot companions are able to develop emotional connections with their users, keeping them company and alerting them to health issues. Disinfection robot typically uses pulsed ultraviolet light and is capable of quickly disinfecting an entire room. In order to combat the Ebola virus sickness, they are utilised. 

 

With the advent of computers in the early 1950s, computer technology in medicine was first used on a global scale. The first professional organisation for informatics in Germany was founded by Gustav Wagner in 1949. Reference is made to the prehistory, history, and future of medical and health information technology. In France, Germany, Belgium, and the Netherlands, specialised university departments and informatics training programmes got their start in the 1960s. In Poland and the US, medical informatics research units first appeared in the 1970s.

 

Since that time, the U.S. and the European Union have worked to build high - quality health informatics research, teaching, and infrastructure. Medical computing, biomedical computing, medical computer science, medical computer technology, medical electronic data processing, medical automatic data processing, medical information processing, medical information science, medical software engineering, and medical computer technology were some of the early names for health informatics. 

 

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Although the field of health informatics is still developing and far from being a fully developed profession, work by the UK Council of Health Informatics Professions, a voluntary registration body, has identified eight key constituencies within the field: information management, knowledge management, portfolio or program or project management, ICT, education and research, clinical informatics, health records, especially service and business - related, and health informatics service management.

 

Professionals from the NHS, academia, and commercial service and solution suppliers are all represented in these constituencies. The International Medical Informatics Association ( IMIA ) has been the most significant international coordinating organisation since the 1970s. As it relates to information technology in domains that are relevant to health, health informatics law deals with changing and occasionally complex legal issues. It deals with the privacy, moral, and practical concerns that usually come up when using technological tools, information, and media in the delivery of healthcare.

 

All issues including information technology, healthcare, and the exchange of information are covered under health informatics law. It focuses on the conditions under which information and records are shared with other disciplines or sectors that support and improve patient care. Providers must develop security measures to guarantee the security of patient information because many health care systems are working to make patient records more easily accessible to them online. 

 

They must be able to guarantee the privacy, the accuracy, and the security of the data involving people, processes, and technology. It is crucial that this component of their private information will also be safeguarded using encryption because there is a chance that payments will be made through this system. Technology is increasingly being used in healthcare settings, and this trend is predicted to continue.

 

Electronic health records or the EHRs, computerised charting, and other types of health information technology systems were implemented by various healthcare facilities in order to provide patient care. The risk of potential breaches in patients' confidentiality and privacy rose with the rise in popularity of health information technology systems and the expansion of the amount of health information that may be communicated and moved electronically.

 

This worry led to the adoption of stringent regulations by both legislators and specific facilities to guarantee patient confidentiality and privacy. The Health Insurance Portability and Accountability Act of 1996, or HIPAA, is one of the federal laws designed to protect patients' health information like medical records, billing information, treatment plans, etc., and to ensure patients' privacy. Patient authority and autonomy over their own health records are granted by HIPAA. 

 

Additionally, the U.S. Department of Health & Human Services states that this law permits patients to read their own medical records, obtain copies of their own records, ask for any inaccurate information to be corrected, find out who has access to their records, and ask for restrictions on who may and cannot view or access their records. One of the first journals devoted to health informatics was Computers and Biomedical Research, which was first published in 1967.

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Other early publications were the American Medical Association's Computers and Medicine, Gallagher Printing's Journal of Clinical Computing, Plenum Press' Journal of Medical Systems, and Springer - Verlag's MD Computing. The first nursing specific journal, currently known as Computers Informatics Nursing or CIN, was first published by Lippincott in 1984 under the name Journal Computers in Nursing. About 235 informatics journals were available in the National Library of Medicine's or NLM journal catalogue as of September 7, 2016. 

 

According to the Journal Citation Reports for 2018, the Journal of Medical Internet Research with impact factor of 4.945, JMIR mHealth and uHealth with 4.301, and the Journal of the American Medical Informatics Association with 4.292 are the top three journals in the field of medical informatics. Clinical informatics is one of many medical subspecialties in the United States.

 

For instance, the American Board of Pathology offers certification in clinical informatics to pathologists who have completed 24 months of associated training, while the American Board of Preventive Medicine offers clinical informatics certification to practitioners of preventive medicine. The development of MD only physician certification in clinical informatics was announced in October 2011 by the American Board of Medical Specialties or the ABMS, the body in charge of specialty MD certification in the United States.

 

The American Board of Preventive Medicine or ABPM administered the first exam for board certification in the clinical informatics subspecialty in October 2013. 432 candidates passed, becoming the first class of clinical informatics Diplomates in 2014. Physicians who want to earn a clinical informatics board certification might do so through fellowship programmes. Doctors must have graduated from a medical school in the US, Canada, or another country that has been approved by the ABPM. 

 

They must also complete a primary residency programme in a medical specialty like internal medicine or one of the 24 sub specialties recognised by the ABMS and be qualified to get a medical licence in the state where their fellowship programme is located. The 24 - month fellowship programme is divided into informatics rotations, didactic methods, research, and clinical practice in the fellows' primary field of expertise. 

 

Bio Informatics - A Peek :

An interdisciplinary discipline of study called bioinformatics creates techniques and software tools for comprehending biological data, particularly when the data sets are big and complicated. To analyse and understand biological data, bioinformatics employs biology, chemistry, physics, computer science, computer programming, information engineering, mathematics, and statistics. Computational biology is the process of analysing and interpreting data that comes after. 

 

Computer simulation investigations of biological inquiries have utilised computational, statistical, and programming methodologies. They include reusing specialised analysis "pipelines," especially in the area of genomics, where genes and single nucleotide polymorphisms or SNPs are identified. These pipelines are used to learn more about the genetic underpinnings of disease, particular adaptations, desirable traits, especially in agricultural species, or population level variations. 

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Proteomics, a branch of bioinformatics, studies the organising principles of nucleic acid and protein sequences. Large amounts of raw data can be used to derive valuable results via image and signal processing. It supports the sequencing and annotation of genomes and their reported mutations in the field of genetics. Text mining of biological literature and the creation of biological and gene ontologies for the organisation and querying of biological data are examples of bioinformatics.

 

It also contributes to the study of the control and expression of genes and proteins. The comparison, analysis, and interpretation of genetic and genomic data, as well as a broader understanding of the evolutionary elements of molecular biology, are made easier with the use of bioinformatics tools. It aids in the analysis and cataloguing of the biological networks and pathways that are a crucial component of systems biology on a more integrative level. It helps in the simulation and modelling of DNA, RNA, proteins, and biomolecular interactions in structural biology. 

 

Paulien Hogeweg and Ben Hesper initially used the word "bioinformatics" to describe the study of information processes in biotic systems in 1970. According to this definition, bioinformatics is a subfield of biochemistry, which is the study of chemical reactions in biological systems. Analysis of biological data, particularly DNA, RNA, and protein sequences, was a component of computational biology and bioinformatics. Beginning in the middle of the 1990s, the area of bioinformatics grew rapidly, partly due to the Human Genome Project and to the quick development of DNA sequencing technology. 

 

Writing and operating software programmes that use algorithms from graph theory, artificial intelligence, soft computing, data mining, image processing, and computer simulation are required when analysing biological data to provide useful information. Theoretical underpinnings including discrete mathematics, control theory, system theory, information theory, and statistics are only a few examples of those on which the algorithms are dependent. 

 

Since the completion of the Human Genome Project, there has been a significant improvement in both speed and cost. Some labs are now able to sequence over 100,000 billion bases year, and a complete genome may now be read for $1,000 or less. After Frederick Sanger discovered the sequence of insulin in the early 1950s, protein sequences were widely available, and computers became crucial in molecular biology. Manual comparison of several sequences proved to be inefficient.

 

One of the first protein sequence databases was created by Margaret Oakley Dayhoff, a pioneer in the field who also developed techniques for sequence alignment and molecular evolution. Her work was initially published as books. Elvin A. Kabat, who invented biological sequence analysis in 1970 with his extensive quantities of antibody sequences published online with Tai Te Wu between 1980 and 1991, was another early bioinformatics pioneer. 

 

Bacteriophage MS2 and X174 were subjected to new DNA sequencing techniques in the 1970s, and the expanded nucleotide sequences were later parsed using informational and statistical algorithms. These investigations proved the idea that bioinformatics will be illuminating by showing that well known properties, such as the coding segments and the triplet code, are exposed in simple statistical analysis. To create a complete picture of these activities, raw biological data must be combined in order to understand how normal cellular functions are altered in various disease states.

 

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The analysis and interpretation of diverse sorts of data is presently the most urgent challenge in the field of bioinformatics as a result of how the subject has developed. Protein domains, protein structures, and nucleotide and amino acid sequences are also included in this. Within bioinformatics and computational biology, significant subfields include the following. Creating and implementing computer programmes to effectively organise and use different forms of information.


The creation of novel mathematical formulas and statistical techniques to analyse interactions between individuals in massive data sets. There are techniques for, among other things, identifying a gene within a sequence, predicting the structure and / or function of a protein, and grouping protein sequences into families of related sequences. Bioinformatics' main objective is to improve our understanding of biological processes. Its emphasis on creating and using computationally expensive strategies to accomplish this goal distinguishes it from other approaches.

 

Pattern recognition, data mining, machine learning techniques, and visualisation are some examples. Sequence alignment, gene discovery, genome assembly, drug design, drug discovery, protein structure alignment, protein structure prediction, gene expression prediction, protein - protein interaction prediction, genome wide association studies, modelling of evolution, and cell division or mitosis are some of the major research initiatives in this area. 

 

In order to address theoretical and applied issues related to the management and interpretation of biological data, bioinformatics involves developing databases, algorithms, computational and statistical approaches, and theory. Rapid advancements in genomic and other molecular research tools, along with advancements in information technology, have produced a vast volume of molecular biology related knowledge during the past few decades.

 

These mathematical and computer techniques used to comprehend biological processes are referred to as bioinformatics. Mapping and analysing DNA and protein sequences, matching DNA and protein sequences for comparison, and building and displaying 3 - D models of protein structures are typical bioinformatics tasks. Since the 1977 sequencing of the bacteriophage Phage -X174, hundreds of organisms' DNA sequences have been deciphered and compiled in databases.

 

This sequencing data is analysed to identify regulatory sequences, structural motifs, repetitive sequences, RNA genes, genes encoding proteins, and regulatory sequences. When using molecular systematics to create phylogenetic trees, gene comparisons between species or within a species can reveal relationships between species or similarities in protein functions. Manually analysing DNA sequences is becoming unfeasible due to the enormous volume of data.

 

Computer tools like BLAST are frequently used to search sequences, which as of 2008, came from over 260,000 different creatures and had over 190 billion nucleotides. Sequences must first be collected from a data storage bank, such as Genbank, before analysis can begin. DNA sequencing is still a challenging issue since the raw data may be noisy or subject to interference from weak signals. For the many experimental DNA sequencing techniques, base calling algorithms have been created. 

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The majority of DNA sequencing methods yield brief sequence fragments that must be put together to form whole gene or genome sequences. According to the sequencing technology, the shotgun sequencing method, used by The Institute for Genomic Research or TIGR to sequence the first bacterial genome, Haemophilus influenzae, produces the sequences of tens of thousands of tiny DNA fragments, each between 35 and 900 nucleotides long.

 

By correctly aligning the ends of these segments, a genome assembly programme can be used to recreate the entire genome. Shotgun sequencing produces sequence data quickly, but for bigger genomes, assembling the fragments can be challenging. The assembly of the pieces for a genome the size of the human genome may take many days of CPU time on large - memory, multiprocessor systems, and the final assembly typically has many gaps that need to be filled in later.

 

Instead of chain - termination or chemical degradation methods, shotgun sequencing is the preferred approach for almost all genomes sequenced, and genome assembly algorithms are a crucial area of bioinformatics study. Annotation in genomics is the process of identifying the start and stop points of genes and other biological characteristics in a DNA sequence that has been sequenced. The majority of genomes are too big to manually annotate.

 

Genome annotation has emerged as the new bioinformatics bottleneck as the rate of genome sequencing continues to outpace that of genome annotation. The three levels of genome annotation are nucleotide, protein, and process levels. One of the main functions of nucleotide-level annotation is gene discovery. Combining ab initio gene prediction with sequence comparison to expressed sequence databases and other animals can be effective for complicated genomes.

 

The integration of the genome sequence with additional genetic and physical mappings of the genome is also made possible by nucleotide - level annotation. Assigning function to the protein products of the genome is the main goal of protein - level annotation. For this kind of annotation, databases of protein sequences, functional domains, and motifs are utilised. A new genome sequence typically has about 50% of the predicted proteins with no clear function. 

 

Process - level annotation aims to comprehend how genes and their offspring interact with cellular and organismal physiology. The inconsistent use of terminology by various model systems has been a barrier to process-level annotation. The Gene Ontology Consortium is assisting with the solution to this issue. The Institute for Genomic Research, which carried out the first extensive sequencing and study of the genome of a free-living organism, the bacterium Haemophilus influenzae, released the first description of a thorough annotation system in 1995.

 

To determine the first functional assignments, the system recognises the genes encoding all proteins, transfer RNAs, and ribosomal RNAs. The protein - coding genes of Haemophilus influenzae can be found using the GeneMark programme, which is constantly evolving and getting better. The National Human Genome Research Institute created the ENCODE project in order to carry out the objectives that the Human Genome Project left unfinished after it was shut down in 2003. 

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This project is a collaborative data collection of the functional components of the human genome using genomic tiling arrays and next - generation DNA - sequencing technologies, which can automatically generate large amounts of data at a significantly lower cost per base while maintaining the same accuracy or base call error and fidelity or assembly error. While sequence similarity and hence homology is the main factor utilised in genome annotation, other features of sequences can also be used to infer the function of genes.

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Forest Informatics - A Discussion :

In order to manage their daily operations, several government and non government organisations began to employ geographical data. With a focus on the gathering, management, and processing of data, information, and knowledge as well as the incorporation of informatic concepts and theories specifically designed to enhance forest management and forest research, forest informatics is the combined science of forestry and informatics. It shares a connection with information science and library science. 

Information gathering, classification, manipulation, storage, retrieval, and dissemination are the main topics of this interdisciplinary science. Documents that can be read by both humans and machines are considered information in this context. Maps, field data sheets, operating schedules, and long-term asset management plans with narrative text are a few examples of documents that can be read by humans.

Files for geographic information systems or GIS, global positioning systems or GPS and other programmes like spreadsheets and relational database management systems are examples of machine readable documents. Similar to management science, Forest Informatics supports decision-making processes with decision support systems, statistical analysis, mathematical modelling, and algorithms from engineering, operations research, computer science, and artificial intelligence.

 

There are many issues in forestry that can be formulated as optimisation problems, such as generalised assignment problem, travelling salesman problem, knapsack problem, job shop scheduling, and vehicle routing problems. These issues include harvest scheduling, model fitting, optimal sampling, remote sensing, crew assignment, image classification, treatment timing, and log bucking problems. Information processing, information system engineering, decision support systems, geographic information systems, and GPS are all included in this practise.

 

Structure, algorithms, behaviour, and interconnections of natural and artificial systems that store, process, access, and convey information about forested ecosystems are studied in this area of research. "Management Information and Computers in Forestry" is the title of a 1970 article by J. G. Grevatt. The author of the article describes and discusses various management information aspects.

 

Latter are such as the nature of management information and decisions, management information in forestry, the management information system itself, the use of computers, the structure of a computer based system, comparisons between clerical and computer systems, and the impact on the field manager, i.e. operation, expenditure, location, and time. According to the author's analysis, organisations of a critical size may be able to justify using computers to process management data on the basis of cost and better information. 

 

When the essay was written, tools like today's global positioning systems hadn't yet been developed, and computers, databases, and geographic information systems were still in their infancy. Business management database systems were more widely used. The next 30 years saw the development of more potent, more affordable, and smaller computers. Business now routinely uses relational database management systems, standardised computer query languages like SQL, and quicker, more tightly linked networks for integrating data and information.

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At that time, separate systems were also developed for geographic information systems that could run on desktop computers and could be tailored for different needs. In the past ten years, several forestry schools have started to offer specialised fields of study at the university level. In these programmes, students can learn the principles of quantification, modelling, descriptive analysis, and predictive analysis of natural resource attributes that are necessary for effective management of forested ecosystems. 

 

More software "backend" programmes are now available at the small forester practitioner level to forecast expected outcomes for forest development based on treatment recommendations. These are offered by both public and private organisations, such as Assisisoft and the NED system of the U.S. Forestry Service. Since the NED system is free, American consulting foresters frequently use its basic features, however only a small number of them make use of the software's full modelling capabilities. 

 

The processing power accessible in the woods is now far higher than it was just a few years ago when PocketPC-based systems were the norm thanks to the widespread use of smartphones and tablets. Microsoft has stated that the PocketPC platform would be discontinued in March 2013. Foresters and wood cruisers can now use new apps, like Forest Metrix, to use smart devices for data collection that will subsequently be exported into more advanced software. 

 

Software that has been created expressly to analyse management choices for forested ecosystems has been employed in a number of large-scale planning initiatives. One application framework for knowledge based decision support of ecological analysis and planning is the Ecosystem Management Decision Support or EMDS system. As seen by the growth of ecological extensions for statistical programmes like R, open source software solutions have also gained in popularity.

 

The book about using R for forest analytics by Andrew Robinson and Jeff D. Hamann is a recent example. 2011 was proclaimed International Year of Forests by the UN in 2006. A suite of software agents, reports, maps, and data feeds have all been created by Forest Informatics, Inc. To anticipate potential strategic, tactical, and operational solutions for forest managers, the application makes use of an intelligent agent architecture. 

 

Ecological Informatics - A Review :

The study of information in ecology and environmental research is known as eco informatics, or ecological informatics. In order to define items and natural processes using language that is accessible to both humans and computers, it merges environmental and information sciences. However, there are other viewpoints on what exactly qualifies as eco informatics, and this is a field of ecology that is fast evolving. A few definitions have been put out, most of which focus on the development of tools for accessing and analysing data from natural systems.

 

However, the objectives of ecoinformatics are unquestionably more expansive than simply creating metadata standards for use in dataset documentation. By creating novel algorithms that allow various environmental datasets to be merged to test ecological hypotheses and by creating methods for accessing and integrating databases of environmental information, ecoinformatics strives to facilitate environmental study and management. The idea of ecosystem services is connected to ecoinformatics. 

 

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Ecoinformatics describes the semantics of knowledge about natural systems. Because of this, a significant portion of current ecoinformatics research connects to the area of computer science known as knowledge representation, and ongoing ecoinformatics efforts are creating connections to initiatives like the Semantic Web. The current efforts to efficiently manage, distribute, and reuse ecological data show how important it is for fields like ecoinformatics to lay the groundwork for efficient management of ecological data. 

 

National Science Foundation Datanet programmes, DataONE, Data Conservancy, and Artificial Intelligence for Environment & Sustainability are a few examples of these endeavours. The use of informatics methods to information on biodiversity, such as taxonomy, biogeography, or ecology, is known as biodiversity informatics. It is described as the use of information technology to organise, explore, analyse, and understand primary data about life, particularly at the species level organisation.

 

Modern computer technologies can produce novel views and analyses of current data as well as future scenario prediction. Although the term "biodiversity informatics" wasn't first used until approximately 1992, it has since proven effective in a wide range of research projects and applications, including the creation of taxonomic databases and geographic information systems. The term "bioinformatics," which refers to the computerised management of data in the specialised field of molecular biology, contrasts with biodiversity informatics. 

 

The use of information technology methods to the issues of organising, retrieving, visualising, and analysing primary biodiversity data is known as biodiversity informatics, distinct from but related to bioinformatics. Names, observations, and records of specimens, as well as genetic and morphological information related to a specimen, make up primary biodiversity data. The management of data from unidentified taxa, such as that generated by environmental sampling and sequencing of mixed field samples, may also fall under the purview of biodiversity informatics. 

 

The term "biodiversity informatics" also refers to computational issues that are specific to names of biological entities, such as the creation of algorithms to deal with different representations of identifiers like species names and authorities, the various classification schemes that these entities may fall under depending on the preferences of various experts in the field, and the syntax and semantics used to describe the information in taxonomic data. 

 

It can be said that the field of biodiversity informatics began with the creation of the first computerised taxonomic databases in the early 1970s and advanced through the subsequent development of distributed search tools in the late 1990s, including the Species Analyst from Kansas University, the North American Biodiversity Information Network NABIN, CONABIO in Mexico, INBio in Costa Rica, and others, as well as the launch of the Global Biodiversity Information Facility. 

 

The American journal Science published a special issue on "Bioinformatics for Biodiversity" in September 2000. The journal Biodiversity Informatics started publishing in 2004. Throughout the 2000s, several international conferences brought together practitioners in biodiversity informatics, including the London e-  Biosphere conference in June 2009. Biodiversity informatics is also covered in a November 2009 supplement to the journal BMC Bioinformatics. 

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According to correspondence reprinted by Walter Berendsohn, John Whiting first used the term "Biodiversity Informatics" in 1992 to describe the work of the Canadian Biodiversity Informatics Consortium, an organisation tasked with combining fundamental biodiversity data with environmental economics and geospatial data in the form of GPS and GIS. As a result, it no longer seems to be obligated to be connected to the GPS/GIS world or to be involved in the computerised maintenance of any biodiversity information.

Environmental Informatics - An Abstract :

Environmental informatics is the use of information science to the study of the environment. As a result, it offers the information processing and communication infrastructure to the multidisciplinary field of environmental sciences, aiming at data, information, and knowledge integration, the application of computational intelligence to environmental data, as well as the identification of environmental impacts of information technology.

 

The generation, collecting, storage, processing, modelling, interpretation, display, and transmission of data and information are all aspects of environmental informatics, according to the UK Natural Environment Research Council. Environmental informatics, according to Kostas Karatzas, is the "development of a new 'knowledge-paradigm' towards meeting environmental management needs." 

 

Environmental informatics, according to Karatzas, "is an integrator of science, methods, and techniques and not just the result of using information and software technology methods, tools, for serving environmental engineering needs." Early 1990 saw the emergence of environmental informatics in Central Europe. The current efforts to efficiently manage, distribute, and reuse ecological and environmental data show how important it is for disciplines like environmental informatics and ecoinformatics to lay the groundwork for efficiently handling ecological data.

 

Legal Informatics - An Abstract :

National Science Foundation Datanet programmes, DataONE, and Data Conservancy are a few examples of these programmes. Information science includes a field called legal informatics. Informatics, according to the American Library Association, is "the study of the structure and properties of information, as well as the application of technology to the organisation, storage, retrieval, and dissemination of information."

 

Law related organisations, such as law firms, courts, and law schools, as well as the consumers of information and information technologies inside these organisations, are all involved in legal informatics, which is the use of informatics within the context of the legal environment. The use of information technology in the application of law, such as the use of subpoenas for information found in email, search queries, and social networks, gives rise to policy challenges in legal informatics.

 

There are many approaches to policy addressing legal informatics issues around the world. For instance, European nations frequently demand the anonymization or erasure of data in order to prevent its use for discovery. The increased adoption of cloud computing has many advantages for providing legal services. By charging users a per-use or subscription fee, legitimate service providers can use the Software as a Service model to profit. Compared to typical bespoke services, this model has a number of advantages. 

 

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Scalability is substantially higher with software as a service. With traditional custom arrangements, each additional client requires an attorney to devote more of a finite resource, their time. A legal service provider can design a product once using software as a service and then use a considerably less constrained resource or cloud computing power to serve each successive customer. By doing common duties, software as a service can be used to supplement traditional customised services, freeing up an attorney to focus on unique work. 

 

Because the legal service provider does not have to be present at the same time as the customer, software as a service can be given more easily. The attorney - client relationship is further complicated by software as a service, which may have an impact on the attorney - client privilege. It is simple to define when the attorney-client privilege applies and when it does not using the conventional delivery approach. 

 

The relationship between a client and their attorney may be moderated by other actors or automated procedures in more complicated forms of legal service delivery, making it challenging to determine which conversations should remain private. The relationship between a client and their lawyer may be moderated by other actors or automated procedures in more sophisticated models of legal service delivery, making it challenging to determine which communications should be treated as privileged information.

 

Platforms for online dispute resolution that use optimisation algorithms and blind bidding leverage artificial intelligence. The modelling of the legal ontology is "an explicit, formal, and general specification of a conceptualization of properties of and relations between objects in a given domain". It also frequently uses artificial intelligence. A subfield of artificial intelligence or AI, artificial intelligence and law, or AI and law, focuses on innovative research on and applications of AI to challenges in legal informatics. 

 

It is also concerned with making a contribution going the other way, exporting tools and methods created for legal issues to all of AI. For instance, theories of legal decision making, particularly models of argumentation, have influenced knowledge representation and reasoning.  Norm based social organisation models have influenced multi agent systems. Case based reasoning has benefited from the analysis of legal precedents. The need to store and retrieve sizable amounts of textual data has influenced conceptual information retrieval and intelligent databases. 

 

Although Loevinger, Allen, and Mehl predicted some of the concepts that would be crucial in AI and Law, Buchanan and Headrick are generally regarded as having made the first significant proposal for integrating AI methods with the legal system.  Early work from this time period includes Ronald Stamper's LEGOL project in the UK and Thorne McCarty's significant TAXMAN project in the US.

 

Early 1980s landmarks include work by Anne Gardner on contract law, Carole Hafner on conceptual retrieval, Rissland on legal hypotheticals, and Imperial College London's work on representing laws using executable logic programmes. Early academic gatherings included a one - time event in Swansea, a run of conferences by IDG in Florence, and Charles Walter's workshops at the University of Houston in 1984 and 1985. The International Conference on AI and Law or ICAIL, which is held every two years, was established in 1987.

 

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It was because of this conference that the International Association for Artificial Intelligence and Law or IAAIL, which was established in order to plan and host successive ICAILs, came to be recognised as the primary forum for publishing and the development of ideas within AI and law. The Artificial Intelligence and Law Journal was established as a result, and it was first released in 1992. The Jurix Foundation for Legal Knowledge Based Systems started hosting the annual JURIX conferences in Europe in 1988.

 

JURIX was founded with the intention of uniting Dutch speaking, Dutch and Flemish researchers, but it swiftly evolved into a worldwide conference with a strong European focus that has been conducted outside of Dutch speaking nations on a regular basis since 2002. The Japanese Society for Artificial Intelligence has been hosting the JURISIN workshops in Japan since 2007. 

 

By allowing machine driven processes to work on the syntactic and semantic elements of digital parliamentary, judicial, and legislative documents, the interoperable legal documents standard Akoma Ntoso facilitates the creation of high-quality information resources and serves as the foundation for AI tools. Through machine assisted writing and machine assisted legal analysis, it seeks to significantly improve the performance, accountability, quality, and openness of parliamentary and legislative activities based on best practises and guidelines.

 

It serves as the foundation for a diverse yet cooperative ecosystem that allows these tools to function and interact, as well as for upcoming applications and use cases based on digital law or rule representation. This ecosystem is embedded in the semantic web environment. Today, a wide range of problems are covered by AI and law, primarily as structured frameworks for legal analysis, argumentation and decision making computational models, models for computational evidential reasoning and utilising multi-agent systems to reason legally.

 

Additionally, we have executable models of legislation, automatic categorization and summary of legal texts, data extraction software for legal databases and texts, e - discovery and other legal applications using machine learning and data mining, retrieval of legal information based on concepts or models, automating minor and routine legal activities with lawbots, using machine learning and artificial intelligence, risk assessment, price and timeline forecasts are made for legal proceedings. 

 

AI and law have employed formal models of legal texts and legal reasoning to make issues clearer, provide a more precise understanding, and serve as a foundation for implementations. Propositional and predicate calculi, deontic, temporal, and non-monotonic logics, state transition diagrams, and other formalisms have all been employed. Along with a full list of references, Prakken and Sartor provide a thorough and authoritative analysis of the application of logic and argumentation in AI and law. 

 

Getting rid of ambiguity is one of formal models' key functions. Legislation, in fact, is rife with ambiguity. Because it is written in normal language without brackets, the meaning of connectives like "and" and "or" can be ambiguous. Legal draughtsmen never write "if and only if," even though this is frequently what they mean when they use the word "if." "Unless" is similarly open to multiple meanings. Layman Allen promoted the use of propositional logic to clear up such grammatical ambiguities in a number of articles.

 

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This is likely the first instance of logic being used to represent law in the field of AI and law. A large area of research on AI and law, beginning with Thorne McCarty's TAXMAN and Ronald Stamper's LEGOL, focused on creating executable models of laws in the late 1970s and throughout the 1980s. The micro PLANNER programming language was used to create TAXMAN, which was utilised to replicate both the majority and minority arguments in the US Tax law case of Eisner v. Macomber. 

 

With the use of a condition action rule language similar to that used for expert systems, LEGOL was used to formalise the laws and ordinances that govern an organisation. The TAXMAN and LEGOL languages were rule-based, executable, and lacked a clear logical meaning. However, Sergot et al.'s formalisation of a significant chunk of the British Nationality Act demonstrated how closely legal documents' natural language resembles the Horn clause subset of first order predicate calculus.

 

It also revealed the necessity of expanding the use of Horn clauses by adding negative conditions to indicate limitations and regulations. The extended Horn clauses that arise can be run as logic programmes. Later work on more complex applications, like that on Supplementary Benefits, demonstrated the need for additional extensions to logic programmes to handle issues like multiple cross references, counterfactuals, deeming provisions, amendments, and extremely technical concepts, like contribution conditions.

 

Cross - reference issues were recommended to be solved using hierarchical representations, and verification and frequent amendment issues were suggested to be solved using so called isomorphic representations. As the 1990s progressed, this line of research was largely included into the creation of formalisations of domain conceptualizations, or so called ontologies, which gained popularity in AI as a result of Gruber's work. Early instances of AI and law include Visser and van Kralingen's frame based ontologies and Valente's functional ontology. 

 

Since then, legal ontologies have become a frequent topic of workshops at conferences on artificial intelligence and law. There are numerous examples, ranging from general top-level and core ontologies to extremely detailed representations of particular pieces of legislation. It is hardly unexpected that deontic logics have been tried as the formal foundation for models of legislation as law consists of collections of rules. These, however, have not been widely used as the foundation for expert systems.

 

This is possibly because expert systems are meant to enforce the norms, whereas deontic logic only becomes truly interesting when we need to take into account norm violations. Law related directed duties, in which a duty is owed to a specific named person, are particularly important since they frequently form the basis of legal actions. There is also some intriguing research that explores normative stances by merging deontic and action logics. Norms have been modelled in the context of multi-agent systems using state transition diagrams.

 

The norms as defined are frequently regulated, i.e., cannot be violated, especially in the setting of electronic institutions, but in other systems infractions are also managed, providing a more accurate depiction of genuine norms. See Modgil et al. for a great illustration of this strategy. Law frequently involves time related issues, including those relating to the law's initiation as well as those relating to its content, such as time limits and deadlines.

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There have been some attempts to model these temporal logics using both temporal logics like defeasible temporal logic and computational formalisms like the Event Calculus. It is important to keep in mind that law is intrinsically non- monotonic when considering the use of logic to model it, as evidenced by the rights of appeal recognised by all legal systems and the way that legal interpretations evolve through time. Additionally, there are many exceptions when laws are being written, and precedents are both followed and overturned when laws are being applied. 

 

Negation as failure is frequently utilised in logic programming methodologies to manage non-monotonicity, although particular non-monotonic logics, like defeasible logic, have also been employed. However, following the emergence of abstract argumentation, monotonic logic argumentation is increasingly used to address these issues as opposed to non - monotonic logics. There are both proprietary and academic quantitative legal prediction models.

 

In the shape of the Supreme Court forecasting project, one of the earliest instances of a functional quantitative legal prediction model was demonstrated. The Supreme Court forecasting programme made an effort to project the outcomes of every case heard during the court's 2002 term. In contrast to experts who accurately predicted just 59.1% of cases, the model anticipated 75% of cases. A 2012 model that forecasted the outcome of class action litigation involving Federal Securities is another illustration of an academic quantitative legal prediction model.

 

Algorithmic models are being developed by some academics and legal technology businesses in an effort to forecast case outcomes. An important aspect of this work is a better case assessment for litigation funding. A proposal has been presented to provide a standard dataset that would enable comparisons amongst systems in order to better assess the calibre of case outcome prediction systems. Progress is still being made on technologies that are focused on both litigation and transactions within the conceptual domain of practise concerns.

 

Predictive coding in particular has the potential to significantly increase efficiency in the legal profession. Although most of the time predictive coding has been utilised in litigation, it is now starting to be used in transaction practise to enhance document analysis in mergers and acquisitions. The significance of legal informatics in the field of transactional law is demonstrated by additional developments such as XML coding in transaction contracts and increasingly sophisticated document preparation tools. 

 

Machines are used to evaluate documents in current applications of AI in the legal sector, especially when a high level of completeness and confidence in the quality of document analysis is required, as in cases of litigation and when due diligence play a role. Predictive coding uses tiny samples to cross - reference related items and eliminate irrelevant documents so that attorneys can concentrate on the main documents that are genuinely important. 

 

It also generates statistically validated results that are equivalent to or superior to human review in terms of accuracy and, most noticeably, rate. New models for the provision of legal services have been made possible by developments in technology and legal informatics. Legal services have historically been a "bespoke" product made specifically for each customer by a qualified practitioner. Parts of these services will transition from being bespoke to being standardised, systematised, packaged, and commoditized in order to operate more effectively.

 

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Different technology and knowledge systems will need to be embraced in order to advance to the next stage. Legal services are now available to people and small to medium - sized businesses because to the growth of the Internet and legal technology and informatics. Legal informatics is a tool that corporate legal departments can utilise to prepare, customise, and manage papers as well as manage patent portfolios. 

 

Cognitive Informatics - An Overview :

Cognitive science is an interdisciplinary field that integrates knowledge from linguistics, psychology, neurology, philosophy, computer science, artificial intelligence, and anthropology to explore the mind and its functions. It explores cognition in a wide sense, including its nature, tasks, and functions. Cognitive scientists focus on how neural systems represent, process, and alter information as they research intelligence and behaviour. Language, perception, memory, attention, reasoning, and emotion are among the mental processes that cognitive scientists study.

 

To do so, they draw on linguistics, psychology, artificial intelligence, philosophy, neuroscience, and anthropology. Cognitive science analysis often covers a wide range of organisational levels, including learning, decision-making, logic, and planning, as well as neural circuitry and modular brain organisation. The idea that representational structures in the mind and computational processes that operate on those structures are the best ways to understand thinking is one of the cornerstones of cognitive research. 

 

Understanding and formulating the fundamentals of intelligence is the aim of cognitive research, with the expectation that doing so will help us better comprehend the mind and learning. The cognitive revolution often referred to as the conceptual forerunner of the cognitive sciences began in the 1950s. The cognitive revolution, an intellectual movement that gave birth to the cognitive sciences, began in the 1950s. Plato's Meno and Aristotle's De Anima are two examples of early Greek philosophical texts that can be used to trace the origins of cognitive science.

 

Modern philosophers like Descartes, David Hume, Immanuel Kant, Benedict de Spinoza, Nicolas Malebranche, Pierre Cabanis, Leibniz, and John Locke rejected scholasticism while using entirely different methods and fundamental ideas than cognitive scientists do today. The early cyberneticists of the 1930s and 1940s, such Warren McCulloch and Walter Pitts, who aimed to comprehend the organising principles of the mind, are credited with developing the present culture of cognitive science. 

 

The first iterations of what are now referred to as artificial neural networks, the computational models that were modelled by the organisation of biological brain networks, were created by McCulloch and Pitts. Early work on computation theory and the digital computer in the 1940s and 1950s was another forerunner. These discoveries were largely due to the work of Kurt Gödel, Alonzo Church, Alan Turing, and John von Neumann.

 

As a metaphor for the mind and as a research instrument, the contemporary computer, also known as the Von Neumann machine, would be crucial to cognitive science. At J.C.R.'s MIT Sloan School of Management, the first cognitive science experiment ever conducted at a university, took place. Licklider is a psychology department employee who uses computer memory in his investigations as a proxy for human cognition. Noam Chomsky wrote a harsh review of B. F. Skinner's book Verbal Behaviour in 1959, which was then published.

 

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The behaviourist paradigm developed by Skinner at the time dominated psychology in the US. Without proposing internal representations, the majority of psychologists concentrated on the functional relationships between stimuli and reaction. Chomsky maintained that generative grammar, which not only identified internal representations but also described their underlying order, was necessary to understand language. 

 

Christopher Longuet Higgins first used the phrase cognitive science in his 1973 commentary on the Lighthill report, which focused on the state of artificial intelligence research at the time. The Cognitive Science Society and the journal Cognitive Science were both established in the same decade. In 1979, the University of California, San Diego hosted the founding meeting of the Cognitive Science Society, which led to cognitive science gaining recognition on a global scale.

 

Neil Stillings established Hampshire College's first undergraduate Cognitive Science programme in 1972. Vassar College was the first college in the world to offer an undergraduate degree in cognitive science in 1982, thanks to Professor Stillings' support. At the University of California, San Diego, the first Cognitive Science Department in the world was established in 1986. As the availability of computers grew in the 1970s and early 1980s, so did the field of artificial intelligence study. 

 

In an effort to more fully understand human thought and to develop artificial minds, researchers like Marvin Minsky wrote computer programmes in languages like LISP that attempted to formally characterise the steps that people took when making decisions and solving problems, for example. "Symbolic AI" is the term for this method. The study programme on symbolic AI's limitations eventually became clear. For example, listing all of human knowledge in a way that a symbolic computer programme might use seems impractical.

 

The development of neural networks and connectionism as a research paradigm occurred in the late 80s and early 90s. According to this theory, which is frequently credited to James McClelland and David Rumelhart, the mind could be described as a collection of intricate associations that are shown as a layered network. Some phenomena, according to critics, are best represented by symbolic models, while connectionist models are frequently so intricate as to have little capacity for explanation.

 

Symbolic and connectionist models have recently been integrated, allowing for the use of both types of explanation. While connectionism and symbolic approaches have both been successful in testing various theories and exploring new avenues for understanding cognition and lower level brain functions, neither is biologically plausible, and as a result, both lack the neuroscientific plausibility needed to be taken seriously. 

 

Connectionism has shown to be an effective alternative to purely domain - specific or domain - general methodologies for computationally examining how cognition develops and takes place in the human brain. For instance, researchers Jeff Elman, Liz Bates, and Annette Karmiloff - Smith have proposed that the brain's neural networks develop as a result of their dynamic interaction with external input. The idea that one can never fully comprehend the mind or brain by focusing on a single level is a fundamental premise of cognitive science.

 

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The challenge of recalling a phone number afterwards might serve as an illustration. Studying behaviour through direct observation, or naturalistic observation, is one way to comprehend this process. After some time has passed, a person may be given a phone number and asked to recall it. The correctness of the response could then be assessed. Studying the firing of individual neurons while a person is trying to remember the phone number is another method for assessing cognitive aptitude. 

 

Both of these trials together wouldn't be able to adequately explain how remembering a phone number works. It is impossible to know how a specific neuron firing translates into the observed behaviour, even if technology existed to map out every neuron in the brain in real-time and it were known when each neuron fired. Therefore, it is crucial to comprehend how these two levels relate to one another.

 

The emerging sciences of the mind, according to Francisco Varela in The Embodied Mind: Cognitive Science and Human Experience, "need to broaden their horizon to encompass both lived human experience and the possibilities for transformation inherent in human experience." A functional level explanation of the process can supply this, according to the traditional cognitivist viewpoint. Understanding the mental processes that give rise to a certain behaviour is improved by investigating a phenomenon from various angles.

 

Marr provided a well-known breakdown of the three levels of analysis, viz., the aims of the computation are specified by the computational theory. The hardware implementation, or how an algorithm and representation could be physically realised, as well as representation and algorithms, which provide a representation of the inputs and outputs and the algorithms that convert one into the other. The subjects of computer science, linguistics, philosophy of mind, psychology, neuroscience, anthropology, and biology are all represented in the interdisciplinary field of cognitive science.

 

Similar to other sciences, cognitive scientists collaborate in the hopes of comprehending the mind and how it interacts with the outside environment. The area sees itself as compatible with the physical sciences and uses simulation or modelling in addition to the scientific method, frequently comparing the results of simulations with features of human cognition. Similar to the subject of psychology, there is significant uncertainty over the existence of a single cognitive science, which has caused some scholars to prefer the plural form "cognitive sciences." 

 

A functionalist view of the mind, which holds that mental states and processes should be explained by their function, or what they do, is held by the majority of cognitive scientists, but not all of them. Functionalism's multiple realizability argument holds that even non-human systems like robots and computers can be said to possess cognition. "Any kind of mental operation or structure that can be studied in precise terms" is what is meant by the term "cognitive" in the context of "cognitive science", per Lakoff and Johnson, 1999.

 

This conception is quite broad and shouldn't be mistaken with the way the term "cognitive" is employed in some analytic philosophical traditions, where it primarily refers to formal rules and truth-conditional semantics. The OED's earliest entries for the word "cognitive" define it as roughly "pertaining to the action or process of knowing". The word was once employed in connection with debates of Platonic ideas of knowing, as the first entry from 1586 demonstrates. 

 

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However, it is likely that the majority of cognitive scientists do not think their area is the study of something as certain as the knowledge sought by Plato. A broad range of subjects related to cognition are covered by the field of cognitive science. It should be noted, nevertheless, that not every subject that might be pertinent to the nature and functioning of brains has historically been the focus of cognitive science.

 

The de - emphasis or avoidance of social and cultural elements, embodiment, emotion, consciousness, animal cognition, and comparative and evolutionary psychologies is a common practise among classical cognitivists. Internal states, such as affects and emotions, consciousness, and covert attention, however, become reachable once again with the demise of behaviourism. For instance, situated and embodied cognition theories consider how the body plays a part in cognition as well as the current condition of the environment. 

 

The hallmark of psychological theory had shifted from observable behaviour to the modelling or recording of mental states as a result of the renewed emphasis on information processing. Some of the key areas of cognitive science are listed here. This list is not all - inclusive. For an overview of the numerous facets of the area, see overview of cognitive science topics. In artificial intelligence or AI, cognitive machine phenomena are studied. The incorporation of elements of human intellect in computers is one of the practical aims of AI.

 

Computers are frequently employed as a tool for the research of cognitive phenomena. Simulations are used in computational modelling to investigate potential structures for human intelligence. There is some discussion in the field over the appropriate way to conceptualise the mind, as a collection of higher - level structures like symbols, strategies, plans, and laws, or as a vast array of little but individually weak elements ( i.e. neurons ). In contrast to the latter, which places a stronger emphasis on symbolic artificial intelligence, the former uses connectionism to investigate the mind. 

 

Whether it is possible to accurately imitate a human brain on a computer without accurately recreating the neurons that make up a human brain is one way to look at the problem. The selection of crucial information is what attention is. The human mind must have a method of selecting which of the millions of impulses it receives to process. One can only shine a spotlight on a specific set of information when paying attention, which is commonly compared to a spotlight.

 

Studies on in - attentional blindness, per Mack and Rock, 1998, as well as the dichotic listening task, per Cherry, 1957, are experiments that lend support to this metaphor. In the dichotic listening test, participants are instructed to concentrate only on one of two messages that are presented to them, one in each ear. When questioned about the substance of the unattended message at the conclusion of the trial, subjects are unable to do so. Cognitive science methods that emphasise embodied cognition place a strong emphasis on how the body and environment affect cognition.

 

This encompasses both neuronal and extra-neural body functions, as well as a variety of other elements, such as affective and emotional processes, posture, motor control, proprioception, and kinaesthesis, as well as autonomic functions like respiration and heartbeat and the enteric gut flora. Accounts of how the body interacts with or is bound to social and physical contexts are also included.

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A wide range of perspectives on the relationship between the brain, body, and environment are included in 4E, viz., embodied, embedded, extended, and enactive cognitions, from causal embeddedness to more assertive claims about how the mind extends to include tools and instruments, as well as the importance of social interactions, action - oriented processes, and affordances. 

 

The range of 4E theories includes those that are more in line with conventional cognitivism, so - called "weak" embodied cognition, and stronger extended and enactive variants that are occasionally referred to as radical embodied cognitive science. Language acquisition and comprehension are highly difficult processes. All humans are capable of learning language efficiently in normal circumstances within the first few years of life. Finding the character that language must have in the abstract for this kind of learning is a fundamental driving force in theoretical linguistics.

 

The following are some of the main research questions in the investigation of language processing in the brain. How much linguistic ability is innate versus how much is acquired ? Why is learning a second language more challenging for adults than learning a first language for babies ? How do people comprehend complex sentences ? The research of speech sound patterns as well as the meaning of individual words and complete sentences are all included in the study of language processing.

 

Orthography, phonetics, phonology, morphology, syntax, semantics, and pragmatics are common categories used to categorise language processing in linguistics. Each of these elements and their interactions can be investigated to learn more about a variety of linguistic features. Linguistics and cognitive science share a tight relationship in the study of language processing. The humanities, which also include studies of history, art, and literature, have always included linguistics as a field of study. 

 

The fundamental issues in the study of language knowledge and usage as a cognitive phenomenon during the past fifty years or so have been how it can be learned and employed, as well as what exactly it entails. While humans appear to generate sentences in ways that are controlled by very complicated systems, linguists have shown that despite this, people are astonishingly oblivious of the laws that regulate their own speech. Thus, if there are norms at all, linguists must use indirect ways to ascertain what they might be.

 

In any case, if there are norms for speech, they don't seem to be transparent to conscious thought. Our processes for acquiring knowledge and information over time are learning and development. Depending on how knowledge is defined, infants are born with little to no information, but they quickly pick up the skills necessary to walk, utilise language, and recognise people and objects. The goal of learning and development research is to identify the possible mechanisms behind these processes.

 

How much of a person's cognitive talents are innate or learned is a crucial question in the study of cognitive development. This is frequently explained in terms of the nature vs. nurture controversy. The nativist viewpoint emphasises that an organism's genetic endowment determines some characteristics that are inherent to it. On the other hand, the empiricist viewpoint emphasises how some skills are picked up via experience.

 

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There is still a lot of discussion over how genetic information could influence cognitive development, even though it is obvious that a kid needs both genetic and environmental input for appropriate development. Some, like Steven Pinker, have argued that the genes must contain specific information containing universal grammatical rules in the area of language acquisition, while others, like Jeffrey Elman and colleagues in Rethinking Innateness, have argued that Pinker's claims are biologically implausible.

 

They contend that while genes influence a learning system's architecture, precise "facts" about how grammar functions can only be learnt through experience. We can store information in memory for subsequent retrieval. It's common to think of memory as having both long-term and short-term stores. We can store knowledge in our long-term memory for extended periods of time ( days, weeks, or years ). The practical upper limit of long-term memory storage is yet unknown.

 

We can store knowledge for brief periods of time ( seconds or minutes ) thanks to our short-term memory. Declarative and procedural forms of memory are also frequently categorised. Declarative memory refers to human memory for facts and specific knowledge, specific meanings, and specific experiences ( for example, "Are apples food?" or "What did I eat for breakfast four days ago?" ). It is divided into subsets of semantic and episodic forms of memory. 

 

Implicit knowledge or memory is typically used to describe procedural memory, which enables humans to recall actions and motor sequences (such as how to ride a bicycle). Similar to psychologists, cognitive scientists also investigate memory, although they tend to concentrate more on how memory affects cognitive processes and how memory and cognition are related to one another. What mental processes may a person go through to recover a forgotten memory serve as an illustration of this?

 

Or, what distinguishes the cognitive processes of recognition ( seeing signs of something before recalling it, or memory in context ), and recall (retrieving a memory, as in "fill-in-the-blank" ? The capacity to process information acquired through the senses is known as perception. The two primary senses that help us perceive our surroundings are sight and sound. For instance, the following issues are covered in some visual perception research questions. How do we distinguish objects ?  

 

Even if we only ever view a small portion of the visual environment at once, why do we nonetheless perceive it as continuous ? Analysing how individuals interpret optical illusions is one method for studying visual perception. A Necker cube image on the right serves as an illustration of a bistable percept, meaning that the cube can be seen from two different perspectives. Perception also includes the study of olfactory, gustatory, and haptic or tactile inputs. 

 

To refer to a system's output, action is performed. This is done in humans by means of motor reactions. Action includes verbal production, planning and moving in space, and performing intricate physical actions. The awareness of internal experiences is what is meant by consciousness. This supports the mind's capacity to see or feel a sense of self. The methods used to explore cognitive science are very diverse.

 

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Research frequently crosses numerous fields of study in this highly multidisciplinary discipline, using systems theory, computer science, psychology, and neuroscience research approaches. One must research behaviour in order to obtain a description of what intelligent behaviour entails. The research in cognitive psychology and psychophysics is closely related to this kind of study. One can learn a little about how different stimuli are processed by evaluating behavioural reactions to those stimuli.

 

The new applications of behavioural measurement in psychology, including behavioural traces, behavioural observations, and behavioural choice, were examined by Lewandowski & Strohmetz (2009). The term "behavioural traces" refers to bits of evidence, such as trash in a parking lot or readings on an electric metre, that show behaviour occurred but the actor is not there. Direct observation of the actor performing the behaviour is required for behavioural observations, such as when observing how closely someone sits next to another person. 

 

A person makes a behavioural choice while deciding between two or more possibilities, such as voting behaviour or the choice of how to punish another participant. Response time is the amount of time that passes between the presentation of a stimulus and the proper reaction can reveal similarities and differences between two cognitive processes, as well as certain characteristics of those processes.

 

For instance, it is obvious that the cognitive process of searching includes serial rather than parallel processing if the reaction times in a search task change proportionally with the quantity of elements. physical reactions in the mind. An established psychological method called psychophysical experiments has been used by cognitive psychology. They often require evaluating some sort of physical characteristic, like how loud a sound is. Contrary to actual physical measures, the correlation of subjective scales between individuals can reveal cognitive or sensory biases.

 

Examples include threshold differences for colours, tones, textures, etc. and sameness judgements for those elements. Monitoring the eyes. Several cognitive functions, most notably language and visual perception, are studied using this paradigm. An individual's focus of attention is related to the fixation point of their eyes. Thus, we can analyse what information is being processed at a given time by observing eye movements. We can examine cognitive processes on incredibly small time scales because to eye tracking.

 

Eye movements reveal online decision-making while performing a task and provide us some idea of how those decisions might be processed. Brain imaging examines how the brain functions while carrying out various tasks. This makes it possible to connect behaviour to brain activity in order to better comprehend how information is processed. The temporal or time - based and spatial location - based resolution of various imaging techniques varies. A common technique in cognitive neuroscience is brain imaging. 

 

Positron emission tomography and single-photon emission computed tomography. Radioactive isotopes are delivered into the subject's bloodstream and taken up by the brain in SPECT and PET. We can determine whether parts of the brain are more active than others by monitoring which regions of the brain absorb the radioactive isotope. While PET and fMRI have similar spatial resolutions, they have quite different temporal resolutions. 

 

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Electro - encephalo - graphy. A number of electrodes are placed on the subject's scalp during an EEG to measure the electrical fields produced by dense populations of neurons in the brain. The spatial resolution of this method is not great, but the time resolution is very good. Magnetic resonance imaging that is functional. The relative flow of oxygenated blood to various regions of the brain is measured by fMRI. It is believed that increased neuronal activity in a particular area of the brain correlates with more oxygenated blood in that area.

This enables us to pinpoint specific functions throughout various brain areas. The spatial and temporal resolution of fMRI is average. Optical Imaging. This method measures the quantity of light reflection by blood near various brain regions using infrared transmitters and receivers. We can determine which parts are more active ( i.e., those with more oxygenated blood ) by comparing how much oxygenated and deoxygenated blood reflect light. The spatial resolution of optical imaging is weak compared to its moderate temporal resolution.

 

It also has the benefit of being exceedingly safe and useful for studying infants' brain development. Magnetoencephalography. Magnetic fields produced by brain activity are measured by MEG. The magnetic fields it monitors are not as muddled or attenuated by the scalp, meninges, and other tissues as the electrical activity measured in EEG is, hence it has better spatial resolution than EEG. SQUID sensors are used by MEG to find minute magnetic fields. 

 

A formal, mathematical and logical representation of an issue is necessary for computational models. The simulation and experimental verification of various specific and general intelligence features use computer models. We can better comprehend the functional layout of a specific cognitive experience by using computational modelling. These are some categories for cognitive modelling strategies. Using symbols to represent abstract mental operations of an intelligent intelligence.

 

On the neurological and associative characteristics of the human brain, sub symbolic. Includes hybrid across the symbolic - sub symbolic boundary. Using the technologies of knowledge - based systems and a philosophical viewpoint, such as "Good Old - Fashioned Artificial Intelligence" or GOFAI, symbolic modelling emerged from computer science paradigms. They were created by the pioneers of cognitive science and then used to expert systems information engineering.

 

Since the early 1990s, it has been used generally in systemics for the study of functional personoids and other intelligence models, as well as for the development of the SOAR environment. Symbolic cognitive modelling has recently been expanded to include the socio-cognitive approach, incorporating social and organisational cognition, coupled with a sub - symbolic non - conscious layer, particularly in the context of cognitive decision - making. 

 

Neural network and connectionist models are examples of symbolic modelling. According to connectionism, the brain and mind are made up of simple nodes, and our ability to solve problems comes from the connections between these nodes. Neural nets are standard examples of this strategy in use. Even though these models attempt to capture biological reality as a representation of how the system functions, some critics of this strategy believe that despite this, these models lack explanatory power.

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This is because, even in systems with straightforward connection rules, the emerging high complexity renders them less comprehensible at the connection-level than they appear to be at the macroscopic level. The following strategies are some others that are becoming more popular. System dynamics theory. mapping connectionist models onto symbolic models using hybrid intelligent systems or neural - symbolic integration. Machine learning is frequently used to create Bayesian models. 

 

All of the aforementioned approaches either tend to be generalised to the form of integrated computational models of a synthetic or abstract intelligence ( i.e. cognitive architecture ) to be applied to the explanation and enhancement of individual and social/organizational decision-making and reasoning, or they tend to concentrate on single simulative programmes or microtheories or "middle-range" theories, modelling specific cognitive faculties, e.g. vision, language, categorization, etc. 

 

We can also better understand components of intelligence by using research techniques that are directly derived from neuroscience and neuropsychology. Through the use of single - unit recording, direct brain stimulation, animal models, and postmortem investigations, these techniques help us comprehend how intelligent behaviour is implemented in a physical system. Models of human cognitive bias and risk perception have been developed thanks to cognitive science, which has also influenced the growth of behavioural finance, a branch of economics.

 

It has also spawned numerous ideas of AI artificial intelligence, persuasion, and compulsion, as well as a novel theory of mathematics philosophy connected to denotational mathematics. It has established itself as a significant branch of contemporary linguistics, as well as in the philosophy of language and epistemology. Understanding the specific functional systems and functional deficiencies of the brain, from speech production to auditory processing and visual perception, has benefited from the study of cognitive science.

 

It has advanced our understanding of how injury to certain brain regions affects cognition, and it has aided in identifying the causes and effects of particular dysfunctions including dyslexia, anopia, and hemispatial neglect. In cognitive science, some of the more well-known names are frequently either the most contentious or the most frequently mentioned.

 

Some well - known figures in philosophy include Daniel Dennett, who writes from the standpoint of computer systems, John Searle, who is well - known for his contentious Chinese room argument, and Jerry Fodor, who supports functionalism. Others include Douglas Hofstadter, who is well-known for publishing Gödel, Escher, Bach, which explores the nature of words and mind, and David Chalmers, who supports dualism and is also noted for articulating the difficult problem of consciousness. 

 

Noam Chomsky and George Lakoff have made significant contributions to linguistics. Both have gained notoriety as political pundits. Marvin Minsky, Herbert A. Simon, and Allen Newell are well - known figures in artificial intelligence. In the field of psychology, well-  known figures including George A. Miller, James McClelland, Philip Johnson-Laird, Lawrence Barsalou, Vittorio Guidano, Howard Gardner, and Steven Pinker are frequently mentioned.

 

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Dan Sperber, Edwin Hutchins, Bradd Shore, James Wertsch, and Scott Atran are anthropologists who have worked on joint projects with political scientists, cognitive and social psychologists, and evolutionary biologists to develop general theories of culture formation, religion, and political affiliation. By David Rumelhart, James McClelland, and Philip Johnson-Laird, computational theories, including models and simulations have also been constructed. With the establishment of the School of Epistemics, the University of Edinburgh created the term "epistemics" in 1969. 

 

The difference between epistemics and epistemology is that the former refers to the scientific study of knowledge, while the latter is the philosophical theory of knowing. The definition given by Christopher Longuet-Higgins is "the construction of formal models of the processes, viz., perceptual, intellectual, and linguistic, by which knowledge and understanding are achieved and communicated."

 

Alvin I. Goldman asserts to have invented the term "epistemics" to denote a reorientation of epistemology in his work "Epistemics: The Regulative Theory of Cognition" from 1978. According to Goldman, the new term is solely used to avoid confrontation and that his epistemics is continuous with conventional epistemology. In Goldman's interpretation, epistemics is allied with cognitive psychology, a modest departure from classical epistemology. 

 

The study of the mental processes and information processing mechanisms that result in knowledge or beliefs is emphasised in epistemics. The School of Epistemics was renamed The Centre for Cognitive Science or CCS in the middle of the 1980s. CCS was integrated into the School of Informatics at the University of Edinburgh in 1998. To develop a comprehensive theory of cognition is one of the main goals of cognitive science. Integrative mechanisms must be provided to explain how simultaneous information processing taking place.

 

Latter occurs in spatially distinct or sub - cortical regions of the brain. It is coordinated and linked together to produce coherent perceptual and symbolic representations. One strategy is to use integrative synchronisation mechanisms to resolve the "Binding problem" ( i.e., the issue of dynamically representing conjunctions of informational elements, from the most fundamental perceptual representations ( "feature binding" ) to the most complex cognitive representations, like symbol structures ( "variable binding ") ). 

 

In other words, the Binding - by - Synchrony ( BBS ) Hypothesis from Neurophysiology suggests that one of the coordinating mechanisms is the temporal phase synchronisation of neuronal activity based on dynamical self - organizing processes in neural networks. This binding difficulty in perceptual cognition and language cognition has been resolved by connectionist cognitive neuroarchitectures that employ integrative synchronisation mechanisms.

 

In perceptual cognition, the issue is how basic object properties and object relations, like the object colour or the object form, can be dynamically bound together or integrated to a representation of this perceptual object by means of a synchronisation mechanism ( also known as "feature binding" or "feature linking" ). 

 

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The challenge in language cognition is to explain how a synchronisation mechanism that is referred to as "variable binding" can dynamically bind semantic concepts and syntactic roles together or integrate them to complex cognitive representations like systematic and compositional symbol structures and propositions.

Irrigation Informatics - Brief Peek : 

The study of information flows and data management in relation to irrigation is the focus of the recently developed academic area known as irrigation informatics. The discipline is one of many recent informatics sub-specialties that combines information science, information processing, and information systems engineering to promote biophysical science or engineering. 

 

The realisation that world food production must double in the twenty-first century to feed growing populations and that irrigation accounts for 36% of it, but that new land for irrigation growth is very limited, has sparked an eagerness on the part of governments and industry to increase agricultural productivity. Since the area of irrigation science is established and solid, scholars in the field are looking to interdisciplinary fields of study to increase production.

 

Informatics is one of these fields, along with others like social science. The perceived success of other informatics topics, such as health informatics, is a significant motivator for development in the area of irrigation informatics. Wherever information technology or data systems are used, irrigation informatics are very much a part of the broader research into irrigation. However, the term informatics is not always used to describe research involving computer systems and data management, so information science or information technology may also be used.

 

As a result, the phrase "irrigation informatics" is rarely used in many irrigation informatics articles. The publication most likely to feature articles on the topic is Computers and electronics in Agriculture or one of the many journals in irrigation science, such as Irrigation Science. There are currently no formal publications or journals that concentrate on irrigation informatics. At least one publication in the proceedings of scientific conferences has specifically used the term "Irrigation Informatics" in recent work in the broader field of irrigation informatics. 

 

Like all informatics, meteorological informatics is being utilised more and more to manage the expanding data quantities coming from sensors, remote sensing, and scientific models. The Water Data Transfer Format ( WDTF ), a standard used by Australian government organisations and meteorological data vendors when transferring data to the Australian Bureau of Meteorology, has recently been introduced.

 

This format provides evapotranspiration specifications as well as other weather factors that are helpful for irrigation and can be utilised with irrigation informatics implementations. Irrigation, often known as watering, is the process of providing precise amounts of water to a specific area of land to aid in the growth of lawns, landscaping plants, and agricultural crops. For more than 5,000 years, irrigation has been a crucial component of agriculture and has been created by several cultures all over the world.

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In arid locations and during periods of below average rainfall, irrigation aids in the growth of crops, the upkeep of landscapes, and the revegetation of damaged soils. Aside from these purposes, irrigation is also used to avoid soil compaction, inhibit weed growth in grain fields, and protect crops from frost. Additionally, it is utilised to support mining activities, chill animals, lower dust, dispose of sewage, and reduce heat. Irrigation and drainage are frequently studied together since they both involve the evacuation of surface and subsurface water from a specific region. 

 

Water is delivered to plants using a variety of irrigation techniques that vary from one another. The earliest type of irrigation is surface irrigation, commonly referred to as gravity irrigation, and it has been in use for a very long time. When using sprinkler irrigation, water is piped to one or more central places within the field and then disseminated via high-pressure water systems that are located overhead. A system known as micro-irrigation uses a piped network to carry water at low pressure and apply it as a tiny discharge to each plant.

 

Compared to sprinkler irrigation, micro - irrigation requires less pressure and water flow. Water is delivered to the root zone of plants using drip irrigation. Field crops have long been sub - irrigated in regions with high water tables. In order to hydrate the soil beneath the root zone of plants, the water table must be artificially raised. Surface water taken from rivers, lakes, or reservoirs, groundwater ( extracted from springs or wells ), or unconventional sources ( such as treated sewage, desalinated water, drainage water, or fog collecting ) can all be used for irrigation.

 

Both full irrigation, where crops seldom ever depend on any input from rainfall, and supplemental irrigation, which is typical in many regions of the world as rainfed agriculture, are possible with irrigation. Full irrigation is less frequent and only happens when crops are cultivated in semi-arid regions outside of rainy seasons or in arid landscapes with extremely low rainfall. 

 

The changes in soil and water quantity and quality brought on by irrigation and the ensuing consequences on the natural and societal conditions in river basins and upstream of an irrigation system are referred to as the environmental effects of irrigation. The consequences are a result of the irrigation system's installation and operation altering the hydrological environment. Depletion of subsurface aquifers due to overdrafting is one of these issues.

 

Due to uneven water distribution or inadequate management, soil may be over-irrigated, which wastes water, chemicals, and could result in water pollution. Over-irrigation can result in deep drainage from increasing water tables, which can produce salinity issues with irrigation and require some kind of subsurface land drainage to manage the watertable. Over the 20th century, irrigation's scope rapidly expanded. Globally, irrigated land covered 8 million hectares in 1800, 94 million hectares in 1950, and 235 million hectares in 1990.

 

30% of the world's food production in 1990 came from irrigated land. Around the world, surface water is redirected via canals, groundwater is pumped, and water from dams is diverted for irrigation. The majority of irrigation projects are overseen by national governments within their boundaries, but some projects are also funded and arranged by private investors and other countries, including the United States, China, and European nations like the United Kingdom. 

 

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Increased agricultural production, particularly of commodity crops, is made possible by irrigation in regions where they would not otherwise be possible. Often with the overriding objective of improving self-sufficiency, nations typically invested in irrigation to improve the output of wheat, rice, or cotton. Surface water ( taken from rivers, lakes, or reservoirs ), groundwater ( extracted from springs or wells ), or unconventional sources ( such as treated sewage, desalinated water, drainage water, or fog collecting ) can all be used for irrigation. 

 

Rainwater harvesting is typically not regarded as a kind of irrigation, although floodwater harvesting is a recognised irrigation technique. Rainwater harvesting is the gathering and concentration of runoff water from roofs or vacant land. If recycled municipal wastewater contains nutrients like nitrogen, phosphorus, and potassium, irrigation with it can help fertilise plants. Recycled water can be used for irrigation since it is less expensive than some other sources and consistently available regardless of the time of year, the weather, or any water restrictions.

 

The nutrients ( nitrogen and phosphorus ) in the treated wastewater have the advantage of acting as fertiliser when recycled water is utilised for irrigation in agriculture. This may make it appealing to reuse sewage - related excreta. Different crops can benefit from the irrigation water in different ways: Food crops that are intended for human consumption are to be consumed raw or uncooked. For crops used to make processed food. 

 

Behaviour Informatics - An Abstract :

Crops that are meant for human consumption but aren't meant to be eaten raw because they've been cooked or otherwise treated in a factory. It may also be applied to non - food crops, such as pastures, forage, fibre, decorative, seed, woodland, and turf crops, which are not grown for human consumption. The informatics of behaviours is known as behaviour informatics ( BI ), which aims to produce behaviour intelligence and behaviour insights.

 

BI is an engineering - specific research technique that combines science and technology. Analysis of present behaviours and the inference of potential future behaviours are both goals of BI. Through the use of patterns, this happens. From a psychological standpoint, BI differs from applied behaviour analysis in that it develops computational theories, methods, and tools to describe, analyse, and manage behaviours of individuals, groups, and/or organisations in both qualitative and quantitative ways. 

 

BI is based on the traditional study of behavioural science, which includes organisational behaviour, applied behaviour analysis, behaviour analysis, and behaviour modelling. Individual and group behaviour development, representation, computational modelling, analysis, learning, simulation, and understanding of behaviour impact, utility, non - occurring behaviours, etc. are typical BI tasks that are involved in behaviour intervention and management.

 

Cognitive and behavioural data are both used in the Behaviour Informatics approach to data. BI has the potential to efficiently depict the overall picture of behavioural decisions and trends by merging the data. The ability to investigate human behaviour while removing problems like self-report bias is one of the objectives of BI. This produces information for research investigations that is more trustworthy and valid. Analysing and learning from behavioural data is the subject of behaviour analytics, which is a subset of behaviour informatics. 

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According to informatics, a behaviour is made up of the following three essential components. Actors, which are behavioural subjects and objects, operations, which are actions or activities, and interactions, which are relationships. All of an actor's or actor group's behaviours can be represented as behaviour sequences and multi - dimensional behaviour matrices, and a behaviour can be represented as a behaviour vector. When examining business trends and intelligence, behaviour informatics takes behaviour into consideration.

 

Since behaviour is taken into account in these analyses, important details about the social and underlying causes of patterns are revealed. Several contexts, including but not limited to marketing, telecommunications, and health care administration, use behaviour informatics. A major turning point in the health care system is behaviour informatics. A method for analysing and organising the various factors that influence a person's healthcare requirements and decisions is provided by behaviour informatics.

 

Behaviour informatics could play a similar role in relation to business models. Behaviour informatics is used by organisations to improve company structure and regime, where it helps to temper optimal business decisions and circumstances. The cognitive functions that exist within animals and the behavioural interactions between species in the natural world are investigated by the behavioural sciences.

 

Through naturalistic observation, carefully supervised scientific testing, and mathematical modelling, it entails the systematic examination and investigation of both human and animal behaviour. It makes use of meticulous formulations and observation to reach reputable, impartial results. Psychology, psychobiology, anthropology, economics, and cognitive science are a few examples of behavioural sciences. Typically, the goal of behavioural science is to generalise about how human behaviour interacts to society and how it affects society as a whole. 

 

The two broad groups of behavioural sciences are as follows. neurosciences, social sciences, and relationship sciences. Information processing sciences study the information processing of social environment stimuli by cognitive entities to engage in social observation, judgement, and decision - making for individual functioning and organism survival. These include social neuroscience, behavioural analysis, cognitive science, neural networks, social cognition, social psychology, semantic networks, and psychology. 

 

The relationship between creatures or cognitive entities in a social system is the focus of relational sciences, which also examine interactions, interaction, communication networks, linkages, and relational tactics or dynamics. These encompass disciplines like social networks, dynamic network analysis, agent - based modelling, behaviour analysis, and microsimulation. Insights from a variety of applied disciplines in the behavioural sciences are investigated and put into practise in the context of daily life and business. 

 

For instance, the study of consumer behaviour examines the choices that people make when making purchases of goods or services. It investigates how customers identify issues and find solutions. This study applies behavioural science by looking at the buying habits of consumers, the reasons influencing those decisions, and the ways to profit from these patterns. The use of behavioural science in a professional setting is known as organisational behaviour.

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It investigates what drives people, how to increase their productivity, what influences this behaviour, and how to leverage these patterns to further the objectives of the business. Organisational behaviour is frequently used by managers to better guide their staff. Behavioural science can be used to understand how people make decisions about their health and, eventually, reduce illness burden by using interventions like loss aversion, framing, defaults, nudges, and more.

 

This is done by combining knowledge from psychology and economics. The Centre for Health Incentives & Behavioural Economics at the University of Pennsylvania applies behavioural science to improve health care delivery, inform health policy, and promote healthy behaviour in areas like physical activity, vaccination uptake, medication adherence, smoking cessation, and food selection. Operations research and media psychology are two further applied branches of behavioural science. 

 

Both behavioural sciences and social sciences focus on the systematic study of behavioural processes, but they differ in the depth of their scientific examination of many facets of conduct.The study of decision - making and inter - organism communication is a focus of behavioural sciences, which abstract empirical data. Fields like psychology, social neuroscience, ethology, and cognitive science are typically involved in this.

 

In contrast, social sciences offer a perspective framework to investigate how social organisations affect how individuals and groups change their structural relationships. They often cover subjects like political science, economics, anthropology, public health, sociology, and public health. The distinction between the social and behavioural sciences is tested in many of these disciplines' subfields.

 

Even though the broader fields of political science and economics tend to place a greater emphasis on systemic and institutional variables, political psychology and behavioural economics, respectively, employ behavioural techniques. Early in the 20th century, behavioural science started to be primarily researched. John B. Watson is one of the study's founders. In 1908, he started working as a psychology professor at Johns Hopkins University. He presided over the American Psychological Association ( APA ) in 1915.

 

His research approaches into behavioural science have generated some debate. The "Little Albert" experiment was one of them. In this experiment, a young toddler was made to dread a white rat. Fear also extended to other white, hairy objects. This was accomplished by connecting the items with a deafening clanging sound. The fact that the infant was never deconditioned sparked debate. The APA presented him with the Award for Distinguished Scientific Contributions and Discoveries in 1957. 

 

A study on loss aversion was done in 2009 by behavioural scientists, Gächter et al., 2009. According to the study's findings, the psychological impact of winning is double that of losing. In their current research on human conduct, behavioural scientists are using loss aversion. It has aided in illuminating why, in some circumstances, punishment frames are more powerful motivators of human activity than reward frames. A methodical approach to comprehending the conduct of people and other animals is behaviourism, which is also spelt as behaviourism.

 

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It is predicated on the idea that behaviour is either a reflex brought on by the combination of specific antecedent stimuli in the environment or a result of the individual's history, particularly reinforcement and punishment contingencies, in conjunction with their current state of motivation and controlling stimuli. Although most behaviourists acknowledge the significance of inheritance in shaping behaviour, they place a greater emphasis on environmental factors. 

 

Deep psychology and other conventional types of psychology, which frequently struggled to provide predictions that could be confirmed experimentally but were based on older research from the late nineteenth century, gave way to behaviourism in the early 1900s. For instance, Edward Thorndike developed the law of effect, a method that used consequences to either reinforce or undermine behaviour. John B. Watson created methodological behaviourism in a 1924 paper.

 

This approach disregarded introspective techniques and tried to understand behaviour primarily via measurement of observed behaviours and events. B. F. Skinner did not propose that covert behavior, including cognition and emotions, is subject to the same governing factors as observable behaviour until the 1930s, which served as the foundation for his theory known as radical behaviourism. Skinner evaluated the reinforcement histories of the discriminative or antecedent stimuli that emit behaviour. 

 

The method became known as operant conditioning. Watson and Ivan Pavlov studied how conditioned neutral stimuli evoke responses in responder conditioning. The application of radical behaviourism, also known as applied behaviour analysis, is employed in a range of settings, such as the treatment of mental diseases including autism and substance misuse and organisational behaviour management.

 

Additionally, despite the fact that the behaviourism and cognitive schools of psychological thinking do not share the same theoretical viewpoints, they have worked in tandem to create cognitive - behavior treatments, which have shown promise in the treatment of a variety of illnesses, such as mild phobias, PTSD, and mood disorders. The names of the numerous behaviourism branches include the following. A relative of Charles Darwin, Francis Galton, proposed the concept of behavioural genetics in 1869.

 

Prior to B. F. Skinner's writings, Jacob Robert Kantor proposed the idea of interbehaviorism. Methodological behaviourism: According to John B. Watson's behaviourism, only public occurrences, such as an individual's motor behaviours, may be objectively observed. Thoughts and feelings were still accepted to exist, but they were not regarded as being a part of the study of behaviour. Additionally, it provided the theoretical underpinnings for the early behaviour modification technique used in the 1970s and 1980s.

 

Unlike the earlier behaviourisms of Skinner, Hull, and Tolman, psychological behaviourism, as advocated by Arthur W. Staats, was founded on a human study programme incorporating many forms of human behaviour. New learning theories are introduced by psychological behaviourism. Humans learn using both unique human learning principles and the concepts that apply to animal learning. These concepts relate to humans' extraordinarily strong capacity for learning. Humans acquire repertoires that allow them to pick up new skills.

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Therefore, human learning is cumulative. Because no other animal possesses the skill, humans are a special species. Radical behaviourism: Skinner's philosophy is an extension of Watson's form of behaviourism in that it postulates that internal processes in an organism, particularly private events like thoughts and feelings, are also a part of the science of behaviour and that environmental factors influence these internal processes in the same way that they influence externally visible behaviours.

 

Private occurrences aren't visible to others directly, although the species' overt behaviour can subsequently reveal them. The fundamental tenet of behaviour analysis is radical behaviourism. Many of the concepts of radical behaviourism were utilised by Willard Van Orman Quine in his research on knowledge and language. Theorised by Howard Rachlin, teleological behaviourism is post - Skinnerian, intentional, and closely related to microeconomics. focuses on impartial observation rather than mental processes. 

 

J. E. R. Staddon's theoretical behaviourism introduces the idea of internal state to account for the impacts of context. The operant notion of B. F. Skinner states that a state is a collection of equivalent events, or past histories in which individuals from the same stimulus class result in individuals from the same response class. Thus, conditioned stimuli are seen to control status rather than stimulus or response. The class - based or general definition of the operant provided by Skinner is logically extended by theoretical behaviourism. 

 

Theoretical behaviourism has the following two subtypes. Hullian and post - Hullian are theoretical, based on group data, and are not dynamic or physiological. Tolman's behavioristic foreshadowing of cognitive psychology is intentional. The experimental examination of behavior's intellectual foundation was radical behaviourism, according to B. F. Skinner.

 

This point of view differs from other behavioural research perspectives in a number of ways, but most significantly, it contradicts methodological behaviourism by admitting feelings, mental states, and introspection as behaviours that may also be studied scientifically. It denies the reflex as a representation of all behaviour, similar to methodological behaviourism, and advocates the study of behaviour as distinct from yet complementary to physiology. Radical behaviourism and other western philosophical perspectives, including American pragmatism, have a lot in common. 

 

Ivan Pavlov's theory of respondent conditioning was first applied to elicit a fearful reflex of crying in a human infant in the infamous Little Albert experiment ( 1920 ), which served as the starting point for understanding covert behaviour or private events in radical behaviourism. Although John B. Watson primarily emphasised his position of methodological behaviourism throughout his career, Watson and Rosalie Rayner conducted the study. 

 

Skinner, on the other hand, disagreed with Watson's use of aversive stimuli in humans, believing that such experiments should only be conducted on animals. In 1959, Skinner observed two pigeons' emotions by noticing that their ruffled feathers gave them the appearance of being angry. The pigeons were kept together in an operant chamber, where they were hostile due to earlier reinforcement from their surroundings.

 

Through stimulus control and subsequent discrimination training, the pigeons learned that the food reinforcer is stopped after each peck and stopped reacting aggressively anytime Skinner turned off the green light. According to Skinner, non - human animals and humans both acquire hostility and experience these feelings, as well as other private occurrences. Because behavioural neuroscience and experimental behavioural psychology are related, we can pinpoint the beginning of the 19th century as the time when the first studies in this field were conducted. 

 

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Later, the popularity of Skinner's early experiments with rats and pigeons, which were collected in his books The Behaviour of Organisms and Schedules of Reinforcement, strengthened this essentially philosophical perspective. His concept of the operant response, whose canonical illustration was the rat's lever-press, was particularly significant. An operant is a class of physically unique but functionally comparable reactions, in contrast to the concept of a physiological or reflex response. 

 

For instance, a rat may press a lever with its left paw, right paw, or tail, but all of these actions have the same impact on the environment and result in the same outcome. Operants are frequently viewed as species of reactions, where individuals vary but the class coheres in the operant - shared consequences and species-shared reproductive success. The difference between Skinner's theory and S - R theory is seen in this. With these conceptual reformulations, Skinner's empirical work added to prior studies on trial-and-error learning by scholars like Thorndike and Guthrie.

 

The usage of the "free operant," so named because the animal was now allowed to answer at its own rate rather than in a sequence of trials defined by the experimenter procedures, was abandoned along with Thorndike's idea of a stimulus - response "association" or "connection." Using this technique, Skinner conducted extensive experimental research on the impact of various reinforcement schedules and rates on the rates of operant responses given by rats and pigeons.

 

He had amazing success teaching animals to give unexpected reactions, emit a lot of responses, and display a lot of empirical regularities only from their behaviour. This gave his conceptual interpretation some degree of credence. His key work Are Theories of Learning Necessary?, in which he criticised what he saw as theoretical inadequacies then prevalent in the study of psychology, is a great example of how his conceptual analysis made his work considerably more rigorous than that of his peers.

 

The Society for Quantitative Analysis of Behaviour is a significant heir to experimental analysis of behaviour. With his 1957 book Verbal Behaviour and other books on language, Skinner switched from his experimental work to concentrate on the conceptual foundations of a science of behaviour. Noam Chomsky's evaluation of Verbal Behaviour, which provided a lexicon and theory for the functional study of verbal behaviour, was harshly critical. The discrepancies between the two and the underlying theories have since been further examined.

 

Skinner did not answer in detail but asserted that Chomsky did not comprehend his ideas. The behaviourist hypothesis, which contends that language is a collection of habits that can be learned by conditioning, is opposed to innateness theory, which has received harsh criticism. Some contend that the behaviourist explanation is too slow to explain a phenomenon as complex as language development. Language learning was not as significant for a behaviorist's examination of human behaviour as the relationship between language and overt behaviour.

 

In a piece of writing that was later reprinted in his 1969 book Contingencies of Reinforcement, Skinner expressed the opinion that people may create language stimuli that would eventually gain influence over their behaviour, just as they could with external stimuli. Because of the potential for such "instructional control" over behaviour, reward contingencies may not necessarily have the same impact on human behaviour as they consistently do in that of other species. 

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As a result, the emphasis of a radical behaviourist analysis of human behaviour shifted to an effort to comprehend the interaction between instructional control and contingency control as well as the behavioural mechanisms that determine how instructions are created and what level of control they acquire over behaviour. Relational frame theory is a new branch of behavioural research on language that has just begun. Behaviourism focuses on a specific interpretation of learning: a shift in the learner's conduct that is brought about by repetition and reinforcement or Rote learning. 

 

When reinforcement is used, Skinner discovered that behaviours may be moulded. Unwanted behaviour is not rewarded, whereas desired behaviour is. Behaviourism was incorporated into the classroom to help teachers support their pupils in achieving academic and personal success. This technique of instruction, known as the audio-lingual method in the field of language learning, is typified by the use of choral chanting of dialogues and important words by the entire class and prompt correction. 

 

The "teacher" is the dominant figure in the classroom and exerts entire control, and the teacher is the one who evaluates learning and determines what is proper or incorrect. The student is simply told what is proper or wrong without any opportunity for review or reflection during the learning process. The conceptualization of learning using this approach could be characterised as "superficial," as the emphasis is on exterior behavioural changes and does not take into account the internal learning processes that lead to behavioural change or the associated emotions. 

 

Operant conditioning, which B.F. Skinner created in 1937, focuses on managing environmental circumstances to alter behaviour. In other words, reinforcement, a stimulus that increases the chance of performing behaviours, and punishment, a stimulus that diminishes such probability, are the historical consequential contingencies that influence behaviour. Positive consequences involve providing stimuli after a reaction, while negative consequences involve withholding stimuli after a response. 

 

The concepts of four typical types of consequences in operant conditioning are explained in the following explanations. Positive reinforcement is the process of giving someone a stimulus they like, want, or want in order to reinforce desired behaviours. For instance, while teaching a dog to sit, the word "sit" is combined with a treat. The reward serves as a form of constructive reinforcement for the sitting behaviour. Rewarding behaviour as soon as it occurs is essential for creating a positive reinforcement effect.


Removing a stimulus that a person does not want reinforces desired behaviours. This is known as negative reinforcement. A child, for instance, detests being browbeaten into cleaning his room. By removing the unfavourable stimulus of nagging after he has tidied his room, his mother rewards his good behaviour. Another illustration is applying sunscreen prior to going outside. Wearing sunscreen prevents the stimulus of getting a sunburn, which has the negative impact of obtaining a sunburn. 

 

Providing a stimulus that a person does not want in order to reduce undesirable behaviours is known as positive punishment. This might take the form of spanking. A parent may slap a child if they are acting in a way that has been forewarned against. The idea is to have that behaviour avoided by adding this stimulation in place of the undesirable input, the spanking. Although the technique's name uses the word "positive," the key is understanding that it actually means "to add to."

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Therefore, the parent adds the negative stimuli, spanking, in an effort to cease the behaviour. The major issue with this kind of training is that the student typically learns to avoid the punisher rather than the desired behaviour. Removing a stimulus that a person craves in order to reduce undesirable behaviours is known as negative punishment. A youngster being grounded after failing a test is an illustration of this. Taking away the child's ability to play video games is an example of grounding.

 

This is a form of negative punishment as long as it is evident that the removal of the ability to play video games was done in response to a test failure. The relationship between the behaviour and its outcome is crucial in this situation. The Skinner Box, "puzzle box," or operant conditioning chamber are examples of classic operant conditioning experiments used to study the effects of operant conditioning principles on rats, cats, and other species. He learned through the Skinner box experiment that rats learn very well if they receive frequent food rewards.

 

Additionally, Skinner discovered that using rewards to influence the rats' behaviour might also be used to influence human learning. Skinner's model was founded on the idea that punishment was used to stop the reactions to behaviours that were not intended and reinforcement was used to encourage desired actions or responses. This hypothesis demonstrated that both people and animals would repeat behaviours that produce favourable results while avoiding those that produce unfavourable one. 

 

The pigeon learned to peck the disc in exchange for the reward of food, which demonstrated how a successful experiment results in learned behaviour. Contrary to responder conditioning, where antecedent stimuli prompt reflexive behaviour, operant behaviour is just emitted and hence does not force its occurrence. These historical consequential contingencies ultimately lead to antecedent stimulus control. It consists of the regulating stimuli listed below. 

 

An antecedent stimulus that raises the likelihood that an organism will engage in a behaviour is known as a discriminative stimulus ( Sd ). In Skinner's lab, this happened in one instance. The pigeon learned in the past that when it pecked, food would be offered is the positive reinforcing stimuli, therefore, whenever the green light ( Sd ) came, it signalled the pigeon to perform the behaviour of pecking. Stimulus delta ( S - delta ) : An antecedent stimulus that instructs an organism not to engage in a behaviour because it has previously been suppressed or penalised. 

 

The instant stopping of a vehicle after the traffic signal turns red ( S - delta ) is one noteworthy example of this. However, if the person chooses to run the red light but then gets a speeding ticket or the positive punishment stimulus, this behaviour may not happen again when the S - delta is present. Respondent conditioning, also known as Pavlovian or classical conditioning, is a crucial behavior - analytic process that does not involve mental or other interior processes, even if operant conditioning dominates discussions of behavioural causes.

 

The most well - known use of classical conditioning is seen in Pavlov's research with dogs. The dog was initially given meat to eat ( an unconditioned stimulus, UCS, which naturally elicits a reaction that is uncontrolled ), which caused the dog to salivate more ( an unconditioned response, UCR, which denotes that a response is naturally triggered by an unconditioned stimulus, UCS ). The dog was then given a bell ring and food after that. 

 

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For instance, it is obvious that the cognitive process of searching includes serial rather than parallel processing if the reaction times in a search task change proportionally with the quantity of elements. physical reactions in the mind. An established psychological method called psychophysical experiments has been used by cognitive psychology. They often require evaluating some sort of physical characteristic, like how loud a sound is. Contrary to actual physical measures, the correlation of subjective scales between individuals can reveal cognitive or sensory biases.

 

Examples include threshold differences for colours, tones, textures, etc. and sameness judgements for those elements. Monitoring the eyes. Several cognitive functions, most notably language and visual perception, are studied using this paradigm. An individual's focus of attention is related to the fixation point of their eyes. Thus, we can analyse what information is being processed at a given time by observing eye movements. We can examine cognitive processes on incredibly small time scales because to eye tracking.

 

Eye movements reveal online decision-making while performing a task and provide us some idea of how those decisions might be processed. Brain imaging examines how the brain functions while carrying out various tasks. This makes it possible to connect behaviour to brain activity in order to better comprehend how information is processed. The temporal or time - based and spatial location - based resolution of various imaging techniques varies. A common technique in cognitive neuroscience is brain imaging. 

 

Positron emission tomography and single-photon emission computed tomography. Radioactive isotopes are delivered into the subject's bloodstream and taken up by the brain in SPECT and PET. We can determine whether parts of the brain are more active than others by monitoring which regions of the brain absorb the radioactive isotope. While PET and fMRI have similar spatial resolutions, they have quite different temporal resolutions. 

 

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Electro - encephalo - graphy. A number of electrodes are placed on the subject's scalp during an EEG to measure the electrical fields produced by dense populations of neurons in the brain. The spatial resolution of this method is not great, but the time resolution is very good. Magnetic resonance imaging that is functional. The relative flow of oxygenated blood to various regions of the brain is measured by fMRI. It is believed that increased neuronal activity in a particular area of the brain correlates with more oxygenated blood in that area.

This enables us to pinpoint specific functions throughout various brain areas. The spatial and temporal resolution of fMRI is average. Optical Imaging. This method measures the quantity of light reflection by blood near various brain regions using infrared transmitters and receivers. We can determine which parts are more active ( i.e., those with more oxygenated blood ) by comparing how much oxygenated and deoxygenated blood reflect light. The spatial resolution of optical imaging is weak compared to its moderate temporal resolution.

 

It also has the benefit of being exceedingly safe and useful for studying infants' brain development. Magnetoencephalography. Magnetic fields produced by brain activity are measured by MEG. The magnetic fields it monitors are not as muddled or attenuated by the scalp, meninges, and other tissues as the electrical activity measured in EEG is, hence it has better spatial resolution than EEG. SQUID sensors are used by MEG to find minute magnetic fields. 

 

A formal, mathematical and logical representation of an issue is necessary for computational models. The simulation and experimental verification of various specific and general intelligence features use computer models. We can better comprehend the functional layout of a specific cognitive experience by using computational modelling. These are some categories for cognitive modelling strategies. Using symbols to represent abstract mental operations of an intelligent intelligence.

 

On the neurological and associative characteristics of the human brain, sub symbolic. Includes hybrid across the symbolic - sub symbolic boundary. Using the technologies of knowledge - based systems and a philosophical viewpoint, such as "Good Old - Fashioned Artificial Intelligence" or GOFAI, symbolic modelling emerged from computer science paradigms. They were created by the pioneers of cognitive science and then used to expert systems information engineering.

 

Since the early 1990s, it has been used generally in systemics for the study of functional personoids and other intelligence models, as well as for the development of the SOAR environment. Symbolic cognitive modelling has recently been expanded to include the socio-cognitive approach, incorporating social and organisational cognition, coupled with a sub - symbolic non - conscious layer, particularly in the context of cognitive decision - making. 

 

Neural network and connectionist models are examples of symbolic modelling. According to connectionism, the brain and mind are made up of simple nodes, and our ability to solve problems comes from the connections between these nodes. Neural nets are standard examples of this strategy in use. Even though these models attempt to capture biological reality as a representation of how the system functions, some critics of this strategy believe that despite this, these models lack explanatory power.

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This is because, even in systems with straightforward connection rules, the emerging high complexity renders them less comprehensible at the connection-level than they appear to be at the macroscopic level. The following strategies are some others that are becoming more popular. System dynamics theory. mapping connectionist models onto symbolic models using hybrid intelligent systems or neural - symbolic integration. Machine learning is frequently used to create Bayesian models. 

 

All of the aforementioned approaches either tend to be generalised to the form of integrated computational models of a synthetic or abstract intelligence ( i.e. cognitive architecture ) to be applied to the explanation and enhancement of individual and social/organizational decision-making and reasoning, or they tend to concentrate on single simulative programmes or microtheories or "middle-range" theories, modelling specific cognitive faculties, e.g. vision, language, categorization, etc. 

 

We can also better understand components of intelligence by using research techniques that are directly derived from neuroscience and neuropsychology. Through the use of single - unit recording, direct brain stimulation, animal models, and postmortem investigations, these techniques help us comprehend how intelligent behaviour is implemented in a physical system. Models of human cognitive bias and risk perception have been developed thanks to cognitive science, which has also influenced the growth of behavioural finance, a branch of economics.

 

It has also spawned numerous ideas of AI artificial intelligence, persuasion, and compulsion, as well as a novel theory of mathematics philosophy connected to denotational mathematics. It has established itself as a significant branch of contemporary linguistics, as well as in the philosophy of language and epistemology. Understanding the specific functional systems and functional deficiencies of the brain, from speech production to auditory processing and visual perception, has benefited from the study of cognitive science.

 

It has advanced our understanding of how injury to certain brain regions affects cognition, and it has aided in identifying the causes and effects of particular dysfunctions including dyslexia, anopia, and hemispatial neglect. In cognitive science, some of the more well-known names are frequently either the most contentious or the most frequently mentioned.

 

Some well - known figures in philosophy include Daniel Dennett, who writes from the standpoint of computer systems, John Searle, who is well - known for his contentious Chinese room argument, and Jerry Fodor, who supports functionalism. Others include Douglas Hofstadter, who is well-known for publishing Gödel, Escher, Bach, which explores the nature of words and mind, and David Chalmers, who supports dualism and is also noted for articulating the difficult problem of consciousness. 

 

Noam Chomsky and George Lakoff have made significant contributions to linguistics. Both have gained notoriety as political pundits. Marvin Minsky, Herbert A. Simon, and Allen Newell are well - known figures in artificial intelligence. In the field of psychology, well-  known figures including George A. Miller, James McClelland, Philip Johnson-Laird, Lawrence Barsalou, Vittorio Guidano, Howard Gardner, and Steven Pinker are frequently mentioned.

 

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Dan Sperber, Edwin Hutchins, Bradd Shore, James Wertsch, and Scott Atran are anthropologists who have worked on joint projects with political scientists, cognitive and social psychologists, and evolutionary biologists to develop general theories of culture formation, religion, and political affiliation. By David Rumelhart, James McClelland, and Philip Johnson-Laird, computational theories, including models and simulations have also been constructed. With the establishment of the School of Epistemics, the University of Edinburgh created the term "epistemics" in 1969. 

 

The difference between epistemics and epistemology is that the former refers to the scientific study of knowledge, while the latter is the philosophical theory of knowing. The definition given by Christopher Longuet-Higgins is "the construction of formal models of the processes, viz., perceptual, intellectual, and linguistic, by which knowledge and understanding are achieved and communicated."

 

Alvin I. Goldman asserts to have invented the term "epistemics" to denote a reorientation of epistemology in his work "Epistemics: The Regulative Theory of Cognition" from 1978. According to Goldman, the new term is solely used to avoid confrontation and that his epistemics is continuous with conventional epistemology. In Goldman's interpretation, epistemics is allied with cognitive psychology, a modest departure from classical epistemology. 

 

The study of the mental processes and information processing mechanisms that result in knowledge or beliefs is emphasised in epistemics. The School of Epistemics was renamed The Centre for Cognitive Science or CCS in the middle of the 1980s. CCS was integrated into the School of Informatics at the University of Edinburgh in 1998. To develop a comprehensive theory of cognition is one of the main goals of cognitive science. Integrative mechanisms must be provided to explain how simultaneous information processing taking place.

 

Latter occurs in spatially distinct or sub - cortical regions of the brain. It is coordinated and linked together to produce coherent perceptual and symbolic representations. One strategy is to use integrative synchronisation mechanisms to resolve the "Binding problem" ( i.e., the issue of dynamically representing conjunctions of informational elements, from the most fundamental perceptual representations ( "feature binding" ) to the most complex cognitive representations, like symbol structures ( "variable binding ") ). 

 

In other words, the Binding - by - Synchrony ( BBS ) Hypothesis from Neurophysiology suggests that one of the coordinating mechanisms is the temporal phase synchronisation of neuronal activity based on dynamical self - organizing processes in neural networks. This binding difficulty in perceptual cognition and language cognition has been resolved by connectionist cognitive neuroarchitectures that employ integrative synchronisation mechanisms.

 

In perceptual cognition, the issue is how basic object properties and object relations, like the object colour or the object form, can be dynamically bound together or integrated to a representation of this perceptual object by means of a synchronisation mechanism ( also known as "feature binding" or "feature linking" ). 

 

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The challenge in language cognition is to explain how a synchronisation mechanism that is referred to as "variable binding" can dynamically bind semantic concepts and syntactic roles together or integrate them to complex cognitive representations like systematic and compositional symbol structures and propositions.

Irrigation Informatics - Brief Peek : 

The study of information flows and data management in relation to irrigation is the focus of the recently developed academic area known as irrigation informatics. The discipline is one of many recent informatics sub-specialties that combines information science, information processing, and information systems engineering to promote biophysical science or engineering. 

 

The realisation that world food production must double in the twenty-first century to feed growing populations and that irrigation accounts for 36% of it, but that new land for irrigation growth is very limited, has sparked an eagerness on the part of governments and industry to increase agricultural productivity. Since the area of irrigation science is established and solid, scholars in the field are looking to interdisciplinary fields of study to increase production.

 

Informatics is one of these fields, along with others like social science. The perceived success of other informatics topics, such as health informatics, is a significant motivator for development in the area of irrigation informatics. Wherever information technology or data systems are used, irrigation informatics are very much a part of the broader research into irrigation. However, the term informatics is not always used to describe research involving computer systems and data management, so information science or information technology may also be used.

 

As a result, the phrase "irrigation informatics" is rarely used in many irrigation informatics articles. The publication most likely to feature articles on the topic is Computers and electronics in Agriculture or one of the many journals in irrigation science, such as Irrigation Science. There are currently no formal publications or journals that concentrate on irrigation informatics. At least one publication in the proceedings of scientific conferences has specifically used the term "Irrigation Informatics" in recent work in the broader field of irrigation informatics. 

 

Like all informatics, meteorological informatics is being utilised more and more to manage the expanding data quantities coming from sensors, remote sensing, and scientific models. The Water Data Transfer Format ( WDTF ), a standard used by Australian government organisations and meteorological data vendors when transferring data to the Australian Bureau of Meteorology, has recently been introduced.

 

This format provides evapotranspiration specifications as well as other weather factors that are helpful for irrigation and can be utilised with irrigation informatics implementations. Irrigation, often known as watering, is the process of providing precise amounts of water to a specific area of land to aid in the growth of lawns, landscaping plants, and agricultural crops. For more than 5,000 years, irrigation has been a crucial component of agriculture and has been created by several cultures all over the world.

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In arid locations and during periods of below average rainfall, irrigation aids in the growth of crops, the upkeep of landscapes, and the revegetation of damaged soils. Aside from these purposes, irrigation is also used to avoid soil compaction, inhibit weed growth in grain fields, and protect crops from frost. Additionally, it is utilised to support mining activities, chill animals, lower dust, dispose of sewage, and reduce heat. Irrigation and drainage are frequently studied together since they both involve the evacuation of surface and subsurface water from a specific region. 

 

Water is delivered to plants using a variety of irrigation techniques that vary from one another. The earliest type of irrigation is surface irrigation, commonly referred to as gravity irrigation, and it has been in use for a very long time. When using sprinkler irrigation, water is piped to one or more central places within the field and then disseminated via high-pressure water systems that are located overhead. A system known as micro-irrigation uses a piped network to carry water at low pressure and apply it as a tiny discharge to each plant.

 

Compared to sprinkler irrigation, micro - irrigation requires less pressure and water flow. Water is delivered to the root zone of plants using drip irrigation. Field crops have long been sub - irrigated in regions with high water tables. In order to hydrate the soil beneath the root zone of plants, the water table must be artificially raised. Surface water taken from rivers, lakes, or reservoirs, groundwater ( extracted from springs or wells ), or unconventional sources ( such as treated sewage, desalinated water, drainage water, or fog collecting ) can all be used for irrigation.

 

Both full irrigation, where crops seldom ever depend on any input from rainfall, and supplemental irrigation, which is typical in many regions of the world as rainfed agriculture, are possible with irrigation. Full irrigation is less frequent and only happens when crops are cultivated in semi-arid regions outside of rainy seasons or in arid landscapes with extremely low rainfall. 

 

The changes in soil and water quantity and quality brought on by irrigation and the ensuing consequences on the natural and societal conditions in river basins and upstream of an irrigation system are referred to as the environmental effects of irrigation. The consequences are a result of the irrigation system's installation and operation altering the hydrological environment. Depletion of subsurface aquifers due to overdrafting is one of these issues.

 

Due to uneven water distribution or inadequate management, soil may be over-irrigated, which wastes water, chemicals, and could result in water pollution. Over-irrigation can result in deep drainage from increasing water tables, which can produce salinity issues with irrigation and require some kind of subsurface land drainage to manage the watertable. Over the 20th century, irrigation's scope rapidly expanded. Globally, irrigated land covered 8 million hectares in 1800, 94 million hectares in 1950, and 235 million hectares in 1990.

 

30% of the world's food production in 1990 came from irrigated land. Around the world, surface water is redirected via canals, groundwater is pumped, and water from dams is diverted for irrigation. The majority of irrigation projects are overseen by national governments within their boundaries, but some projects are also funded and arranged by private investors and other countries, including the United States, China, and European nations like the United Kingdom. 

 

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Increased agricultural production, particularly of commodity crops, is made possible by irrigation in regions where they would not otherwise be possible. Often with the overriding objective of improving self-sufficiency, nations typically invested in irrigation to improve the output of wheat, rice, or cotton. Surface water ( taken from rivers, lakes, or reservoirs ), groundwater ( extracted from springs or wells ), or unconventional sources ( such as treated sewage, desalinated water, drainage water, or fog collecting ) can all be used for irrigation. 

 

Rainwater harvesting is typically not regarded as a kind of irrigation, although floodwater harvesting is a recognised irrigation technique. Rainwater harvesting is the gathering and concentration of runoff water from roofs or vacant land. If recycled municipal wastewater contains nutrients like nitrogen, phosphorus, and potassium, irrigation with it can help fertilise plants. Recycled water can be used for irrigation since it is less expensive than some other sources and consistently available regardless of the time of year, the weather, or any water restrictions.

 

The nutrients ( nitrogen and phosphorus ) in the treated wastewater have the advantage of acting as fertiliser when recycled water is utilised for irrigation in agriculture. This may make it appealing to reuse sewage - related excreta. Different crops can benefit from the irrigation water in different ways: Food crops that are intended for human consumption are to be consumed raw or uncooked. For crops used to make processed food. 

 

Behaviour Informatics - An Abstract :

Crops that are meant for human consumption but aren't meant to be eaten raw because they've been cooked or otherwise treated in a factory. It may also be applied to non - food crops, such as pastures, forage, fibre, decorative, seed, woodland, and turf crops, which are not grown for human consumption. The informatics of behaviours is known as behaviour informatics ( BI ), which aims to produce behaviour intelligence and behaviour insights.

 

BI is an engineering - specific research technique that combines science and technology. Analysis of present behaviours and the inference of potential future behaviours are both goals of BI. Through the use of patterns, this happens. From a psychological standpoint, BI differs from applied behaviour analysis in that it develops computational theories, methods, and tools to describe, analyse, and manage behaviours of individuals, groups, and/or organisations in both qualitative and quantitative ways. 

 

BI is based on the traditional study of behavioural science, which includes organisational behaviour, applied behaviour analysis, behaviour analysis, and behaviour modelling. Individual and group behaviour development, representation, computational modelling, analysis, learning, simulation, and understanding of behaviour impact, utility, non - occurring behaviours, etc. are typical BI tasks that are involved in behaviour intervention and management.

 

Cognitive and behavioural data are both used in the Behaviour Informatics approach to data. BI has the potential to efficiently depict the overall picture of behavioural decisions and trends by merging the data. The ability to investigate human behaviour while removing problems like self-report bias is one of the objectives of BI. This produces information for research investigations that is more trustworthy and valid. Analysing and learning from behavioural data is the subject of behaviour analytics, which is a subset of behaviour informatics. 

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According to informatics, a behaviour is made up of the following three essential components. Actors, which are behavioural subjects and objects, operations, which are actions or activities, and interactions, which are relationships. All of an actor's or actor group's behaviours can be represented as behaviour sequences and multi - dimensional behaviour matrices, and a behaviour can be represented as a behaviour vector. When examining business trends and intelligence, behaviour informatics takes behaviour into consideration.

 

Since behaviour is taken into account in these analyses, important details about the social and underlying causes of patterns are revealed. Several contexts, including but not limited to marketing, telecommunications, and health care administration, use behaviour informatics. A major turning point in the health care system is behaviour informatics. A method for analysing and organising the various factors that influence a person's healthcare requirements and decisions is provided by behaviour informatics.

 

Behaviour informatics could play a similar role in relation to business models. Behaviour informatics is used by organisations to improve company structure and regime, where it helps to temper optimal business decisions and circumstances. The cognitive functions that exist within animals and the behavioural interactions between species in the natural world are investigated by the behavioural sciences.

 

Through naturalistic observation, carefully supervised scientific testing, and mathematical modelling, it entails the systematic examination and investigation of both human and animal behaviour. It makes use of meticulous formulations and observation to reach reputable, impartial results. Psychology, psychobiology, anthropology, economics, and cognitive science are a few examples of behavioural sciences. Typically, the goal of behavioural science is to generalise about how human behaviour interacts to society and how it affects society as a whole. 

 

The two broad groups of behavioural sciences are as follows. neurosciences, social sciences, and relationship sciences. Information processing sciences study the information processing of social environment stimuli by cognitive entities to engage in social observation, judgement, and decision - making for individual functioning and organism survival. These include social neuroscience, behavioural analysis, cognitive science, neural networks, social cognition, social psychology, semantic networks, and psychology. 

 

The relationship between creatures or cognitive entities in a social system is the focus of relational sciences, which also examine interactions, interaction, communication networks, linkages, and relational tactics or dynamics. These encompass disciplines like social networks, dynamic network analysis, agent - based modelling, behaviour analysis, and microsimulation. Insights from a variety of applied disciplines in the behavioural sciences are investigated and put into practise in the context of daily life and business. 

 

For instance, the study of consumer behaviour examines the choices that people make when making purchases of goods or services. It investigates how customers identify issues and find solutions. This study applies behavioural science by looking at the buying habits of consumers, the reasons influencing those decisions, and the ways to profit from these patterns. The use of behavioural science in a professional setting is known as organisational behaviour.

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It investigates what drives people, how to increase their productivity, what influences this behaviour, and how to leverage these patterns to further the objectives of the business. Organisational behaviour is frequently used by managers to better guide their staff. Behavioural science can be used to understand how people make decisions about their health and, eventually, reduce illness burden by using interventions like loss aversion, framing, defaults, nudges, and more.

 

This is done by combining knowledge from psychology and economics. The Centre for Health Incentives & Behavioural Economics at the University of Pennsylvania applies behavioural science to improve health care delivery, inform health policy, and promote healthy behaviour in areas like physical activity, vaccination uptake, medication adherence, smoking cessation, and food selection. Operations research and media psychology are two further applied branches of behavioural science. 

 

Both behavioural sciences and social sciences focus on the systematic study of behavioural processes, but they differ in the depth of their scientific examination of many facets of conduct.The study of decision - making and inter - organism communication is a focus of behavioural sciences, which abstract empirical data. Fields like psychology, social neuroscience, ethology, and cognitive science are typically involved in this.

 

In contrast, social sciences offer a perspective framework to investigate how social organisations affect how individuals and groups change their structural relationships. They often cover subjects like political science, economics, anthropology, public health, sociology, and public health. The distinction between the social and behavioural sciences is tested in many of these disciplines' subfields.

 

Even though the broader fields of political science and economics tend to place a greater emphasis on systemic and institutional variables, political psychology and behavioural economics, respectively, employ behavioural techniques. Early in the 20th century, behavioural science started to be primarily researched. John B. Watson is one of the study's founders. In 1908, he started working as a psychology professor at Johns Hopkins University. He presided over the American Psychological Association ( APA ) in 1915.

 

His research approaches into behavioural science have generated some debate. The "Little Albert" experiment was one of them. In this experiment, a young toddler was made to dread a white rat. Fear also extended to other white, hairy objects. This was accomplished by connecting the items with a deafening clanging sound. The fact that the infant was never deconditioned sparked debate. The APA presented him with the Award for Distinguished Scientific Contributions and Discoveries in 1957. 

 

A study on loss aversion was done in 2009 by behavioural scientists, Gächter et al., 2009. According to the study's findings, the psychological impact of winning is double that of losing. In their current research on human conduct, behavioural scientists are using loss aversion. It has aided in illuminating why, in some circumstances, punishment frames are more powerful motivators of human activity than reward frames. A methodical approach to comprehending the conduct of people and other animals is behaviourism, which is also spelt as behaviourism.

 

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It is predicated on the idea that behaviour is either a reflex brought on by the combination of specific antecedent stimuli in the environment or a result of the individual's history, particularly reinforcement and punishment contingencies, in conjunction with their current state of motivation and controlling stimuli. Although most behaviourists acknowledge the significance of inheritance in shaping behaviour, they place a greater emphasis on environmental factors. 

 

Deep psychology and other conventional types of psychology, which frequently struggled to provide predictions that could be confirmed experimentally but were based on older research from the late nineteenth century, gave way to behaviourism in the early 1900s. For instance, Edward Thorndike developed the law of effect, a method that used consequences to either reinforce or undermine behaviour. John B. Watson created methodological behaviourism in a 1924 paper.

 

This approach disregarded introspective techniques and tried to understand behaviour primarily via measurement of observed behaviours and events. B. F. Skinner did not propose that covert behavior, including cognition and emotions, is subject to the same governing factors as observable behaviour until the 1930s, which served as the foundation for his theory known as radical behaviourism. Skinner evaluated the reinforcement histories of the discriminative or antecedent stimuli that emit behaviour. 

 

The method became known as operant conditioning. Watson and Ivan Pavlov studied how conditioned neutral stimuli evoke responses in responder conditioning. The application of radical behaviourism, also known as applied behaviour analysis, is employed in a range of settings, such as the treatment of mental diseases including autism and substance misuse and organisational behaviour management.

 

Additionally, despite the fact that the behaviourism and cognitive schools of psychological thinking do not share the same theoretical viewpoints, they have worked in tandem to create cognitive - behavior treatments, which have shown promise in the treatment of a variety of illnesses, such as mild phobias, PTSD, and mood disorders. The names of the numerous behaviourism branches include the following. A relative of Charles Darwin, Francis Galton, proposed the concept of behavioural genetics in 1869.

 

Prior to B. F. Skinner's writings, Jacob Robert Kantor proposed the idea of interbehaviorism. Methodological behaviourism: According to John B. Watson's behaviourism, only public occurrences, such as an individual's motor behaviours, may be objectively observed. Thoughts and feelings were still accepted to exist, but they were not regarded as being a part of the study of behaviour. Additionally, it provided the theoretical underpinnings for the early behaviour modification technique used in the 1970s and 1980s.

 

Unlike the earlier behaviourisms of Skinner, Hull, and Tolman, psychological behaviourism, as advocated by Arthur W. Staats, was founded on a human study programme incorporating many forms of human behaviour. New learning theories are introduced by psychological behaviourism. Humans learn using both unique human learning principles and the concepts that apply to animal learning. These concepts relate to humans' extraordinarily strong capacity for learning. Humans acquire repertoires that allow them to pick up new skills.

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Therefore, human learning is cumulative. Because no other animal possesses the skill, humans are a special species. Radical behaviourism: Skinner's philosophy is an extension of Watson's form of behaviourism in that it postulates that internal processes in an organism, particularly private events like thoughts and feelings, are also a part of the science of behaviour and that environmental factors influence these internal processes in the same way that they influence externally visible behaviours.

 

Private occurrences aren't visible to others directly, although the species' overt behaviour can subsequently reveal them. The fundamental tenet of behaviour analysis is radical behaviourism. Many of the concepts of radical behaviourism were utilised by Willard Van Orman Quine in his research on knowledge and language. Theorised by Howard Rachlin, teleological behaviourism is post - Skinnerian, intentional, and closely related to microeconomics. focuses on impartial observation rather than mental processes. 

 

J. E. R. Staddon's theoretical behaviourism introduces the idea of internal state to account for the impacts of context. The operant notion of B. F. Skinner states that a state is a collection of equivalent events, or past histories in which individuals from the same stimulus class result in individuals from the same response class. Thus, conditioned stimuli are seen to control status rather than stimulus or response. The class - based or general definition of the operant provided by Skinner is logically extended by theoretical behaviourism. 

 

Theoretical behaviourism has the following two subtypes. Hullian and post - Hullian are theoretical, based on group data, and are not dynamic or physiological. Tolman's behavioristic foreshadowing of cognitive psychology is intentional. The experimental examination of behavior's intellectual foundation was radical behaviourism, according to B. F. Skinner.

 

This point of view differs from other behavioural research perspectives in a number of ways, but most significantly, it contradicts methodological behaviourism by admitting feelings, mental states, and introspection as behaviours that may also be studied scientifically. It denies the reflex as a representation of all behaviour, similar to methodological behaviourism, and advocates the study of behaviour as distinct from yet complementary to physiology. Radical behaviourism and other western philosophical perspectives, including American pragmatism, have a lot in common. 

 

Ivan Pavlov's theory of respondent conditioning was first applied to elicit a fearful reflex of crying in a human infant in the infamous Little Albert experiment ( 1920 ), which served as the starting point for understanding covert behaviour or private events in radical behaviourism. Although John B. Watson primarily emphasised his position of methodological behaviourism throughout his career, Watson and Rosalie Rayner conducted the study. 

 

Skinner, on the other hand, disagreed with Watson's use of aversive stimuli in humans, believing that such experiments should only be conducted on animals. In 1959, Skinner observed two pigeons' emotions by noticing that their ruffled feathers gave them the appearance of being angry. The pigeons were kept together in an operant chamber, where they were hostile due to earlier reinforcement from their surroundings.

 

Through stimulus control and subsequent discrimination training, the pigeons learned that the food reinforcer is stopped after each peck and stopped reacting aggressively anytime Skinner turned off the green light. According to Skinner, non - human animals and humans both acquire hostility and experience these feelings, as well as other private occurrences. Because behavioural neuroscience and experimental behavioural psychology are related, we can pinpoint the beginning of the 19th century as the time when the first studies in this field were conducted. 

 

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Later, the popularity of Skinner's early experiments with rats and pigeons, which were collected in his books The Behaviour of Organisms and Schedules of Reinforcement, strengthened this essentially philosophical perspective. His concept of the operant response, whose canonical illustration was the rat's lever-press, was particularly significant. An operant is a class of physically unique but functionally comparable reactions, in contrast to the concept of a physiological or reflex response. 

 

For instance, a rat may press a lever with its left paw, right paw, or tail, but all of these actions have the same impact on the environment and result in the same outcome. Operants are frequently viewed as species of reactions, where individuals vary but the class coheres in the operant - shared consequences and species-shared reproductive success. The difference between Skinner's theory and S - R theory is seen in this. With these conceptual reformulations, Skinner's empirical work added to prior studies on trial-and-error learning by scholars like Thorndike and Guthrie.

 

The usage of the "free operant," so named because the animal was now allowed to answer at its own rate rather than in a sequence of trials defined by the experimenter procedures, was abandoned along with Thorndike's idea of a stimulus - response "association" or "connection." Using this technique, Skinner conducted extensive experimental research on the impact of various reinforcement schedules and rates on the rates of operant responses given by rats and pigeons.

 

He had amazing success teaching animals to give unexpected reactions, emit a lot of responses, and display a lot of empirical regularities only from their behaviour. This gave his conceptual interpretation some degree of credence. His key work Are Theories of Learning Necessary?, in which he criticised what he saw as theoretical inadequacies then prevalent in the study of psychology, is a great example of how his conceptual analysis made his work considerably more rigorous than that of his peers.

 

The Society for Quantitative Analysis of Behaviour is a significant heir to experimental analysis of behaviour. With his 1957 book Verbal Behaviour and other books on language, Skinner switched from his experimental work to concentrate on the conceptual foundations of a science of behaviour. Noam Chomsky's evaluation of Verbal Behaviour, which provided a lexicon and theory for the functional study of verbal behaviour, was harshly critical. The discrepancies between the two and the underlying theories have since been further examined.

 

Skinner did not answer in detail but asserted that Chomsky did not comprehend his ideas. The behaviourist hypothesis, which contends that language is a collection of habits that can be learned by conditioning, is opposed to innateness theory, which has received harsh criticism. Some contend that the behaviourist explanation is too slow to explain a phenomenon as complex as language development. Language learning was not as significant for a behaviorist's examination of human behaviour as the relationship between language and overt behaviour.

 

In a piece of writing that was later reprinted in his 1969 book Contingencies of Reinforcement, Skinner expressed the opinion that people may create language stimuli that would eventually gain influence over their behaviour, just as they could with external stimuli. Because of the potential for such "instructional control" over behaviour, reward contingencies may not necessarily have the same impact on human behaviour as they consistently do in that of other species. 

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As a result, the emphasis of a radical behaviourist analysis of human behaviour shifted to an effort to comprehend the interaction between instructional control and contingency control as well as the behavioural mechanisms that determine how instructions are created and what level of control they acquire over behaviour. Relational frame theory is a new branch of behavioural research on language that has just begun. Behaviourism focuses on a specific interpretation of learning: a shift in the learner's conduct that is brought about by repetition and reinforcement or Rote learning. 

 

When reinforcement is used, Skinner discovered that behaviours may be moulded. Unwanted behaviour is not rewarded, whereas desired behaviour is. Behaviourism was incorporated into the classroom to help teachers support their pupils in achieving academic and personal success. This technique of instruction, known as the audio-lingual method in the field of language learning, is typified by the use of choral chanting of dialogues and important words by the entire class and prompt correction. 

 

The "teacher" is the dominant figure in the classroom and exerts entire control, and the teacher is the one who evaluates learning and determines what is proper or incorrect. The student is simply told what is proper or wrong without any opportunity for review or reflection during the learning process. The conceptualization of learning using this approach could be characterised as "superficial," as the emphasis is on exterior behavioural changes and does not take into account the internal learning processes that lead to behavioural change or the associated emotions. 

 

Operant conditioning, which B.F. Skinner created in 1937, focuses on managing environmental circumstances to alter behaviour. In other words, reinforcement, a stimulus that increases the chance of performing behaviours, and punishment, a stimulus that diminishes such probability, are the historical consequential contingencies that influence behaviour. Positive consequences involve providing stimuli after a reaction, while negative consequences involve withholding stimuli after a response. 

 

The concepts of four typical types of consequences in operant conditioning are explained in the following explanations. Positive reinforcement is the process of giving someone a stimulus they like, want, or want in order to reinforce desired behaviours. For instance, while teaching a dog to sit, the word "sit" is combined with a treat. The reward serves as a form of constructive reinforcement for the sitting behaviour. Rewarding behaviour as soon as it occurs is essential for creating a positive reinforcement effect.


Removing a stimulus that a person does not want reinforces desired behaviours. This is known as negative reinforcement. A child, for instance, detests being browbeaten into cleaning his room. By removing the unfavourable stimulus of nagging after he has tidied his room, his mother rewards his good behaviour. Another illustration is applying sunscreen prior to going outside. Wearing sunscreen prevents the stimulus of getting a sunburn, which has the negative impact of obtaining a sunburn. 

 

Providing a stimulus that a person does not want in order to reduce undesirable behaviours is known as positive punishment. This might take the form of spanking. A parent may slap a child if they are acting in a way that has been forewarned against. The idea is to have that behaviour avoided by adding this stimulation in place of the undesirable input, the spanking. Although the technique's name uses the word "positive," the key is understanding that it actually means "to add to."

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Therefore, the parent adds the negative stimuli, spanking, in an effort to cease the behaviour. The major issue with this kind of training is that the student typically learns to avoid the punisher rather than the desired behaviour. Removing a stimulus that a person craves in order to reduce undesirable behaviours is known as negative punishment. A youngster being grounded after failing a test is an illustration of this. Taking away the child's ability to play video games is an example of grounding.

 

This is a form of negative punishment as long as it is evident that the removal of the ability to play video games was done in response to a test failure. The relationship between the behaviour and its outcome is crucial in this situation. The Skinner Box, "puzzle box," or operant conditioning chamber are examples of classic operant conditioning experiments used to study the effects of operant conditioning principles on rats, cats, and other species. He learned through the Skinner box experiment that rats learn very well if they receive frequent food rewards.

 

Additionally, Skinner discovered that using rewards to influence the rats' behaviour might also be used to influence human learning. Skinner's model was founded on the idea that punishment was used to stop the reactions to behaviours that were not intended and reinforcement was used to encourage desired actions or responses. This hypothesis demonstrated that both people and animals would repeat behaviours that produce favourable results while avoiding those that produce unfavourable one. 

 

The pigeon learned to peck the disc in exchange for the reward of food, which demonstrated how a successful experiment results in learned behaviour. Contrary to responder conditioning, where antecedent stimuli prompt reflexive behaviour, operant behaviour is just emitted and hence does not force its occurrence. These historical consequential contingencies ultimately lead to antecedent stimulus control. It consists of the regulating stimuli listed below. 

 

An antecedent stimulus that raises the likelihood that an organism will engage in a behaviour is known as a discriminative stimulus ( Sd ). In Skinner's lab, this happened in one instance. The pigeon learned in the past that when it pecked, food would be offered is the positive reinforcing stimuli, therefore, whenever the green light ( Sd ) came, it signalled the pigeon to perform the behaviour of pecking. Stimulus delta ( S - delta ) : An antecedent stimulus that instructs an organism not to engage in a behaviour because it has previously been suppressed or penalised. 

 

The instant stopping of a vehicle after the traffic signal turns red ( S - delta ) is one noteworthy example of this. However, if the person chooses to run the red light but then gets a speeding ticket or the positive punishment stimulus, this behaviour may not happen again when the S - delta is present. Respondent conditioning, also known as Pavlovian or classical conditioning, is a crucial behavior - analytic process that does not involve mental or other interior processes, even if operant conditioning dominates discussions of behavioural causes.

 

The most well - known use of classical conditioning is seen in Pavlov's research with dogs. The dog was initially given meat to eat ( an unconditioned stimulus, UCS, which naturally elicits a reaction that is uncontrolled ), which caused the dog to salivate more ( an unconditioned response, UCR, which denotes that a response is naturally triggered by an unconditioned stimulus, UCS ). The dog was then given a bell ring and food after that. 

 

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Bio Chemistry informatics aids in physical models. Our consultants aid via, informatics classes in greater noida, G. T. Road, Dadri, Gautam Buddha Nagar, NH-91, Greater Noida, Uttar Pradesh 203207, informatics classes in greater noida west, gautam budh nagar, uttar pradesh, informatics classes in noida extension, Pali Village, Pali, Greater Noida, Uttar Pradesh 203207, informatics classes in noida, G. T. Road, Dadri, NH-91, Greater Noida, Uttar Pradesh 203207, informatics classes in faridabad, haryana, Hero Honda chowk and informatics classes in gurgaon, haryana.

BioTechnology informatics extols Bio Chemistry. We coach via, informatics classes in gurugram, haryana, informatics classes in gurgaon gurugram, haryana, informatics classes in gurugram gurgaon, haryana, informatics classes in ghaziabad, uttar pradesh, informatics classes in Delhi, Ashraf Apartment, 2, Saqlain Rd, Jogabai Extension, Jamia Nagar, Okhla, New Delhi, Delhi 110025, informatics classes in Delhi NCR, informatics classes in New Delhi NCR, informatics classes in Alwar, Rajasthan, Desoola Village and informatics classes in Jaipur, Rajasthan.

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Sanskrit informatics show historical chemical inventions. We mentor via, informatics class in gurgaon gurugram, haryana, informatics class in gurugram gurgaon, haryana, informatics class in ghaziabad, uttar pradesh, informatics class in Delhi, Jamia Nagar, Okhla, New Delhi, Delhi 110025, informatics class in Delhi NCR, A- Block, New Friends Colony, New Delhi, Delhi 110025, informatics class in New Delhi NCR, Bharat Nagar, New Friends Colony, New Delhi, Delhi 110065, informatics class in Alwar, Rajasthan and informatics class in Jaipur, Rajasthan, Narayan Singh Circle.

Hindi informatics tell about Indian polity. Our guides aid via, informatics tuition near greater noida, Khandera Girirajpur, Uttar Pradesh 203207, informatics tuition near greater noida west, Megapolis, Beel Akbarpur, Uttar Pradesh 203207, informatics tuition near noida extension, Main, GT Rd, near VIET Engineering College, Megapolis, Beel Akbarpur, Uttar Pradesh 203207, informatics tuition near noida, Palla, Greater Noida, Uttar Pradesh 201308, informatics tuition near faridabad, haryana, Sector 22A and informatics tuition near gurgaon, haryana.

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Engineering Mathematics informatics is valuable. Our teachers aid via informatics tuitions near greater noida, Megapolis, Beel Akbarpur, UP 203207, informatics tuitions near greater noida west, VGI Rd, Beel Akbarpur, Uttar Pradesh 203207, informatics tuitions near noida extension, gautam budh nagar, uttar pradesh, informatics tuitions near noida, BEEL AKBARPUR, Dadri, UP 203207, informatics tuitions near faridabad, haryana, DLF Cyber Park, 3rd Floor, Tower A & B, Phase III, Udyog Vihar, Sector 20, Gurugram, Haryana 122008 and informatics tuitions near gurgaon, haryana.

Applied Arts informatics helps in Drawing & Painting. We train via informatics tuitions near gurugram, haryana, informatics tuitions near gurgaon gurugram, haryana, informatics tuitions near gurugram gurgaon, haryana, informatics tuitions near ghaziabad, uttar pradesh, informatics tuitions near Delhi, Hills Rd, C Block, Kailash Hills, East of Kailash, New Delhi, Delhi 110065, informatics tuitions near Delhi NCR, informatics tuitions near New Delhi NCR, informatics tuitions near Alwar, Rajasthan, Hanuman Circle and informatics tuitions near Jaipur, Rajasthan.

Microeconomics informatics provides economics data. Our team aids via informatics teacher near greater noida, NH34, Beel Akbarpur, Uttar Pradesh 203207, informatics teacher near greater noida west, gautam budh nagar, uttar pradesh, informatics teacher near noida extension, Palla Rd, Thapkhera Village, Greater Noida, Palla, Uttar Pradesh 203207, informatics teacher near noida, Bairangpur Urf Naibasti, Uttar Pradesh 203207, informatics teacher near faridabad, haryana, Udyog Vihar, Sector 19, Gurugram 122022 and informatics teacher near gurgaon, haryana.

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Accounting informatics builds macroeconomics data. Our classes aid via informatics teachers near greater noida, Ghaziabad - Bulandsher G. T Road, Dadri, NH 91, Greater Noida, Phase-2, Dadri, UP 203207, informatics teachers near greater noida west, Ghaziabad - Bulandshahr G.T. Road, P.O.- NH-91 G, Dadri Main Rd, Greater Noida, UP 203207, informatics teachers near noida extension, gautam budh nagar, uttar pradesh, informatics teachers near noida, gautam budh nagar, uttar pradesh, informatics teachers near faridabad, haryana and informatics teachers near gurgaon, haryana.

Botany informatics provides Biology data. We coach via informatics teachers near gurugram, haryana, informatics teachers near gurgaon gurugram, haryana, informatics teachers near gurugram gurgaon, haryana, informatics teachers near ghaziabad, uttar pradesh, informatics teachers near Delhi, Market, near C Block, Kailash Hills, East of Kailash, New Delhi, Delhi 110065, informatics tuitions near Delhi NCR, informatics teachers near New Delhi NCR, informatics teachers near Alwar, Rajasthan, Kathumar Tehsil and informatics teachers near Jaipur, Rajasthan.

Zoology informatics assists in BioTechnology Data. Our training classes aid via informatics classes near greater noida, Eastern Peripheral Expy, Bairangpur Urf Naibasti, UP 203207, informatics classes near greater noida west, Beel Akbarpur, UP 203207, informatics classes near noida extension, UP, informatics classes near noida, Ansal API SHUSHANT MEGAPOLIS, Greater Noida, Bodaki, Ramghad, Chmravli, UP 203207, informatics classes near faridabad, DLF Cyber City, DLF Phase 2, Sector 24, Gurugram, Haryana 122022 and informatics classes near gurgaon, haryana.

Civics informatics improves human relations. We mentor via informatics classes near gurugram, haryana, informatics classes near gurgaon gurugram, haryana, informatics classes near gurugram gurgaon, haryana, informatics classes near ghaziabad, uttar pradesh, informatics classes near Delhi, Block A, Dayanand Colony, Lajpat Nagar, New Delhi, Delhi 110024, informatics classes near Delhi NCR, informatics classes near New Delhi NCR, informatics classes near Alwar, Rajasthan, Kherli, Arua, Kathoomar and informatics classes near Jaipur, Rajasthan.

Web informatics uses AI, ICT, CCT, IPDS, IT & python coding. Our university aids via informatics class near greater noida, Megapolis, Chamrawali Ramgarh, Greater Noida, Uttar Pradesh 203207, informatics class near greater noida west, Datawli, Greater Noida, Uttar Pradesh 203207, informatics class near noida extension, Datawali, Dadri, Greater Noida, Uttar Pradesh 203207, informatics class near noida, Datawali, Uttar Pradesh 203207, informatics class near faridabad, haryana, DLF Cyber City Rd, DLF Phase 3, Sector 24, Gurugram, Haryana 122022 and informatics class near gurgaon, haryana.

Home science informatics helps develop life applications. We profess via informatics class near gurugram, haryana, informatics class near gurgaon gurugram, haryana, informatics class near gurugram gurgaon, haryana, informatics class near ghaziabad, uttar pradesh, informatics class near Delhi, Gurudwara Prakash Mohalla, New Delhi, informatics class near Delhi NCR, Iskcon Temple Rd, Sant Nagar, East of Kailash, New Delhi, Delhi 110065, informatics class near New Delhi NCR, informatics class near Alwar, Rajasthan, Shivaji Circle and informatics class near Jaipur, Rajasthan.

Data Sciences &