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What is Machine Learning

Chapter 5
Handbook of Research on Foundations and Applications of Intelligent Business Analytics
Is concerned with how computers can adapt to new circumstances and detect and extrapolate patterns and knowledge.
Published in Chapter:
A Process-Oriented Framework for Regulating Artificial Intelligence Systems
Andrew Stranieri (Federation University, Australia) and Zhaohao Sun (Papua New Guinea University of Technology, Papua New Guinea)
Copyright: © 2022 |Pages: 17
DOI: 10.4018/978-1-7998-9016-4.ch005
Abstract
Frameworks for the regulation of artificial intelligence (AI) systems are emerging; some are based on regulation theories; others are more technologically focused. Regulation of AI systems is likely to emerge in an ad-hoc, unstructured, and uncoordinated fashion that renders high level frameworks philosophically interesting but of limited benefit in practice. In this paper, the task of arriving at a collection of interventions that regulate an AI system is taken to be a process-oriented problem. It presents a process-oriented framework for the design of regulating systems by deliberating groups. It also discusses regulations of AI systems and responsibility, mechanisms and institutions, key elements for regulating AI systems. The proposed approach might facilitate research and development of responsible AI, explainable AI, and ethical AI for an ethical and inclusive digitized society. It also has implications for the development of e-business, e-services, and e-society.
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Chapter 4
Concerning the Integration of Machine Learning Content in Mechatronics Curricula
A branch of artificial intelligence. Summarizes different approaches to “learn” from data.
Published in Chapter: Concerning the Integration of Machine Learning Content in Mechatronics Curricula; From: Revolutionizing Education in the Age of AI and Machine Learning
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Chapter 5
Artificial Intelligence as a Tool for Hybrid Education
An area of artificial intelligence that focuses on the development of algorithms that can learn from and make predictions or decisions based on data.
Published in Chapter: Artificial Intelligence as a Tool for Hybrid Education; From: Promoting Quality Hybrid Learning Through Leadership and Educational Management
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Chapter 133
Internet of Things and Data Science in Healthcare
The field of study that deals with the construction of algorithms that can learn from data and make predictions on data.
Published in Chapter: Internet of Things and Data Science in Healthcare; From: Encyclopedia of Information Science and Technology, Fifth Edition
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Chapter 17
Comparison of Machine Learning Algorithms in Predicting the COVID-19 Outbreak
A sub-branch of artificial intelligence and makes data-based predictions using existing data.
Published in Chapter: Comparison of Machine Learning Algorithms in Predicting the COVID-19 Outbreak; From: Handbook of Research on Interdisciplinary Perspectives on the Threats and Impacts of Pandemics
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Chapter 3
Forecasting Techniques for the Pandemic Trend of COVID-19
A subject of artificial intelligence that aims at the task of computational algorithms, which allow machines to learning objects automatically through historical data.
Published in Chapter: Forecasting Techniques for the Pandemic Trend of COVID-19; From: Applied Big Data Analytics and Its Role in COVID-19 Research
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Chapter 2
A Survey on Emerging Cyber Crimes and Their Impact Worldwide
It is the science of training the machine how to learn using algorithms and mathematical models to improve their performance.
Published in Chapter: A Survey on Emerging Cyber Crimes and Their Impact Worldwide; From: Encyclopedia of Criminal Activities and the Deep Web
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Chapter 13
Utilizing Artificial Intelligence for Text Classification in Communication Sciences: Reliability of ChatGPT Models in Turkish Texts
Machine learning is a way for computers to learn from data without being explicitly programmed. It's like teaching a computer to recognize patterns and make decisions based on examples it's given. Instead of following fixed rules, machine learning algorithms adjust and improve their performance over time as they're exposed to more data. This helps them make predictions or decisions without being explicitly programmed for every possible scenario.
Published in Chapter: Utilizing Artificial Intelligence for Text Classification in Communication Sciences: Reliability of ChatGPT Models in Turkish Texts; From: Design and Development of Emerging Chatbot Technology
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Chapter 8
Introduction and Implementation of Machine Learning Algorithms in R
Helps machine to perform action without being explicitly coding.
Published in Chapter: Introduction and Implementation of Machine Learning Algorithms in R; From: Sentiment Analysis and Knowledge Discovery in Contemporary Business
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Chapter 10
U.S. Unemployment Rate Prediction by Economic Indices in the COVID-19 Pandemic Using Neural Network, Random Forest, and Generalized Linear Regression
An application of artificial intelligence on the basis of automatically learn and adapt from data. Also known as a data analysis method that utilize intrinsic self-learning system to help on making prediction.
Published in Chapter: U.S. Unemployment Rate Prediction by Economic Indices in the COVID-19 Pandemic Using Neural Network, Random Forest, and Generalized Linear Regression; From: Biomedical and Business Applications Using Artificial Neural Networks and Machine Learning
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Chapter 4
Applying Smart Security Model to Protect Client Services From the Threats of the Optical Network
Is a branch of artificial intelligence (AI) that offers the ability to learn and improve from experience automatically without using any programming.
Published in Chapter: Applying Smart Security Model to Protect Client Services From the Threats of the Optical Network; From: Advancements in Security and Privacy Initiatives for Multimedia Images
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Chapter 19
Image Processing and Machine Learning Techniques for the Segmentation of cDNA
It refers to the design and development of algorithms and techniques that allow computers to “learn”. The purpose of machine learning is to extract information from several types of data automatically, using computational and statistical methods.
Published in Chapter: Image Processing and Machine Learning Techniques for the Segmentation of cDNA; From: Handbook of Research on Advanced Techniques in Diagnostic Imaging and Biomedical Applications
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Chapter 181
Knowledge-Based Artificial Intelligence: Methods and Applications
Branch of Artificial Intelligence based on building models by learning directly from data while minimizing human effort.
Published in Chapter: Knowledge-Based Artificial Intelligence: Methods and Applications; From: Encyclopedia of Data Science and Machine Learning
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Chapter 11
The Application of Machine Learning for Predicting Global Seismicity
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Chapter 1
Evolution of Integration, Build, Test, and Release Engineering Into DevOps and to DevSecOps
A branch of Artificial Intelligence which involves writing programs that can identify patterns, learn from data, and make predictions.
Published in Chapter: Evolution of Integration, Build, Test, and Release Engineering Into DevOps and to DevSecOps; From: Tools and Techniques for Software Development in Large Organizations: Emerging Research and Opportunities
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Chapter 15
Artificial Intelligence and Robotics-Based Minimally Invasive Surgery: Innovations and Future Perceptions
A branch of computer science and artificial intelligence (AI) that focuses on emulating human learning and steadily improving accuracy through the use of data and algorithms.
Published in Chapter: Artificial Intelligence and Robotics-Based Minimally Invasive Surgery: Innovations and Future Perceptions; From: Contemporary Applications of Data Fusion for Advanced Healthcare Informatics
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Chapter 3
A Brief Overview on Intelligent Computing-Based Biological Data and Image Analysis
It is the ability of a system to learn or adapt something automatically from the environment, that is, experiments performed or the data being shown to the system and can make some decision in the unknown environment without any human intervention.
Published in Chapter: A Brief Overview on Intelligent Computing-Based Biological Data and Image Analysis; From: Applications of Advanced Machine Intelligence in Computer Vision and Object Recognition: Emerging Research and Opportunities
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Chapter 2
Artificial Intelligence Applied to Digital Marketing
Machine learning is a type of Artificial Intelligence (AI) that allows software applications to become more accurate in predicting outcomes without being explicitly programmed to do so. Machine learning algorithms use historical data as input to predict new output values.
Published in Chapter: Artificial Intelligence Applied to Digital Marketing; From: The Use of Artificial Intelligence in Digital Marketing: Competitive Strategies and Tactics
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Chapter 3
Recent and Emerging Technologies in Industrial IoT
Machine learning is a growing technology which enables computers to learn automatically from past data. Machine learning uses various algorithms for building mathematical models and making predictions using historical data or information.
Published in Chapter: Recent and Emerging Technologies in Industrial IoT; From: Opportunities and Challenges of Industrial IoT in 5G and 6G Networks
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Chapter 17
Predicting Human Actions Using a Hybrid of ReliefF Feature Selection and Kernel-Based Extreme Learning Machine
A system that studies the construction and working principle of algorithms that can be learned as structural function and can predict from data.
Published in Chapter: Predicting Human Actions Using a Hybrid of ReliefF Feature Selection and Kernel-Based Extreme Learning Machine; From: Handbook of Research on Predictive Modeling and Optimization Methods in Science and Engineering
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Chapter 17
Sustainable E-Waste Management: Present Situation, Emerging Solutions, and Future Trends
A field of artificial intelligence that uses statistical techniques to give computer systems the ability to learn.
Published in Chapter: Sustainable E-Waste Management: Present Situation, Emerging Solutions, and Future Trends; From: Sustainable Solutions for E-Waste and Development
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Chapter 11
Two Enhancement Levels for Male Fertility Rate Categorization Using Whale Optimization and Pegasos Algorithms
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Chapter 1
Applying Artificial Intelligence to Financial Investing
A method of automatically learning patterns from data in order to make future predictions.
Published in Chapter: Applying Artificial Intelligence to Financial Investing; From: Advanced Methodologies and Technologies in Business Operations and Management
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Chapter 40
An Introduction to Clustering Algorithms in Big Data
It is an application of artificial intelligence that incorporate the ability to learn automatically in the system.
Published in Chapter: An Introduction to Clustering Algorithms in Big Data; From: Encyclopedia of Information Science and Technology, Fifth Edition
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Chapter 6
Machine Learning With Avatar-Based Management of Sleptsov Net-Processor Platform to Improve Cyber Security
Is the use of artificial intelligence (AI) that provides systems with the capability to learn and automatically improve from experience (data) without being explicitly programmed.
Published in Chapter: Machine Learning With Avatar-Based Management of Sleptsov Net-Processor Platform to Improve Cyber Security; From: Machine Learning and Cognitive Science Applications in Cyber Security
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Chapter 5
Social Perspective of Suspicious Activity Detection in Facial Analysis: An ML-Based Approach for the Indian Perspective
Machine learning (ML) is the scientific study of algorithms and statistical models that computer systems use to perform a specific task without using explicit instructions, relying on patterns and inference instead. It is seen as a subset of artificial intelligence.
Published in Chapter: Social Perspective of Suspicious Activity Detection in Facial Analysis: An ML-Based Approach for the Indian Perspective; From: Artificial Intelligence Paradigms for Smart Cyber-Physical Systems
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Chapter 5
Ethical Rationality in AI: On the Prospect of Becoming a Full Ethical Agent
The process by which machines “learn” from vast amounts of data which they are fed. This technology is mostly associated with algorithms and artificial neural networks that are optimized so that they can combine data, detect patterns, and produce novel output at a speed highly superior to human-level intelligence.
Published in Chapter: Ethical Rationality in AI: On the Prospect of Becoming a Full Ethical Agent; From: Machine Law, Ethics, and Morality in the Age of Artificial Intelligence
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Chapter 28
Self-Driving Robotic Cars: Cyber Security Developments
Machine learning is described as a research area that deals with designing algorithms. An important feature of those algorithms is the automatic improvement of technical systems that rely on experience – the automatic improvement follows rules and measures pre-set by the human developer. With machine learning, we need clearly defined tasks, the accompanying metrics, and training data.
Published in Chapter: Self-Driving Robotic Cars: Cyber Security Developments; From: Handbook of Research on Cyber Crime and Information Privacy
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Chapter 22
Artificial Intelligence: Current Issues and Applications
The artificial intelligence discipline geared toward the technological development of human knowledge.
Published in Chapter: Artificial Intelligence: Current Issues and Applications; From: Handbook of Research on Manufacturing Process Modeling and Optimization Strategies
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Chapter 10
Development Trends in Robotization and Artificial Intelligence
The design and application of computer algorithms that automatically improve when using data.
Published in Chapter: Development Trends in Robotization and Artificial Intelligence; From: The Strategies of Informing Technology in the 21st Century
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Chapter 13
Artificial Intelligence for Innovative Customer Services in the Hospitality Industry of India
Machine learning is one of the building blocks of AI. The term refers to a process in which a machine, for example, a chatbot, is endowed with the capacity to learn automatically.
Published in Chapter: Artificial Intelligence for Innovative Customer Services in the Hospitality Industry of India; From: Leveraging AI and Emotional Intelligence in Contemporary Business Organizations
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Chapter 120
Hierarchical Neuro-Fuzzy Systems Part I
Concerned with the design and development of algorithms and techniques that allow computers to “learn”. The major focus of machine learning research is to automatically extract useful information from historical data, by computational and statistical methods
Published in Chapter: Hierarchical Neuro-Fuzzy Systems Part I; From: Encyclopedia of Artificial Intelligence
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Chapter 5
Machine Learning Application With Avatar-Based Management Security to Reduce Cyber Threat
Is a class of methods of artificial/natural intelligence, the characteristic feature of which is not a direct solution of the problem, but training in the process of applying solutions to a set of similar problems.
Published in Chapter: Machine Learning Application With Avatar-Based Management Security to Reduce Cyber Threat; From: Machine Learning and Cognitive Science Applications in Cyber Security
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Chapter 2
Intelligent Systems in Diverse Domains: Digital Transformation
is a branch of Artificial Intelligence, that receives input, and makes predictions for output, based on previous training on the datasets.
Published in Chapter: Intelligent Systems in Diverse Domains: Digital Transformation; From: Strengthening Industrial Cybersecurity to Protect Business Intelligence
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Chapter 9
e-WOM Analysis Methods
Machine learning is the application of algorithms and statistical models in order to perform a specific task automatically using artificial intelligence instead of explicitly providing instructions to the algorithm or model.
Published in Chapter: e-WOM Analysis Methods; From: Exploring the Power of Electronic Word-of-Mouth in the Services Industry
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Chapter 17
Sustainable and Autonomous Soil Irrigation: Agro Smart Solution
An approach to artificial intelligence (AI) that makes it possible for systems to learn and grow from data and experiences without being explicitly programmed, resulting in behavior that is more intelligent and adaptable.
Published in Chapter: Sustainable and Autonomous Soil Irrigation: Agro Smart Solution; From: Internet of Behaviors Implementation in Organizational Contexts
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Chapter 4
An IoT-Based Energy Meter for Energy Level Monitoring, Predicting, and Optimization
Machine learning is a next level of artificial intelligence that gives systems capability to learn without human intervention and improve from practice without any human programming. It targets on the program development; it can be able data access and data learning for themselves.
Published in Chapter: An IoT-Based Energy Meter for Energy Level Monitoring, Predicting, and Optimization; From: Challenges and Opportunities for the Convergence of IoT, Big Data, and Cloud Computing
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Chapter 160
Predicting Estimated Arrival Times in Logistics Using Machine Learning
Sub-field of Artificial Intelligence which comprises various methods that enable computer systems to extract patterns from data.
Published in Chapter: Predicting Estimated Arrival Times in Logistics Using Machine Learning; From: Encyclopedia of Data Science and Machine Learning
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Chapter 12
Artificial Intelligence and Supply Chain Management Application, Development, and Forecast
Computers with the ability to learn without being explicitly programmed, computers learn and solve programs.
Published in Chapter: Artificial Intelligence and Supply Chain Management Application, Development, and Forecast; From: Handbook of Research on Applied AI for International Business and Marketing Applications
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Chapter 1
Introduction to Artificial Intelligence
Is a section of AI that deals with the ability of a machine to learn for the set of experiences it has been exposed to.
Published in Chapter: Introduction to Artificial Intelligence; From: Handbook of Research on AI Methods and Applications in Computer Engineering
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Chapter 130
Credit Risk Assessment and Data Mining
Sub-area of artificial intelligence that includes techniques able to learn new concepts from a set of samples.
Published in Chapter: Credit Risk Assessment and Data Mining; From: Encyclopedia of Information Science and Technology, Second Edition
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Chapter 4
Ionization, Gadget Radiation Analysis, and Disease Control by Yajna: An Ancient Vedic Wisdom for Human Health Relevant Amidst Pandemic Threats
Machine learning (ML) is the study of computer algorithms that improve automatically through experience. It is seen as a subset of artificial intelligence. Machine learning algorithms build a mathematical model based on sample data, known as “training data”, in order to make predictions or decisions without being explicitly programmed to do so. Machine learning algorithms are used in a wide variety of applications, such as email filtering and computer vision, where it is difficult or infeasible to develop conventional algorithms to perform the needed tasks.
Published in Chapter: Ionization, Gadget Radiation Analysis, and Disease Control by Yajna: An Ancient Vedic Wisdom for Human Health Relevant Amidst Pandemic Threats; From: Handbook of Research on Interdisciplinary Studies on Healthcare, Culture, and the Environment
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Chapter 16
Use of Generative AI Tools to Facilitate Personalized Learning in the Flipped Classroom
It is an important part of artificial intelligence, in which machines learn and optimize behaviors by analyzing data generated by users’ activities.
Published in Chapter: Use of Generative AI Tools to Facilitate Personalized Learning in the Flipped Classroom; From: Transforming Education With Generative AI: Prompt Engineering and Synthetic Content Creation
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Chapter 42
Current Challenges in Intrusion Detection Systems
A research area of artificial intelligence, which is interested in developing algorithms to extract knowledge from the given data.
Published in Chapter: Current Challenges in Intrusion Detection Systems; From: Encyclopedia of Multimedia Technology and Networking, Second Edition
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Chapter 5
Autonomous Integrated Business Planning
Machine learning is a field of artificial intelligence that uses statistical techniques to understand the patterns behind the data, establish co-relation between those patterns and “learn” from data, without being explicitly programmed
Published in Chapter: Autonomous Integrated Business Planning; From: Digital Supply Chain, Disruptive Environments, and the Impact on Retailers
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Chapter 21
The Summers and Winters of Artificial Intelligence
AI programs that extract useful patterns from vast amounts of data and makes unforeseen predictions based on these data, without explicit programming.
Published in Chapter: The Summers and Winters of Artificial Intelligence; From: Encyclopedia of Information Science and Technology, Fourth Edition
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Chapter 10
Exploring the Possibilities of Artificial Intelligence and Big Data Techniques to Enhance Gamified Financial Services
A learning technique that gives machines the ability to learn without being explicitly programmed. It is seen as a subset of Artificial Intelligence.
Published in Chapter: Exploring the Possibilities of Artificial Intelligence and Big Data Techniques to Enhance Gamified Financial Services; From: Next-Generation Applications and Implementations of Gamification Systems
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Chapter 11
Ethical Considerations and Challenges in Neurodegenerative Diseases Using Machine Learning
Machine learning is a subset of artificial intelligence that enables systems to automatically learn and improve from experience without being explicitly programmed, using algorithms to recognize patterns and make predictions.
Published in Chapter: Ethical Considerations and Challenges in Neurodegenerative Diseases Using Machine Learning; From: Deep Learning Approaches for Early Diagnosis of Neurodegenerative Diseases
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Chapter 29
Variable Importance Evaluation for Machine Learning Tasks
Computational methods that are used in data mining tasks such as clustering, classification and prediction.
Published in Chapter: Variable Importance Evaluation for Machine Learning Tasks; From: Encyclopedia of Information Science and Technology, Third Edition
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Chapter 3
Humans Enter the Age of Avatarism
The scientific study of algorithms and statistical models that computer systems use to perform a specific task without using explicit instructions, relying on patterns and inference instead. Seen as a subset of artificial intelligence, machine learning algorithms build a mathematical model based on sample data, known as “training data”, in order to make predictions or decisions without being explicitly programmed to perform a task ( https://en.wikipedia.org/wiki/Machine_learning ).
Published in Chapter: Humans Enter the Age of Avatarism; From: Handbook of Research on the Global Impacts and Roles of Immersive Media
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Chapter 19
Recognizing Physical Activities using Wearable Devices
It is a scientific approach that can learn from data, machine learning algorithms build a model from training data which is used for predictions or decisions.
Published in Chapter: Recognizing Physical Activities using Wearable Devices; From: Handbook of Research on Innovations in the Diagnosis and Treatment of Dementia
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Chapter 207
Stochastic Programming and Value Based Decisions
A computational methodology that provides automatic means of improving programmed tasks from experience.
Published in Chapter: Stochastic Programming and Value Based Decisions; From: Encyclopedia of Business Analytics and Optimization
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Chapter 45
Automated Image Analysis Approaches in Histopathology
The automation of the classification of different regions in the image as objects.
Published in Chapter: Automated Image Analysis Approaches in Histopathology; From: Handbook of Research on Systems Biology Applications in Medicine
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Chapter 28
Artificial Intelligence for Sustainable Food Systems
A subfield of artificial intelligence that focuses on the development of algorithms and models that enable computers to automatically learn and improve from experience without being explicitly programmed. It involves the analysis of data patterns to make predictions, and decisions, or perform tasks without human intervention.
Published in Chapter: Artificial Intelligence for Sustainable Food Systems; From: Fostering Cross-Industry Sustainability With Intelligent Technologies
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Chapter 8
Topical Repute on Artificial Intelligence-Based Approaches in COVID-19 Supervision: Distinct Kingpin on Drug Re-Purposing Blueprint
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Chapter 289
Towards Low-Cost Energy Monitoring
The design and development of computer program based on statistics rules, which is used to learn facts from some data.
Published in Chapter: Towards Low-Cost Energy Monitoring; From: Encyclopedia of Information Science and Technology, Third Edition
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Chapter 5
How Artificial Intelligence (AI) is Transforming the User Experience in Digital Marketing
This means the use and development of computer systems that are able to learn and adapt without following explicit instructions, by using algorithms and statistical models to analyze and draw inferences from patterns in data.
Published in Chapter: How Artificial Intelligence (AI) is Transforming the User Experience in Digital Marketing; From: The Use of Artificial Intelligence in Digital Marketing: Competitive Strategies and Tactics
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Chapter 14
Artificial Intelligence a Driver for Digital Transformation
Is scientific study of algorithms and statistical models that perform various functions without having to be programmed by a human, and without using explicit instructions, relying on patterns and inference. ML algorithms build a mathematical model based on sample data, known as “training data”, in order to make predictions or decisions. Its application to business is referred as predictive analytics.
Published in Chapter: Artificial Intelligence a Driver for Digital Transformation; From: Handbook of Research on Digital Transformation and Challenges to Data Security and Privacy
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Chapter 34
User-Adapted Information Services
Machine learning is a subfield of Artificial Intelligence which provides algorithms for the discovery of relations or rules in large data sets. Machine learning leads to functions which can automatically classify or categorize objects based on their features. Inductive learning from labeled examples is the most well known application.
Published in Chapter: User-Adapted Information Services; From: Handbook of Research on Digital Libraries: Design, Development, and Impact
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Chapter 154
Artificial Intelligence and Machine Learning Innovation in SDGs
Is a subfield of artificial intelligence where machines are trained to imitate human intelligence, thereby being able to perform complex tasks.
Published in Chapter: Artificial Intelligence and Machine Learning Innovation in SDGs; From: Encyclopedia of Data Science and Machine Learning
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Chapter 8
The Internet of Things in the Corporate Environment: Cross-Industry Perspectives and Implementation Issues
A technology used to make a computer-controlled device, to observe, understand and learn a given situation and make future predictions like a human brain.
Published in Chapter: The Internet of Things in the Corporate Environment: Cross-Industry Perspectives and Implementation Issues; From: Handbook of Research on Digital Transformation, Industry Use Cases, and the Impact of Disruptive Technologies
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Chapter 33
Artificial Intelligence, Consumers, and the Experience Economy
Machine learning is the development and use of computer algorithms that automatically through experience and by training on data.
Published in Chapter: Artificial Intelligence, Consumers, and the Experience Economy; From: Encyclopedia of Data Science and Machine Learning
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Chapter 17
Machine Learning in the Catering Industry
Algorithms that allow machines to learn automatically.
Published in Chapter: Machine Learning in the Catering Industry; From: Encyclopedia of Data Science and Machine Learning
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Chapter 11
Time Series Forecasting in Retail Sales Using LSTM and Prophet
A branch of artificial intelligence that studies computational algorithms that adapt their parameters accordingly to a given training dataset.
Published in Chapter: Time Series Forecasting in Retail Sales Using LSTM and Prophet; From: Handbook of Research on Applied Data Science and Artificial Intelligence in Business and Industry
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Chapter 6
The Application of Big Data and Cloud Computing Among Smallholder Farmers in Sub-Saharan Africa
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Chapter 6
Artificial Intelligence in Innovation Labs: Map of Cases for the Public Sector
Is an artificial intelligence technique that allows to learn patterns and generate answers from the analysis of millions of data and records.
Published in Chapter: Artificial Intelligence in Innovation Labs: Map of Cases for the Public Sector; From: Handbook of Research on Applied Artificial Intelligence and Robotics for Government Processes
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Chapter 234
Value Based Decision Control for Complex Systems
A computational methodology that provides automatic means of improving programmed tasks from experience.
Published in Chapter: Value Based Decision Control for Complex Systems; From: Encyclopedia of Business Analytics and Optimization
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Chapter 11
Intelligent System for Predicting Healthcare Readmissions
Machine learning is a tool used in health care to help medical professionals care for patients and manage clinical data. It is an application of artificial intelligence, which involves programming computers to mimic how people think and learn.
Published in Chapter: Intelligent System for Predicting Healthcare Readmissions; From: Analyzing Current Digital Healthcare Trends Using Social Networks
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Chapter 5
Digital Transformation in Contemporary Organizations: Creativity and Innovation in the 4th Industrial Revolution
A system where relationship among input and output can be established through feature extraction and classification distinctly.
Published in Chapter: Digital Transformation in Contemporary Organizations: Creativity and Innovation in the 4th Industrial Revolution; From: Multidisciplinary Approaches in AI, Creativity, Innovation, and Green Collaboration
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Chapter 90
Machine Learning for Decision Support in the ICU
Machine learning is a kind of algorithm that provides systems the ability to automatically learn and improve from data without human intervention.
Published in Chapter: Machine Learning for Decision Support in the ICU; From: Encyclopedia of Data Science and Machine Learning
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Chapter 12
Making Smart Cities Smarter: Role of AI in Smart Cities Application
A field of artificial intelligence that uses statistical techniques to give computer systems the ability to learn.
Published in Chapter: Making Smart Cities Smarter: Role of AI in Smart Cities Application; From: Exploring Ethical Dimensions of Environmental Sustainability and Use of AI
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Chapter 7
The Future of Advertising: Influencing and Predicting Response Through Artificial Intelligence, Machine Learning, and Neuroscience
Is a method of data analysis that automates analytical model building. It is a branch of artificial intelligence based on the idea that systems can learn from data, identify patterns and make decisions with minimal human intervention.
Published in Chapter: The Future of Advertising: Influencing and Predicting Response Through Artificial Intelligence, Machine Learning, and Neuroscience; From: Handbook of Research on Applied Data Science and Artificial Intelligence in Business and Industry
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Chapter 13
Sentiment Analysis, Social Media, and Public Administration
It is a subfield of artificial intelligence often used in data mining that identifies rules and patterns of large data sets (NLP), based on the use of algorithms and application training through a training corpus.
Published in Chapter: Sentiment Analysis, Social Media, and Public Administration; From: Handbook of Research on Democratic Strategies and Citizen-Centered E-Government Services
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Chapter 30
A Machine Learning Approach to Data Cleaning in Databases and Data Warehouses
Machine learning is an area of artificial intelligence concerned with the development of techniques that allow computers to learn. Learning is the ability of the machine to improve its performance based on previous results.
Published in Chapter: A Machine Learning Approach to Data Cleaning in Databases and Data Warehouses; From: Handbook of Research on Fuzzy Information Processing in Databases
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Chapter 12
Evolving Cyber Threats, Combating Techniques, and Open Issues in Online Social Networks
Machine Learning is a subdivision of artificial intelligence. Machine learning is the study of computer algorithms which, through experience, automatically improve.
Published in Chapter: Evolving Cyber Threats, Combating Techniques, and Open Issues in Online Social Networks; From: Handbook of Research on Cyber Crime and Information Privacy
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Chapter 8
IoT-Based Health Risk Prediction by Collecting and Analyzing HIIT Data in Real Time Using Edge Computing
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Chapter 21
A Literature Review on Thyroid Hormonal Problems in Women Using Data Science and Analytics: Healthcare Applications
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Chapter 1
A Comparative Study of Machine Learning Techniques for Gesture Recognition Using Kinect
A subfield of computer science that evolved from the study of pattern recognition and computational learning theory in artificial intelligence.
Published in Chapter: A Comparative Study of Machine Learning Techniques for Gesture Recognition Using Kinect; From: Handbook of Research on Human-Computer Interfaces, Developments, and Applications
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Chapter 16
Data Science Process for Smart Cities
Supervised and unsupervised learning methods and techniques.
Published in Chapter: Data Science Process for Smart Cities; From: Latin American Women and Research Contributions to the IT Field
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Chapter 9
Smart System and Services Using Artificial Intelligence and Machine Learning Algorithms: Sky of AI
Without being specifically designed to do so, machine learning (ML), a form of artificial intelligence (AI), enables software systems to become better at making predictions about the future.
Published in Chapter: Smart System and Services Using Artificial Intelligence and Machine Learning Algorithms: Sky of AI; From: Applying Drone Technologies and Robotics for Agricultural Sustainability
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Chapter 12
Hybrid Intrusion Detection System for Smart Home Applications
It refers to a technique that automates building analytical models using algorithms to analyse large amounts of data. It is based on the idea that computer systems can learn by identifying patterns within data and make decisions with minimal human intervention.
Published in Chapter: Hybrid Intrusion Detection System for Smart Home Applications; From: Developing and Monitoring Smart Environments for Intelligent Cities
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Chapter 8
Automatic Image Captioning Using Different Variants of the Long Short-Term Memory (LSTM) Deep Learning Model
A subfield of Artificial Intelligence that enables systems to learn and improve based upon previous experience and data without the need of explicit programming.
Published in Chapter: Automatic Image Captioning Using Different Variants of the Long Short-Term Memory (LSTM) Deep Learning Model; From: Deep Learning Research Applications for Natural Language Processing
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Chapter 16
Machine Learning Approach to Search Query Classification
The area of artificial intelligence that studies the algorithms and processes that allow machines to learn. These algorithms use a combination of techniques to learn from examples, from prior knowledge, or from experience.
Published in Chapter: Machine Learning Approach to Search Query Classification; From: Handbook of Research on Web Log Analysis
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Chapter 5
Role of Artificial Intelligence in Cyber Security: A Useful Overview
Is a branch of Artificial Intelligence which focus on using data and applying algorithms to make predictions and classify data. Machine Learning broadly has three types of classification algorithms, supervised learning, unsupervised learning and reinforcement learning.
Published in Chapter: Role of Artificial Intelligence in Cyber Security: A Useful Overview; From: Information Security Practices for the Internet of Things, 5G, and Next-Generation Wireless Networks
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Chapter 14
The Pivotal Role of Edge Computing With Machine Learning and Its Impact on Healthcare
The major role played by the machine learning is to detect the disease through the image processing techniques, which could be very hard through the normal diagnosis. This disease could be anything like genetic disorders or cancers that is very difficult to be diagnosed at a very initial stage.
Published in Chapter: The Pivotal Role of Edge Computing With Machine Learning and Its Impact on Healthcare; From: Deep Neural Networks for Multimodal Imaging and Biomedical Applications
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Chapter 13
The Edgard Project: Towards a Discussion of Ethical Tech in Art and Design
A computer process utilized to improve itself using new data inputted in an analytical model.
Published in Chapter: The Edgard Project: Towards a Discussion of Ethical Tech in Art and Design; From: Smart Systems Design, Applications, and Challenges
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Chapter 12
Artificial Intelligence Applications for Event Management and Marketing
Refers to computer science techniques used to give machines the ability to learn without the need for explicit programming (Birer, 2020).
Published in Chapter: Artificial Intelligence Applications for Event Management and Marketing; From: Impact of ICTs on Event Management and Marketing
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Chapter 17
User Sentiment Prediction and Analysis for Payment App Reviews Using Supervised and Unsupervised Machine Learning Approaches
An area of artificial intelligence (AI) that includes the creation of algorithms and models that can learn from and anticipate or make choices based on data.
Published in Chapter: User Sentiment Prediction and Analysis for Payment App Reviews Using Supervised and Unsupervised Machine Learning Approaches; From: Handbook of Research on AI and Machine Learning Applications in Customer Support and Analytics
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Chapter 2
Interdisciplinary Application of Machine Learning, Data Science, and Python for Cricket Analytics
A subfield of artificial intelligence that involves the development of algorithms and models that enable computers to learn and improve from experience.
Published in Chapter: Interdisciplinary Application of Machine Learning, Data Science, and Python for Cricket Analytics; From: Advanced Interdisciplinary Applications of Machine Learning Python Libraries for Data Science
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Chapter 58
Understanding Machine Learning Concepts
It refers to a learning technique that gives machines the ability to learn without being explicitly programmed. It is seen as a subset of Artificial Intelligence.
Published in Chapter: Understanding Machine Learning Concepts; From: Encyclopedia of Data Science and Machine Learning
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Chapter 8
Artificial Intelligence and Machine Learning Algorithms
Machine learning (ML) is the branch of computer science that comes under the umbrella of Artificial Intelligence. ML deals with the learning of machines to perform various tasks that can be done better than human beings.
Published in Chapter: Artificial Intelligence and Machine Learning Algorithms; From: Challenges and Applications for Implementing Machine Learning in Computer Vision
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Chapter 8
Opportunities and Challenges of Using Big Data Applications in Institutions of Higher Learning Libraries and Research Institutions
An application of artificial intelligence that provides systems the ability to automatically learn and improve from experience without being explicitly programmed.
Published in Chapter: Opportunities and Challenges of Using Big Data Applications in Institutions of Higher Learning Libraries and Research Institutions; From: Big Data Applications for Improving Library Services
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Chapter 14
Data Mining Applications in Computer-Supported Collaborative Learning
Is a subfield of computer science and artificial intelligence that deals with the construction and study of systems that can learn from data, rather than follow only explicitly programmed instructions. Machine learning is employed in a range of computing tasks where designing and programming explicit rule-based algorithms is infeasible for a variety of reasons.
Published in Chapter: Data Mining Applications in Computer-Supported Collaborative Learning; From: Handbook of Research on Interactive Information Quality in Expanding Social Network Communications
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Chapter 10
Smart Agriculture Services Using Deep Learning, Big Data, and IoT (Internet of Things)
It is again a sub set of AI in which we classify the data with the help of input data set, ANN, SVM, Random Forest are some of the algorithm used in this case.
Published in Chapter: Smart Agriculture Services Using Deep Learning, Big Data, and IoT (Internet of Things); From: Smart Agricultural Services Using Deep Learning, Big Data, and IoT
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Chapter 62
Customer Analytics Using Sentiment Analysis and Net Promoter Score
The study of computer algorithms that can improve automatically through experience and by the use of data. It is seen as a part of artificial intelligence.
Published in Chapter: Customer Analytics Using Sentiment Analysis and Net Promoter Score; From: Encyclopedia of Data Science and Machine Learning
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Chapter 7
Analyzing and Predicting Learner Sentiment Toward Specialty Schools Using Machine Learning Techniques
A type of data analysis that automates the process of creating analytical models. It's a branch of artificial intelligence (AI) based on the idea that machines can learn from data, detect patterns, and make decisions with little or no human input.
Published in Chapter: Analyzing and Predicting Learner Sentiment Toward Specialty Schools Using Machine Learning Techniques; From: Applying Data Science and Learning Analytics Throughout a Learner’s Lifespan
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Chapter 11
Multi-Criteria Decision Making With Machine Learning for Vehicle Routing Problem
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Chapter 10
Artificial Intelligence Applications in Agricultural Sustainability: Enhancing Efficiency and Resilience
ML is a subset of AI that focuses on the development of algorithms and statistical models that allow computers to learn from and make predictions or decisions based on data without explicit programming.
Published in Chapter: Artificial Intelligence Applications in Agricultural Sustainability: Enhancing Efficiency and Resilience; From: Convergence of Cloud Computing, AI, and Agricultural Science
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Chapter 6
Analysis of Big Data
Machine learning is the science of computer algorithms which can learn and develop on their own with experience and data. It is considered to be a component of artificial intelligence.
Published in Chapter: Analysis of Big Data; From: Encyclopedia of Data Science and Machine Learning
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Chapter 15
Threat Detection in Cyber Security Using Data Mining and Machine Learning Techniques
The field of study that is concerned with given computers the ability to learn from their experience and environment without being explicitly programmed.
Published in Chapter: Threat Detection in Cyber Security Using Data Mining and Machine Learning Techniques; From: Modern Theories and Practices for Cyber Ethics and Security Compliance
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Chapter 385
Linguistic Indexing of Images with Database Mediation
An area of artificial intelligence that allows computers to apply rules and algorithms in a learning process. It overlaps with data mining and statistics and has wide applications in areas such as object recognition, computer vision, robot locomotion and bioinformatics.
Published in Chapter: Linguistic Indexing of Images with Database Mediation; From: Encyclopedia of Information Science and Technology, Second Edition
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Chapter 5
Understanding Data Analytics Is Good but Knowing How to Use It Is Better!
A method of designing a sequence of actions to solve a problem that optimizes automatically through experience and with limited or no human intervention.
Published in Chapter: Understanding Data Analytics Is Good but Knowing How to Use It Is Better!; From: Big Data Analytics for Entrepreneurial Success
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Chapter 5
Predictive Pioneers: Bridging the Mental Health Gap in Older Adults Through Advanced Digital Technologies
Artificial intelligence, known as machine learning, focuses on teaching computers new skills by exposing them to data and analyzing it statistically. The main goal is to build systems that can improve with time and do not need to be programmed to do anything.
Published in Chapter: Predictive Pioneers: Bridging the Mental Health Gap in Older Adults Through Advanced Digital Technologies; From: Using Machine Learning to Detect Emotions and Predict Human Psychology
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Chapter 9
The Role of Artificial Intelligence in Cyber Security
A facet of AI that focuses on algorithms, allowing machines to learn without being programmed and change when exposed to new data.
Published in Chapter: The Role of Artificial Intelligence in Cyber Security; From: Countering Cyber Attacks and Preserving the Integrity and Availability of Critical Systems
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Chapter 3
Fluctuation of Emotion in Stress and Different Coping Mechanisms During the COVID-19 Pandemic
Machine learning, a realm of artificial intelligence, empowers computers to learn and adapt, mirroring our own cognitive processes in the digital realm.
Published in Chapter: Fluctuation of Emotion in Stress and Different Coping Mechanisms During the COVID-19 Pandemic; From: Multidisciplinary Approach to Information Technology in Library and Information Science
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Chapter 92
Forecasting Techniques for Energy Optimization in Buildings
A branch of artificial intelligence related to the design of algorithms that learn dynamically from examples of their input variables to make estimations about the output ones.
Published in Chapter: Forecasting Techniques for Energy Optimization in Buildings; From: Encyclopedia of Information Science and Technology, Third Edition
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Chapter 14
Artificial Intelligence in Education: Current Insights and Future Perspectives
A field of artificial intelligence that uses statistical techniques to give computer systems the ability to learn.
Published in Chapter: Artificial Intelligence in Education: Current Insights and Future Perspectives; From: Handbook of Research on Learning in the Age of Transhumanism
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Chapter 5
A Survey on Intelligence Tools for Data Analytics
It refers to developing the ability in computers to use available data to train themselves automatically, and to learn from its own experiences without being explicitly programmed.
Published in Chapter: A Survey on Intelligence Tools for Data Analytics; From: Handbook of Research on Engineering, Business, and Healthcare Applications of Data Science and Analytics
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Chapter 2
Implementation of Different Machine Learning Projects Using Scikit-learn and Tensorflow Frameworks
It is the modeling of systems that make predictions by using mathematical and statistical processes on data.
Published in Chapter: Implementation of Different Machine Learning Projects Using Scikit-learn and Tensorflow Frameworks; From: Methodologies, Frameworks, and Applications of Machine Learning
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Chapter 11
Patient Data De-Identification: A Conditional Random-Field-Based Supervised Approach
A field of computer science that exploits the development of the algorithms for making a prediction on the data on the basis of its learning.
Published in Chapter: Patient Data De-Identification: A Conditional Random-Field-Based Supervised Approach; From: Handbook of Research on Applied Cybernetics and Systems Science
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Chapter 5
Multi-Criteria Decision-Making Techniques for Histopathological Image Classification
A field of information technology that has the ability to learn data insights by using statistical techniques.
Published in Chapter: Multi-Criteria Decision-Making Techniques for Histopathological Image Classification; From: Histopathological Image Analysis in Medical Decision Making
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Chapter 14
Machine Learning for Smart Tourism and Retail
The field in computer science which is concerned on the development of algorithms that can make computers learn from data.
Published in Chapter: Machine Learning for Smart Tourism and Retail; From: Handbook of Research on Big Data Clustering and Machine Learning
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Chapter 3
Behavioral Analytics of Consumer Complaints
is a sort of data analysis that uses artificial intelligence to automate the process of developing analytical models. It's a branch of AI based on the premise that robots can learn from data, discover patterns, and make judgments with little or no human involvement.
Published in Chapter: Behavioral Analytics of Consumer Complaints; From: AI-Driven Intelligent Models for Business Excellence
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Chapter 3
Humans Need Not Apply: Artificial Intelligence, Robotics, Machine Learning, and the Future of Work
Is a branch of AI, which encompasses the study of algorithms and statistical models used by computers to execute specific tasks without using explicit commands.
Published in Chapter: Humans Need Not Apply: Artificial Intelligence, Robotics, Machine Learning, and the Future of Work; From: Anywhere Working and the Future of Work
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Chapter 5
Investing in Artificial Intelligence (AI) for Business Leaders to Enhance Decision-Making
A type of artificial intelligence (AI) that allows applications to learn by practice and predict outcomes with accuracy.
Published in Chapter: Investing in Artificial Intelligence (AI) for Business Leaders to Enhance Decision-Making; From: Complex AI Dynamics and Interactions in Management
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Chapter 9
The Role of Big Data Analytics in Drug Discovery and Vaccine Development Against COVID-19
A subject of artificial intelligence that aims at the task of computational algorithms, which allow machines to learning objects automatically through historical data.
Published in Chapter: The Role of Big Data Analytics in Drug Discovery and Vaccine Development Against COVID-19; From: Applied Big Data Analytics and Its Role in COVID-19 Research
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Chapter 1
Contemporary Imagetics and Post-Images in Digital Media Art: Inspirational Artists and Current Trends (1948-2020)
Computer algorithms that use data to train and automatically improve over experience.
Published in Chapter: Contemporary Imagetics and Post-Images in Digital Media Art: Inspirational Artists and Current Trends (1948-2020); From: Multidisciplinary Perspectives on New Media Art
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Chapter 12
Improving Customer Experience Using Sentiment Analysis in E-Commerce
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Chapter 3
Automated Essay Scoring Using Deep Learning Algorithms
A rising area in computer science, where the computer systems are programmed to learn information from rich data sets to produce reliable results to a given problem.
Published in Chapter: Automated Essay Scoring Using Deep Learning Algorithms; From: Handbook of Research on Modern Educational Technologies, Applications, and Management
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Chapter 10
Prediction and Analysis of Financial Crises Using Machine Learning
A field of artificial intelligence that involves the development of algorithms and models that enable computers to learn and make predictions or decisions without explicit programming.
Published in Chapter: Prediction and Analysis of Financial Crises Using Machine Learning; From: Advancement in Business Analytics Tools for Higher Financial Performance
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Chapter 7
The Development of Small-Medium Enterprises (SMEs) and the Role of Digital Ecosystems During the COVID-19 Pandemic: A Case of Indonesia
Machine learning is the study of computer algorithms that improve automatically through experience and by the use of data. It is seen as a part of artificial intelligence.
Published in Chapter: The Development of Small-Medium Enterprises (SMEs) and the Role of Digital Ecosystems During the COVID-19 Pandemic: A Case of Indonesia; From: Handbook of Research on Current Trends in Asian Economics, Business, and Administration
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Chapter 5
Redefining the Meaning of Learning
A subset of AI that leverages computer algorithms that improve from one iteration to the other through the analysis and processing of data.
Published in Chapter: Redefining the Meaning of Learning; From: Handbook of Research on Future of Work and Education: Implications for Curriculum Delivery and Work Design
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Chapter 6
A Hybrid Classification Algorithm and Its Application on Four Real-World Data Sets
The scientific study of using computer systems and improving it to be able to learn and adapt without using an explicit instruction.
Published in Chapter: A Hybrid Classification Algorithm and Its Application on Four Real-World Data Sets; From: Advanced Bioinspiration Methods for Healthcare Standards, Policies, and Reform
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Chapter 2
Machine Learning in Computer Vision
A part of the artificial intelligence domain. It consists of various algorithms which can be used to analyze data and get some insights from the data.
Published in Chapter: Machine Learning in Computer Vision; From: Applications of Machine Learning and Artificial Intelligence in Education
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Chapter 15
Comparative Study on ASD Identification Using Machine and Deep Learning
Is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed.
Published in Chapter: Comparative Study on ASD Identification Using Machine and Deep Learning; From: Interdisciplinary Approaches to Altering Neurodevelopmental Disorders
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Chapter 5
Monitoring, Predicting, and Optimizing Energy Consumptions: A Goal Toward Global Sustainability
Mechanisms that use datasets to find patterns and correlations in order to build models which will be applied to new data in order to predict its outcomes.
Published in Chapter: Monitoring, Predicting, and Optimizing Energy Consumptions: A Goal Toward Global Sustainability; From: Smart Systems Design, Applications, and Challenges
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Chapter 107
Using Machine Learning to Extract Insights From Consumer Data
An approach to derive computer algorithms and statistical models that can learn to improve their performance based on use of data, without explicit instructions (data-generative models).
Published in Chapter: Using Machine Learning to Extract Insights From Consumer Data; From: Encyclopedia of Data Science and Machine Learning
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Chapter 15
User Experience Measurement: Recent Practice of E-Businesses
Application of AI that makes systems to instinctively learn and get better from experience without the use of explicit instructions.
Published in Chapter: User Experience Measurement: Recent Practice of E-Businesses; From: Handbook of Research on User Experience in Web 2.0 Technologies and Its Impact on Universities and Businesses
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Chapter 8
Deep Learning and Sustainable Telemedicine
Machine learning is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. Machine learning focuses on the development of computer programs that can access data and use it learn for themselves.
Published in Chapter: Deep Learning and Sustainable Telemedicine; From: Impacts of Information Technology on Patient Care and Empowerment
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Chapter 9
Crow-ENN: An Optimized Elman Neural Network with Crow Search Algorithm for Leukemia DNA Sequence Classification
Machine learning is a subfield of AI, which is concerned with designing and development of computer algorithms which get improved with experience.
Published in Chapter: Crow-ENN: An Optimized Elman Neural Network with Crow Search Algorithm for Leukemia DNA Sequence Classification; From: Mobile Devices and Smart Gadgets in Medical Sciences
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Chapter 1
A Machine Learning Approach to Classify the Telecommunication Customers Based on Their Profitability
Machine learning (ML) is a sort of artificial intelligence (AI) that allows the software to improve its accuracy at predicting outcomes without being explicitly programmed to do so.
Published in Chapter: A Machine Learning Approach to Classify the Telecommunication Customers Based on Their Profitability; From: Social Customer Relationship Management (Social-CRM) in the Era of Web 4.0
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Chapter 11
Educational Software Based on Matlab GUIs for Neural Networks Courses
A subfield of computer science and artificial intelligence which aims to develop techniques that allow machines to solve problems automatically. This learning is done by generalizing problems based on training data.
Published in Chapter: Educational Software Based on Matlab GUIs for Neural Networks Courses; From: Handbook of Research on Computational Simulation and Modeling in Engineering
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Chapter 4
Opportunities and Challenges of Big Data in Healthcare
The subfield of artificial intelligence that uses learning algorithms in handling problems.
Published in Chapter: Opportunities and Challenges of Big Data in Healthcare; From: Handbook of Research on Healthcare Administration and Management
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Chapter 1
What Is Open Source Software (OSS) and What Is Big Data?
A process that gives machine the ability to learn without being explicitly programmed.
Published in Chapter: What Is Open Source Software (OSS) and What Is Big Data?; From: Open Source Software for Statistical Analysis of Big Data: Emerging Research and Opportunities
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Chapter 3
Android-Based Skin Cancer Recognition System Using Convolutional Neural Network
It is the modeling of systems that make predictions by using mathematical and statistical processes on data.
Published in Chapter: Android-Based Skin Cancer Recognition System Using Convolutional Neural Network; From: Diagnostic Applications of Health Intelligence and Surveillance Systems
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Chapter 8
Smart Diagnostics of COVID-19 With Data-Driven Approaches
A subject of artificial intelligence that aims at the task of computational algorithms, which allow machines to learning objects automatically through historical data.
Published in Chapter: Smart Diagnostics of COVID-19 With Data-Driven Approaches; From: Applied Big Data Analytics and Its Role in COVID-19 Research
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Chapter 7
Revolutionizing Diabetic Retinopathy Diagnostics and Therapy Through Artificial Intelligence: A Smart Vision Initiative
A subset of AI that involves the development of algorithms that can learn and make predictions or decisions based on data. This technology is used in AI-driven diagnostic tools for diabetic retinopathy.
Published in Chapter: Revolutionizing Diabetic Retinopathy Diagnostics and Therapy Through Artificial Intelligence: A Smart Vision Initiative; From: Transformative Approaches to Patient Literacy and Healthcare Innovation
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Chapter 7
Beyond Pandemic: Accelerating Artificial Intelligence and Financial Analytics
The use of computer systems that can learn and adapt without following instructions via algorithms and statistical models to analyze the patterns that occur in the data.
Published in Chapter: Beyond Pandemic: Accelerating Artificial Intelligence and Financial Analytics; From: Economic Impact and Recovery Following a Global Health Crisis
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Chapter 392
Machine Learning
The programming of computers to optimize a performance criterion using example data or past experience.
Published in Chapter: Machine Learning; From: Encyclopedia of Information Science and Technology, Second Edition
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Chapter 2
What the 3Vs Acronym Didn't Put Into Perspective?
A method of designing a sequence of actions to solve a problem that optimizes automatically through experience and with limited or no human intervention.
Published in Chapter: What the 3Vs Acronym Didn't Put Into Perspective?; From: Big Data Analytics for Entrepreneurial Success
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Chapter 14
Comprehending Algorithmic Bias and Strategies for Fostering Trust in Artificial Intelligence
Machine learning, a specialised domain within the realm of artificial intelligence (AI) and computer science, is dedicated to the utilisation of data and algorithms in order to replicate the learning process observed in humans. Through this iterative approach, the system's precision is gradually enhanced ( IBM, n.d. ).
Published in Chapter: Comprehending Algorithmic Bias and Strategies for Fostering Trust in Artificial Intelligence; From: Digital Technologies, Ethics, and Decentralization in the Digital Era
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Chapter 5
Artificial Intelligence and Marketing: Progressive or Disruptive Transformation? Review of the Literature
Machine learning is a subfield of artificial intelligence that focuses on the development of algorithms and statistical models that allow computers to learn from and make predictions or decisions based on data, without being explicitly programmed. In machine learning, an algorithm is trained on a large dataset and uses that data to make predictions or decisions based on new, unseen data. The goal of machine learning is to build models that can automatically improve their accuracy over time, by continuously learning from the data they process.
Published in Chapter: Artificial Intelligence and Marketing: Progressive or Disruptive Transformation? Review of the Literature; From: Confronting Security and Privacy Challenges in Digital Marketing
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Chapter 157
Analysis of Large-Scale OMIC Data Using Self Organizing Maps
Branch of artificial intelligence addressing algorithms which derive knowledge in terms of patterns from empirical ‘input’ data. Those patterns can be utilized to characterize and make predictions on previously unknown data.
Published in Chapter: Analysis of Large-Scale OMIC Data Using Self Organizing Maps; From: Encyclopedia of Information Science and Technology, Third Edition
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Chapter 12
Mind Uploading in Artificial Intelligence
A branch of artificial intelligence dealing with computer systems that learn and adjust the system using algorithms and statistical analysis.
Published in Chapter: Mind Uploading in Artificial Intelligence; From: Philosophy of Artificial Intelligence and Its Place in Society
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Chapter 9
Animal Activity Recognition From Sensor Data Using Ensemble Learning
The application of intelligent algorithms that teach computers to create analytical models.
Published in Chapter: Animal Activity Recognition From Sensor Data Using Ensemble Learning; From: Emerging Trends in IoT and Integration with Data Science, Cloud Computing, and Big Data Analytics
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Chapter 14
Intelligent Big Data Analytics in Health
Basically refers to the techniques for extracting useful information from hidden patterns. It can be defined a system consist of many methods that learn and improve from data.
Published in Chapter: Intelligent Big Data Analytics in Health; From: Early Detection of Neurological Disorders Using Machine Learning Systems
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Chapter 2
Total Variation Applications in Computer Vision
It is concerned with the study of pattern recognition as well as computational learning in artificial intelligence, exploring the structure and studying algorithms that can infer knowledge from and formulate predictions about data. Such algorithms work by building a model from known inputs so as to craft data-driven predictions or decisions, instead of pursuing a predetermined program.
Published in Chapter: Total Variation Applications in Computer Vision; From: Handbook of Research on Emerging Perspectives in Intelligent Pattern Recognition, Analysis, and Image Processing
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Chapter 3
A Systematic Bibliometric Literature Review on Data Science in Marketing
Is a field of inquiry devoted to understanding and building methods that 'learn', that is, methods that leverage data to improve performance on some set of tasks.
Published in Chapter: A Systematic Bibliometric Literature Review on Data Science in Marketing; From: Enhancing Business Communications and Collaboration Through Data Science Applications
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Chapter 6
Intelligent Management and Efficient Operation of Big Data
It is a type of Artificial Intelligence (AI) that provides computers with the ability to learn without being explicitly programmed. Machine learning explores the construction and study of algorithms that can learn from and make predictions on data.
Published in Chapter: Intelligent Management and Efficient Operation of Big Data; From: Handbook of Research on Trends and Future Directions in Big Data and Web Intelligence
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Chapter 9
Sentiment Mining: A Data-Driven Approach for Optimizing Digital Marketing Strategies
Machine learning is like teaching computers to learn from data and make decisions or predictions without being explicitly programmed. It's a way for computers to get smarter and improve themselves by analyzing information and patterns. Essentially, it's about teaching machines to learn from examples and data, so they can perform tasks or make choices on their own.
Published in Chapter: Sentiment Mining: A Data-Driven Approach for Optimizing Digital Marketing Strategies; From: The Use of Artificial Intelligence in Digital Marketing: Competitive Strategies and Tactics
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Chapter 12
A Comprehensive Analysis of Data Mining Tools for Biomedical Data Classification: Assessing Strengths, Weaknesses, and Future Directions
A field of computer science related to artificial intelligence that involves algorithms that enable machines to learn from data.
Published in Chapter: A Comprehensive Analysis of Data Mining Tools for Biomedical Data Classification: Assessing Strengths, Weaknesses, and Future Directions; From: Methodologies, Frameworks, and Applications of Machine Learning
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Chapter 157
In-Memory Analytics
The science of developing techniques to give the computer inference and deduction capabilities to achieve diverse processing tasks autonomously.
Published in Chapter: In-Memory Analytics; From: Encyclopedia of Information Science and Technology, Fourth Edition
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Chapter 10
The Effects of Artificial Intelligence (AI) on Marketing
The process of algorithms that generate other algorithms to solve them using Artificial Intelligence.
Published in Chapter: The Effects of Artificial Intelligence (AI) on Marketing; From: Complex AI Dynamics and Interactions in Management
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Chapter 18
Using Graph Neural Network to Enhance Quality of Service Prediction
Is a branch of artificial intelligence and computer science that focuses on using data and algorithms to imitate how humans learn and improve their accuracy ( Janiesch et al., 2021 ).
Published in Chapter: Using Graph Neural Network to Enhance Quality of Service Prediction; From: Handbook of Research on AI Methods and Applications in Computer Engineering
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Chapter 9
Epileptic Seizure Detection Using Machine Learning Techniques
An area of techniques and algorithms that learn from data and perform a particular task.
Published in Chapter: Epileptic Seizure Detection Using Machine Learning Techniques; From: Diagnostic Applications of Health Intelligence and Surveillance Systems
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Chapter 11
Insulin DNA Sequence Classification Using Levy Flight Bat With Back Propagation Algorithm
Machine learning is the sub field of Artificial intelligence which is a huge and multipurpose field in the modern technological world. Machine learning is related with the development and design of the computational system that can adopt themselves and learn. In machine learning the computational system learns on the basis of the training data.
Published in Chapter: Insulin DNA Sequence Classification Using Levy Flight Bat With Back Propagation Algorithm; From: Mobile Devices and Smart Gadgets in Medical Sciences
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Chapter 46
Using Custom-Built, Small-Scale Educational Solutions to Teach Qualitative Research Literacy: No Code, Code, and Complex Applications
Computer algorithms which improve through the use of data, without following explicit instructions. Part of artificial intelligence.
Published in Chapter: Using Custom-Built, Small-Scale Educational Solutions to Teach Qualitative Research Literacy: No Code, Code, and Complex Applications; From: Handbook of Research on Acquiring 21st Century Literacy Skills Through Game-Based Learning
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Chapter 3
Writing Machine for Blind People
Machine learning is a next level of artificial intelligence that gives systems capability to learn without human intervention and improve from practice without any human programming. It targets on the program development, it can be able data access and data learning for themselves.
Published in Chapter: Writing Machine for Blind People; From: Assistive Technologies for Differently Abled Students
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Chapter 9
Exploring Public Perceptions of COVID-19 Vaccine Adverse Effects Through Social Media Analysis
With the help of ML, which uses learning and ever expanding the experience to understand issue complexity and the requirement for adaptation, computers can accomplish difficult tasks without human interaction ( Zhang, 2020 ). ML models can be categorised into ten categories based on how an algorithm is trained and the availability of the result during training. These include reinforcement, evolutionary, ensemble, artificial neural networks, instance-based, semi-supervised, unsupervised, supervised, and hybrid learning ( Alzubi et al., 2018 ).
Published in Chapter: Exploring Public Perceptions of COVID-19 Vaccine Adverse Effects Through Social Media Analysis; From: Handbook of Research on Advancements of Contactless Technology and Service Innovation in Library and Information Science
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Chapter 8
Usage of Auxiliary Systems and Artificial Intelligence in Home-Based Rehabilitation: A Review
The application and development of computer systems that can learn and adapt themselves by analyzing and drawing inferences from data patterns, utilizing algorithms and statistical models, without explicit human instructions.
Published in Chapter: Usage of Auxiliary Systems and Artificial Intelligence in Home-Based Rehabilitation: A Review; From: Exploring the Convergence of Computer and Medical Science Through Cloud Healthcare
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Chapter 1
Adopting Artificial Intelligence in Healthcare: A Narrative Review
Is the use of data, software, and algorithms to enhance the ability of a machine to imitate human behavior.
Published in Chapter: Adopting Artificial Intelligence in Healthcare: A Narrative Review; From: The Use of Artificial Intelligence in Digital Marketing: Competitive Strategies and Tactics
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Chapter 20
Library Engagement With Emerging Technologies in Research and Learning
A sub-discipline of artificial intelligence (AI) that uses collections of examples to train software to recognize patterns, and to act on that recognition.
Published in Chapter: Library Engagement With Emerging Technologies in Research and Learning; From: Handbook of Research on Facilitating Collaborative Learning Through Digital Content and Learning Technologies
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Chapter 4
A Modified Stacking Ensemble Machine Learning Algorithm Using Genetic Algorithms
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Chapter 10
An Intelligent Machine-Driven Perspective to Archaeological Pottery Reassembly
The science of getting computers to learn and improve their learning over time in an accurate and automatic manner, by giving them data and information.
Published in Chapter: An Intelligent Machine-Driven Perspective to Archaeological Pottery Reassembly; From: Encyclopedia of Information Science and Technology, Fifth Edition
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Chapter 5
Applied Intelligence for Mental Health Detection: ManoVaidya – A Mental Health Therapist
Machine learning is a data processing tool that automates the construction of analytical models. It is an artificial intelligence division that assumes that systems should learn from their input, recognize trends, and determine with little human interference.
Published in Chapter: Applied Intelligence for Mental Health Detection: ManoVaidya – A Mental Health Therapist; From: Handbook of Research on Applied Intelligence for Health and Clinical Informatics
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Chapter 65
Algorithms and Bias
A process where a computer program “learns” from a data set.
Published in Chapter: Algorithms and Bias; From: Encyclopedia of Organizational Knowledge, Administration, and Technology
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Chapter 2
Application of Machine Learning Algorithms to the IoE: A Survey
Mechanisms that use datasets to find patterns and correlations in order to build models which will be applied to new data in order to predict its outcomes.
Published in Chapter: Application of Machine Learning Algorithms to the IoE: A Survey; From: Harnessing the Internet of Everything (IoE) for Accelerated Innovation Opportunities
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Chapter 27
Artificial Intelligence and K-12: How to Explain?
An application of artificial intelligence that aims to teach computers how to learn and act without being explicitly programmed.
Published in Chapter: Artificial Intelligence and K-12: How to Explain?; From: Handbook of Research on Teaching With Virtual Environments and AI
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Chapter 12
Enhancing Active Learning Pedagogy through Online Collaborative Learning
An artificial intelligence discipline geared towards the development of computer programs which learns how to perform a task through self-training or experience and they are able to improve their performance and handle new tasks ( Mitchell T., 1997 ).
Published in Chapter: Enhancing Active Learning Pedagogy through Online Collaborative Learning; From: Handbook of Research on Active Learning and the Flipped Classroom Model in the Digital Age
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Chapter 35
A Hybrid Computational Intelligence Algorithm to Transform Traditional IPC Into a Smart Camera
A branch of Artificial Intelligence that learns from data and automates pattern recognition, image identification and decision making with minimum human intervention.
Published in Chapter: A Hybrid Computational Intelligence Algorithm to Transform Traditional IPC Into a Smart Camera; From: Handbook of Research on Innovation and Development of E-Commerce and E-Business in ASEAN
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Chapter 105
The Artificial Intelligence in the Sphere of the Administrative Law
It is an application of artificial intelligence that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. AU48: Reference appears to be out of alphabetical order. Please check
Published in Chapter: The Artificial Intelligence in the Sphere of the Administrative Law; From: Encyclopedia of Data Science and Machine Learning
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Chapter 119
Intelligent Systems to Support Human Decision Making
Ability of a machine to improve its performance through learning.
Published in Chapter: Intelligent Systems to Support Human Decision Making; From: Encyclopedia of Business Analytics and Optimization
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Chapter 13
Strengthening of Financial Profiles in Latin America: From the Lack of Interest to the Proposal of a Web Platform for Remote Advice
It is an area of artificial intelligence aimed to develop algorithms to give computers the capabilities learn to solve some types of problems from the data provided.
Published in Chapter: Strengthening of Financial Profiles in Latin America: From the Lack of Interest to the Proposal of a Web Platform for Remote Advice; From: Handbook of Research on Applied Artificial Intelligence and Robotics for Government Processes
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Chapter 7
Application of Deep Learning for EEG
Machine learning is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed.
Published in Chapter: Application of Deep Learning for EEG; From: Handbook of Research on Advancements of Artificial Intelligence in Healthcare Engineering
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Chapter 18
What Is Deep Learning and How Has It Helped the COVID-19 Pandemic?
A sub-field of artificial intelligence that aims to develop models/systems that can learn from data.
Published in Chapter: What Is Deep Learning and How Has It Helped the COVID-19 Pandemic?; From: Handbook of Research on Interdisciplinary Perspectives on the Threats and Impacts of Pandemics
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Chapter 2
Pattern Recognition by IoT Systems of Machine Learning
The application of statistical models and algorithms to analyse data patterns and draw inferences, allowing computer systems to adapt and learn without explicit instructions.
Published in Chapter: Pattern Recognition by IoT Systems of Machine Learning; From: Handbook of Research on Machine Learning-Enabled IoT for Smart Applications Across Industries
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Chapter 8
XHAC: Explainable Human Activity Classification From Sensor Data
The utilization of algorithms to develop meaningful models by training the computers.
Published in Chapter: XHAC: Explainable Human Activity Classification From Sensor Data; From: Emerging Trends in IoT and Integration with Data Science, Cloud Computing, and Big Data Analytics
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Chapter 6
Transforming Urban Slums: Pathway to Functionally Intelligent Cities in Developing Countries
A process of machine modelling of some behavior based on the accumulation of information that best describes the required outcome. In the detection of slums, machine learning derived from remote images of known slums can identify other slums.
Published in Chapter: Transforming Urban Slums: Pathway to Functionally Intelligent Cities in Developing Countries; From: Developing and Monitoring Smart Environments for Intelligent Cities
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Chapter 1
Integrating Knowledge-Driven and Data-Driven Methodologies for an Efficient Clinical Decision Support System
A branch of artificial intelligence that provides systems the ability to learn and improve from their experience without being explicitly programmed.
Published in Chapter: Integrating Knowledge-Driven and Data-Driven Methodologies for an Efficient Clinical Decision Support System; From: Diverse Perspectives and State-of-the-Art Approaches to the Utilization of Data-Driven Clinical Decision Support Systems
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Chapter 16
Hybrid Computational Intelligence
A branch of artificial intelligence which concerns the construction and study of systems that can learn from data.
Published in Chapter: Hybrid Computational Intelligence; From: Encyclopedia of Information Science and Technology, Third Edition
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Chapter 63
Learning Classifiers from Distributed Data Sources
A key objective of Machine Learning is to design and analyze algorithms that are able to improve the performance at some task through experience. A machine learning system is specified by several components: (a) Learner – an algorithm or a computer program that is able to use the experience to improve its performance; (b) Task – a description of the task that the learner is trying to accomplish (e.g., learn a concept, a function, etc.); (c) Experience – specification of the information that the learner uses to perform the learning; (d) Background knowledge – the information that the learner has about the task before the learning process (e.g., ”simple” answers are preferable over “complex” answers); (e) Performance Criteria – measure the quality of the learning output in terms of accuracy, simplicity, efficiency, and so forth.
Published in Chapter: Learning Classifiers from Distributed Data Sources; From: Handbook of Research on Innovations in Database Technologies and Applications: Current and Future Trends
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Chapter 4
A Primer for Developing Computer-Mediated Solutions for the Modern Workforce: Using Artificial Intelligence for Situationally Aware Human-Computer Interaction
A type of computing system in which algorithms learn as they process data within a given context. These systems then use that derived knowledge to deepen their level of understanding of what is happening within the system.
Published in Chapter: A Primer for Developing Computer-Mediated Solutions for the Modern Workforce: Using Artificial Intelligence for Situationally Aware Human-Computer Interaction; From: Handbook of Research on New Media, Training, and Skill Development for the Modern Workforce
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Chapter 28
Creating Social Technologies to Assist and Understand Social Interactions
Machine Learning: This subfield of artificial intelligence is concerned with the design, analysis, implementation and applications of programs that learn from experience. The discovery of general rules from large data sets using computational and statistical methods is an important application area. Such large data sets can, for example, be corpora that contain audio and video recorded human-human or human-computer nteraction.
Published in Chapter: Creating Social Technologies to Assist and Understand Social Interactions; From: Handbook of Research on Socio-Technical Design and Social Networking Systems
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Chapter 11
Preference Reversal Under Vulnerability: An Application of Neural Networks in Mexican Family Firms
Computational field that aims to the development of techniques for making machines to learn.
Published in Chapter: Preference Reversal Under Vulnerability: An Application of Neural Networks in Mexican Family Firms; From: Handbook of Research on the Strategic Management of Family Businesses
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Chapter 2
Intelligent Manufacturing Systems Driven by Artificial Intelligence in Industry 4.0
Is one of many subfields of artificial intelligence concerning the ways that computers learn from experience to improve their ability to think, plan, decide and act.
Published in Chapter: Intelligent Manufacturing Systems Driven by Artificial Intelligence in Industry 4.0; From: Handbook of Research on Integrating Industry 4.0 in Business and Manufacturing
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Chapter 21
Introduction to Machine Learning as a New Methodological Framework for Performance Assessment
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Chapter 3
Only Can AI Understand Me?: Big Data Analytics, Decision Making, and Reasoning
Is concerned about how computers can adapt to new circumstances and to detect and extrapolate patterns and knowledge.
Published in Chapter: Only Can AI Understand Me?: Big Data Analytics, Decision Making, and Reasoning; From: Intelligent Analytics With Advanced Multi-Industry Applications
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Chapter 12
IoT-Controlled Railway Gate System With ML Object Detection Approach: Applied Approach for a Secured IoT System
The belief that family is central to wellbeing and that family members and family issues take precedence over other aspects of life.
Published in Chapter: IoT-Controlled Railway Gate System With ML Object Detection Approach: Applied Approach for a Secured IoT System; From: Applied Approach to Privacy and Security for the Internet of Things
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Chapter 30
Entrepreneurship and Technopreneurship in Era 4.0: GO-JEK Extended to Decacorn
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Chapter 5
Opportunities and Challenges of Adopting Artificial Intelligence for Learning and Teaching in Higher Education
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Chapter 4
How Artificial Intelligence Can Enhance Predictive Maintenance in Smart Factories
It refers to the study of computer algorithms that improve automatically through experience. It is seen as a subset of Artificial Intelligence.
Published in Chapter: How Artificial Intelligence Can Enhance Predictive Maintenance in Smart Factories; From: Empowering Sustainable Industrial 4.0 Systems With Machine Intelligence
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Chapter 3
Intelligent Recommender Systems in E-Commerce: Opportunities and Challenges for Online Customers
It is a data analytics technique that uses algorithms and statistical models to teach computing system to perform a specific task. Rather than relying on explicit instructions, machine learning relies on patterns and inferences.
Published in Chapter: Intelligent Recommender Systems in E-Commerce: Opportunities and Challenges for Online Customers; From: Handbook of Research on IT Applications for Strategic Competitive Advantage and Decision Making
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Chapter 3
Frameworks for Integration of Future-Oriented Computational Thinking in K-12 Schools
a large branch of artificial intelligence that studies, develops, and implements computer systems capable of learning beyond their explicit programming. Such systems “learn” through their experience with numeric, language, visual, auditory, and/or tactile data.
Published in Chapter: Frameworks for Integration of Future-Oriented Computational Thinking in K-12 Schools; From: Handbook of Research on Integrating Computer Science and Computational Thinking in K-12 Education
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Chapter 14
Artificial Intelligence in Higher Education: A New Horizon
With machine learning (ml), a form of artificial intelligence (ai), software programs can predict outcomes more accurately without having to be explicitly instructed to do so. To forecast new output values, machine learning algorithms use historical data as input.
Published in Chapter: Artificial Intelligence in Higher Education: A New Horizon; From: Handbook of Research on AI Methods and Applications in Computer Engineering
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Chapter 3
Learning Framework for Real-World Facial Emotion Recognition
Machine learning is a subfield of artificial intelligence that focuses on building algorithms and models that can learn from and make predictions or decisions based on data, without being explicitly programmed. It involves the use of statistical and computational techniques to enable machines to learn from experience and improve their performance on a given task.
Published in Chapter: Learning Framework for Real-World Facial Emotion Recognition; From: AI-Enabled Social Robotics in Human Care Services
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Chapter 9
Artificial Intelligence Applications in Cybersecurity
This is when machines or in most cases computers learn from exposure to different environments they have been subjected to.
Published in Chapter: Artificial Intelligence Applications in Cybersecurity; From: Handbook of Research on AI Methods and Applications in Computer Engineering
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Chapter 1
Digital Marketing Perspectives: Challenges and Opportunities Raised by Technology
ML is a type of artificial intelligence (AI) that involves the use of algorithms and statistical models to enable machines to learn and improve from experience without being explicitly programmed.
Published in Chapter: Digital Marketing Perspectives: Challenges and Opportunities Raised by Technology; From: Confronting Security and Privacy Challenges in Digital Marketing
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Chapter 3
Artificial Intelligence and Its Integration with Regenerative Medicine Approach
Major component of artificial intelligence in the healthcare industry. Its main capabilities include acquiring and applying knowledge to a specific problem through the use of programmed algorithmic strategies.
Published in Chapter: Artificial Intelligence and Its Integration with Regenerative Medicine Approach; From: Integrating Digital Health Strategies for Effective Administration
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Chapter 1
Artificial Intelligence: Concepts and Notions
Most recent advances in AI have been achieved by applying machine learning to very large data sets. Machine-learning algorithms detect patterns and learn how to make predictions and recommendations by processing data and experiences, rather than by receiving explicit programming instruction. The algorithms also adapt in response to new data and experiences to improve efficacy over time (Source: https://www.mckinsey.com/business-functions/mckinsey-analytics/our-insights/an-executives-guide-to-ai ).
Published in Chapter: Artificial Intelligence: Concepts and Notions; From: Responsible AI and Ethical Issues for Businesses and Governments
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Chapter 3
Blockchain Platform to Resolve Security Issues in IoT and Smart Networks
Machine learning is a branch of artificial intelligence (AI) and computer science which focuses on the use of data and algorithms to imitate the way that humans learn, gradually improving its accuracy.
Published in Chapter: Blockchain Platform to Resolve Security Issues in IoT and Smart Networks; From: AI-Enabled Agile Internet of Things for Sustainable FinTech Ecosystems
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Chapter 13
Artificial Intelligence Technology to Help Students With Disabilities: Promises and Implications for Teaching and Learning
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Chapter 13
Opportunistic Edge Computing Architecture for Smart Healthcare Systems
Machine Learning models are scientific and statistical model that are developed on gathered data for classification, analysis, and forecast the future events.
Published in Chapter: Opportunistic Edge Computing Architecture for Smart Healthcare Systems; From: Handbook of Research on Engineering, Business, and Healthcare Applications of Data Science and Analytics
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Chapter 6
Where Is Digital Retailing Evolving?: An Evaluation From the Perspective of Digital Retailing Technologies
Machine learning is a powerful tool that enables systems to learn patterns from data, continuously improve performance, and make predictions based on those patterns.
Published in Chapter: Where Is Digital Retailing Evolving?: An Evaluation From the Perspective of Digital Retailing Technologies; From: Navigating the Shifting Landscape of Consumer Behavior
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Chapter 2
Artificial Intelligence Applications in Renewable Power Systems
The application and advancement of computer systems capable of learning and adapting autonomously through the utilization of statistical models and algorithms to analyze data patterns and derive conclusions.
Published in Chapter: Artificial Intelligence Applications in Renewable Power Systems; From: Industrial Applications of Big Data, AI, and Blockchain
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Chapter 13
Board Games AI
Refers to the activity or ability of computer programs to learn without being explicitly programmed given an adequate amount of data.
Published in Chapter: Board Games AI; From: Encyclopedia of Information Science and Technology, Fourth Edition
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Chapter 12
Enhancing Crypto Ransomware Detection Through Network Analysis and Machine Learning
Machine learning is a field of artificial intelligence that involves the development of algorithms and models that enable computers to learn and make predictions or decisions without being explicitly programmed, by leveraging patterns and insights from data.
Published in Chapter: Enhancing Crypto Ransomware Detection Through Network Analysis and Machine Learning; From: Innovative Machine Learning Applications for Cryptography
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Chapter 11
Big Data Technologies and Pharmaceutical Manufacturing
A broad category of algorithms that can be trained to create the ability to make decisions. There are many different types of training methods and architectures, a full discussion of which is out of the scope of this paper.
Published in Chapter: Big Data Technologies and Pharmaceutical Manufacturing; From: Encyclopedia of Data Science and Machine Learning
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Chapter 51
Methods and Techniques of Data Mining
The study and use of algorithms and mathematical and statistical models in computer systems to perform a specific tasks but not using explicit instructions, on the contrary relying on discovery of patterns and inference. Is considered part of Artificial Intelligence. The software is to adapt to new circumstances and to detect and extrapolate patterns.
Published in Chapter: Methods and Techniques of Data Mining; From: Encyclopedia of Information Science and Technology, Fifth Edition
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Chapter 6
Machine Learning Approach for Kashmiri Word Sense Disambiguation
Machine learning is a part of Artificial Intelligence concerned with the development of models that let computers learn and make judgments without the requirement of being programmed explicitly. The machine learning models are designed with the aim of elevating their performance through experience or exposure to data.
Published in Chapter: Machine Learning Approach for Kashmiri Word Sense Disambiguation; From: Empowering Low-Resource Languages With NLP Solutions
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Chapter 17
Machine Learning and Financial Investing
As a broad subfield of artificial intelligence, machine learning is concerned with the design and development of algorithms and techniques that allow computers to “learn”
Published in Chapter: Machine Learning and Financial Investing; From: Handbook of Research on Machine Learning Applications and Trends: Algorithms, Methods, and Techniques
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Chapter 7
Past, Present, and Future of Artificial Intelligence in Library Services
Machine learning (ML) is a type of artificial intelligence (AI) that trains applications with historical data, where they become accurate in predicting outcomes because they have been trained beforehand.
Published in Chapter: Past, Present, and Future of Artificial Intelligence in Library Services; From: Handbook of Research on Emerging Trends and Technologies in Librarianship
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Chapter 3
An In-Depth Exploration of AI and Humanoid Robotics' Role in Contemporary Healthcare
A branch of artificial intelligence that enables systems to learn patterns from data, improving performance over time without explicit programming. It encompasses supervised learning, unsupervised learning, and reinforcement learning. Applications range from image recognition and natural language processing to personalized recommendations and autonomous vehicles, transforming various industries.
Published in Chapter: An In-Depth Exploration of AI and Humanoid Robotics' Role in Contemporary Healthcare; From: Approaches to Human-Centered AI in Healthcare
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Chapter 1
Building Intelligent Cities: Concepts, Principles, and Technologies
This can be regarded as a subset of AI which refers to analyzing structured data and identifying trends (correlations) for specific outcomes and using that information to predict future values (causation).
Published in Chapter: Building Intelligent Cities: Concepts, Principles, and Technologies; From: Developing and Monitoring Smart Environments for Intelligent Cities
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Chapter 16
Smart Store Base Grocery: Facilitating Future Forecasting Profit
An application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed.
Published in Chapter: Smart Store Base Grocery: Facilitating Future Forecasting Profit; From: Managerial Challenges and Social Impacts of Virtual and Augmented Reality
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Chapter 14
Predicting Healthcare Readmissions Using Artificial Intelligence
Machine learning is a tool used in health care to help medical professionals care for patients and manage clinical data. It is an application of artificial intelligence, which involves programming computers to mimic how people think and learn.
Published in Chapter: Predicting Healthcare Readmissions Using Artificial Intelligence; From: Handbook of Research on AI and Machine Learning Applications in Customer Support and Analytics
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Chapter 100
Functional Dimension Reduction for Chemometrics
An area of Artificial Intelligence dealing with adaptive computational methods such as Artificial Neural Networks and Genetic Algorithms.
Published in Chapter: Functional Dimension Reduction for Chemometrics; From: Encyclopedia of Artificial Intelligence
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Chapter 15
Beyond Virtual and Augmented Reality: AI-Assisted Mixed Reality and Its Impacts on Society
An application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed.
Published in Chapter: Beyond Virtual and Augmented Reality: AI-Assisted Mixed Reality and Its Impacts on Society; From: Managerial Challenges and Social Impacts of Virtual and Augmented Reality
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Chapter 11
Restaurant Sales Prediction Using Machine Learning
A subfield of Artificial Intelligence (AI) that enables systems to learn and improve from experience without being explicitly programmed.
Published in Chapter: Restaurant Sales Prediction Using Machine Learning; From: Handbook of Research on AI and Machine Learning Applications in Customer Support and Analytics
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Chapter 2
Machine Learning and Optimization Applications for Soft Robotics
Machine Learning is an area of artificial intelligence in which computers can self-learn based on past experiences.
Published in Chapter: Machine Learning and Optimization Applications for Soft Robotics; From: Design and Control Advances in Robotics
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Chapter 8
AI-Driven System for Early Detection and Diagnosis of Cataracts by Image Recognition and Machine Learning Algorithms
Important to artificial intelligence (AI) is machine learning (ML), which allows systems to learn from data automatically, without human intervention. Machine learning (ML) applies algorithms to data in order to help computers learn to recognize patterns, make decisions, and gradually become more efficient. Supervised, unsupervised, and reinforcement learning are three of the most common ML methods.
Published in Chapter: AI-Driven System for Early Detection and Diagnosis of Cataracts by Image Recognition and Machine Learning Algorithms; From: AI-Driven Innovations in Digital Healthcare: Emerging Trends, Challenges, and Applications
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Chapter 11
Data Mining and Machine Learning Approaches in Breast Cancer Biomedical Research
It is a field of statistics and computer science that gives computer systems the ability to “learn” with data, without being explicitly programmed.
Published in Chapter: Data Mining and Machine Learning Approaches in Breast Cancer Biomedical Research; From: Sentiment Analysis and Knowledge Discovery in Contemporary Business
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Chapter 4
Re-Shaping Business Strategy in the Era of Digitization
A type/application of artificial intelligence in which a machine (computer) automatically learns without explicit programming.
Published in Chapter: Re-Shaping Business Strategy in the Era of Digitization; From: Handbook of Research on Strategic Fit and Design in Business Ecosystems
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Chapter 17
New Data-Related Roles for Librarians: Using Bibliometric Analysis and Visualization to Increase Visibility of Research Impact
A type of artificial intelligence (AI) that uses algorithms to predict relevance of items in an unlabeled / unclassified corpus. Unsupervised machine learning (e.g., clustering) does not use training data to make predictions. Supervised machine learning uses a training dataset to make predictions of unlabeled data.
Published in Chapter: New Data-Related Roles for Librarians: Using Bibliometric Analysis and Visualization to Increase Visibility of Research Impact; From: Handbook of Research on Knowledge and Organization Systems in Library and Information Science
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Chapter 159
Machine Learning and Sensor Data Fusion for Emotion Recognition
Is a kind of Artificial intelligence to enable machines to think using data and algorithms.
Published in Chapter: Machine Learning and Sensor Data Fusion for Emotion Recognition; From: Encyclopedia of Data Science and Machine Learning
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Chapter 12
Machine Learning and Its Application in Monitoring Diabetes Mellitus
Machine learning is a scientific approach to analyse available data using algorithms and statistical models to accomplish a specific task by utilizing the patterns evolved.
Published in Chapter: Machine Learning and Its Application in Monitoring Diabetes Mellitus; From: Handbook of Research on Engineering, Business, and Healthcare Applications of Data Science and Analytics
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Chapter 7
Machine Learning Techniques for IoT-Based Indoor Tracking and Localization
Machine learning, a branch of artificial intelligence (AI), gains the computer systems the automatic self-learning ability via past experiences.
Published in Chapter: Machine Learning Techniques for IoT-Based Indoor Tracking and Localization; From: Emerging Trends in IoT and Integration with Data Science, Cloud Computing, and Big Data Analytics
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Chapter 5
Distance Learning in Chemical Engineering: Past, Present, and Future
Methodologies of automated learning by machines/algorithms such that they develop improved responses; a common technology for realisation of artificial intelligence.
Published in Chapter: Distance Learning in Chemical Engineering: Past, Present, and Future; From: Handbook of Research on Determining the Reliability of Online Assessment and Distance Learning
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Chapter 12
Customer Experience in the Restaurant Industry: Use of Smart Technologies
It is a method paradigm that makes inferences from existing data using mathematical and statistical methods and makes predictions about what is unknown with these inferences.
Published in Chapter: Customer Experience in the Restaurant Industry: Use of Smart Technologies; From: Handbook of Research on Smart Technology Applications in the Tourism Industry
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Chapter 12
The Impact of FinTech on Financial Services in India: Past, Present, and Future Trends
ML is a method of data analysis that automates analytical model building. It is a branch of artificial intelligence based on the idea that systems can learn from data, identify patterns, and make decisions with minimal human intervention.
Published in Chapter: The Impact of FinTech on Financial Services in India: Past, Present, and Future Trends; From: Innovative Strategies for Implementing FinTech in Banking
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Chapter 10
Empowering Health With an Advanced Multi-Disease Prediction System and Medical Encyclopaedia: Apna Clinic
Machine Learning is the field of study which gives computers the ability to learn without being explicitly programmed. In our project, we have mainly used Supervised Machine Learning algorithms.
Published in Chapter: Empowering Health With an Advanced Multi-Disease Prediction System and Medical Encyclopaedia: Apna Clinic; From: Intelligent Engineering Applications and Applied Sciences for Sustainability
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Chapter 14
The Role of Big Data Research Methodologies in Describing Investor Risk Attitudes and Predicting Stock Market Performance: Deep Learning and Risk Tolerance
A data analysis technique where a machine, with data, repeats a certain calculation to find a pattern or trend. Machine learning includes various algorithms to find patterns.
Published in Chapter: The Role of Big Data Research Methodologies in Describing Investor Risk Attitudes and Predicting Stock Market Performance: Deep Learning and Risk Tolerance; From: Handbook of Research on New Challenges and Global Outlooks in Financial Risk Management
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Chapter 9
High Performance Computing, Big Data, and Cloud Computing: The Perfect De Facto Trio or Converging Technological Mantras?
A scientific discipline in the field of artificial intelligence that creates systems that learn automatically. Learning in this context means identifying complex patterns in millions of data.
Published in Chapter: High Performance Computing, Big Data, and Cloud Computing: The Perfect De Facto Trio or Converging Technological Mantras?; From: Organizational Transformation and Managing Innovation in the Fourth Industrial Revolution
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Chapter 12
AI-Enabled Internet of Nano Things Methodology for Healthcare Information Management
Machine learning (ML) lets programs make better predictions over time, even if they haven't been trained. Machine learning algorithms produce predictions from historical data.
Published in Chapter: AI-Enabled Internet of Nano Things Methodology for Healthcare Information Management; From: AI-Enabled Multiple-Criteria Decision-Making Approaches for Healthcare Management
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Chapter 1
Importance of Deep Learning Models in the Medical Imaging Field
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Chapter 8
Deep Learning for Facial Skin Issues Detection: A Study for Global Care With Healthcare 5.0
Machine learning is a branch of artificial intelligence (AI) and computer science which focuses on the use of data and algorithms to imitate the way that humans learn, gradually improving its accuracy.
Published in Chapter: Deep Learning for Facial Skin Issues Detection: A Study for Global Care With Healthcare 5.0; From: Federated Learning and AI for Healthcare 5.0
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Chapter 56
Machine Learning Enables Decision-Making Processes for an Enterprise
The process by which a computer system learns from experience. Algorithms are developed to make the computer system learn from the historical cases.
Published in Chapter: Machine Learning Enables Decision-Making Processes for an Enterprise; From: Encyclopedia of Data Science and Machine Learning
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Chapter 3
Machine Learning Techniques to Mitigate Security Attacks in IoT
A field of information technology that has the ability to learn data insights by using statistical techniques.
Published in Chapter: Machine Learning Techniques to Mitigate Security Attacks in IoT; From: Security and Privacy Issues in Sensor Networks and IoT
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Chapter 10
Applications of Data Mining in the Healthcare Industry
Concerned with the development of algorithms and techniques, which allow computers to “learn.”
Published in Chapter: Applications of Data Mining in the Healthcare Industry; From: Encyclopedia of Healthcare Information Systems
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Chapter 172
Healthcare Data Analysis in the Internet of Things Era
The field of study that deals with the construction of algorithms that can learn from data and make predictions on data.
Published in Chapter: Healthcare Data Analysis in the Internet of Things Era; From: Encyclopedia of Information Science and Technology, Fourth Edition
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Chapter 8
Using Deep Learning and Big Data Analytics for Managing Cyber-Attacks
Machine learning is a branch of AI, computer science, and statistics that focuses on making most of the data and apply algorithms to mimic the way humans learn, act, and perform with aim of steadily improving accuracy of the predictions/tasks done by the machine.
Published in Chapter: Using Deep Learning and Big Data Analytics for Managing Cyber-Attacks; From: New Approaches to Data Analytics and Internet of Things Through Digital Twin
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Chapter 28
Credit Scoring: A Constrained Optimization Framework With Hybrid Evolutionary Feature Selection
A subject in computer science, aimed at studying theories, algorithms, and applications of systems that learn like humans by discovering knowledge from raw data.
Published in Chapter: Credit Scoring: A Constrained Optimization Framework With Hybrid Evolutionary Feature Selection; From: Handbook of Research on Applied AI for International Business and Marketing Applications
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Chapter 382
Learning Systems Engineering
The ability of a machine to improve its performance based on previous results.
Published in Chapter: Learning Systems Engineering; From: Encyclopedia of Information Science and Technology, Second Edition
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Chapter 148
A Bayesian Network Model for Probability Estimation
The process of generating a computer system that is capable of independently acquiring data and integrating that data to generate useful knowledge.
Published in Chapter: A Bayesian Network Model for Probability Estimation; From: Encyclopedia of Information Science and Technology, Third Edition
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Chapter 2
Deep Learning on Edge: Challenges and Trends
A subfield of artificial intelligence whose objective is to give systems the ability to learn and improve by its own without being explicitly programmed to do it.
Published in Chapter: Deep Learning on Edge: Challenges and Trends; From: Smart Systems Design, Applications, and Challenges
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Chapter 5
Applications of Chatbots: Exploring the Dynamics of Chatbot Evolution
A subset of AI that involves the development of algorithms and statistical models that enable computers to perform tasks without explicit instructions, by relying on patterns and inference.
Published in Chapter: Applications of Chatbots: Exploring the Dynamics of Chatbot Evolution; From: Design and Development of Emerging Chatbot Technology
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Chapter 18
Identifying Patterns in Fresh Produce Purchases: The Application of Machine Learning Techniques
A branch of artificial intelligence that focuses on data analysis methods that allow for automation of the process of analytical model building.
Published in Chapter: Identifying Patterns in Fresh Produce Purchases: The Application of Machine Learning Techniques; From: Handbook of Research on Big Data Clustering and Machine Learning
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Chapter 8
Using Sentiment Analytics to Understand Learner Experiences in Serious Games
A computer algorithm that can learn and adapt without following explicit instructions by identifying and analyzing data patterns.
Published in Chapter: Using Sentiment Analytics to Understand Learner Experiences in Serious Games; From: Next-Generation Applications and Implementations of Gamification Systems
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Chapter 2
Cell-Free Synthetic Biology: The Novel Approach Towards the Biotechnological Applications
A field of artificial intelligence that focuses on the development of algorithms and statistical models that enable computer systems to learn and improve from data without explicit programming.
Published in Chapter: Cell-Free Synthetic Biology: The Novel Approach Towards the Biotechnological Applications; From: Applications of Synthetic Biology in Health, Energy, and Environment
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Chapter 10
Machine Learning Techniques to Diagnose and Treat Cancer Disease
It is defined as group of techniques that combine mathematics with computational processing to learn patterns in a set of data with different purposes: classification, prediction, etc.
Published in Chapter: Machine Learning Techniques to Diagnose and Treat Cancer Disease; From: Artificial Intelligence for Societal Development and Global Well-Being
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Chapter 13
An IoT-Based Earthquake Warning System for Smart Cities
An artificial intelligence technique that makes the system to learn automatically with the help of statistical techniques.
Published in Chapter: An IoT-Based Earthquake Warning System for Smart Cities; From: Handbook of Research on Implementation and Deployment of IoT Projects in Smart Cities
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Chapter 7
BI and Analytics for Effective Disaster Recovery Management Lessons From the Bayou
Composed of many technologies (such as deep learning, neural networks and natural language processing), used in unsupervised and supervised learning, that operate guided by existing data.
Published in Chapter: BI and Analytics for Effective Disaster Recovery Management Lessons From the Bayou; From: Achieving Organizational Agility, Intelligence, and Resilience Through Information Systems
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Chapter 20
Recent Advances in Automated Chromosome Image Analysis
As a broad subfield of artificial intelligence, machine learning is concerned with the design and development of algorithms and techniques that allow computers to “learn”.
Published in Chapter: Recent Advances in Automated Chromosome Image Analysis; From: Handbook of Research on Advanced Techniques in Diagnostic Imaging and Biomedical Applications
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Chapter 5
New Marketing Approaches and Consumer Trends in Line With Technological Developments
It is a field that enables a computer system to learn using data. It helps algorithms automatically learn from data in order to perform a specific task and provides these algorithms with analysis, prediction and decision-making capabilities.
Published in Chapter: New Marketing Approaches and Consumer Trends in Line With Technological Developments; From: Navigating the Shifting Landscape of Consumer Behavior
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Chapter 2
Detection of Diabetic Retinopathy With Mobile Application Using Deep Learning
Machine learning is an application of artificial intelligence that provides systems the ability to automatically learn and improve from experience without being explicitly programmed.
Published in Chapter: Detection of Diabetic Retinopathy With Mobile Application Using Deep Learning; From: Diagnostic Applications of Health Intelligence and Surveillance Systems
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Chapter 10
Applications of Artificial Intelligence in Assessment for Learning in Schools
It is pivotal to artificial intelligence, which provides computer to learn the patterns in the data without being explicitly programmed. It is also termed as data mining, knowledge engineering, expert systems etc.
Published in Chapter: Applications of Artificial Intelligence in Assessment for Learning in Schools; From: Handbook of Research on Digital Content, Mobile Learning, and Technology Integration Models in Teacher Education
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Chapter 8
Machine Learning for Healthcare: Various Tools and Techniques of Machine Learning in Healthcare
The use and development of computer systems that are able to learn and adapt without following explicit instructions, by using algorithms and statistical models to analyze and draw inferences from patterns in data.
Published in Chapter: Machine Learning for Healthcare: Various Tools and Techniques of Machine Learning in Healthcare; From: Quality Control Applications in the Pharmaceutical and Medical Device Manufacturing Industry
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Chapter 20
Virtual Singers Empowered by Machine Learning
Artificial intelligence that allows machines to improve automatically.
Published in Chapter: Virtual Singers Empowered by Machine Learning; From: Encyclopedia of Data Science and Machine Learning
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Chapter 121
Hierarchical Neuro-Fuzzy Systems Part II
Concerned with the design and development of algorithms and techniques that allow computers to “learn”. The major focus of machine learning research is to automatically extract useful information from historical data, by computational and statistical methods.
Published in Chapter: Hierarchical Neuro-Fuzzy Systems Part II; From: Encyclopedia of Artificial Intelligence
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Chapter 5
Ethical Navigations: Adaptable Frameworks for Responsible AI Use in Higher Education
A subset of AI that incorporates aspects of computer science, mathematics, and coding. Machine learning focuses on developing algorithms and models that help machines learn from data and predict trends and behaviors, without human assistance.
Published in Chapter: Ethical Navigations: Adaptable Frameworks for Responsible AI Use in Higher Education; From: Exploring the Ethical Implications of Generative AI
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Chapter 2
Unleashing the Potential of Every Child: The Transformative Role of Artificial Intelligence in Personalized Learning
AI systems’ ability to iteratively enhance specific tasks by identifying patterns in data without explicit programming.
Published in Chapter: Unleashing the Potential of Every Child: The Transformative Role of Artificial Intelligence in Personalized Learning; From: Embracing Cutting-Edge Technology in Modern Educational Settings
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Chapter 20
Automatic Detection and Assessment of Autism Spectrum Disorder: A Systematic Review
Machine learning (ML) is a type of artificial intelligence (AI) that allows software applications to become more accurate at predicting outcomes without being explicitly programmed to do so.
Published in Chapter: Automatic Detection and Assessment of Autism Spectrum Disorder: A Systematic Review; From: Handbook of Research on Critical Issues in Special Education for School Rehabilitation Practices
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Chapter 13
A Comprehensive Review on AI Techniques for Healthcare
It is an application of artificial intelligence that provides systems the ability to automatically learn and improve from experience without being explicitly programmed.
Published in Chapter: A Comprehensive Review on AI Techniques for Healthcare; From: Revolutionizing Healthcare Through Artificial Intelligence and Internet of Things Applications
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Chapter 11
Cyber Secure Man-in-the-Middle Attack Intrusion Detection Using Machine Learning Algorithms
Machine learning is the process of predicting future result based on previous history.
Published in Chapter: Cyber Secure Man-in-the-Middle Attack Intrusion Detection Using Machine Learning Algorithms; From: AI and Big Data’s Potential for Disruptive Innovation
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Chapter 30
Responsible Machine Learning for Ethical Artificial Intelligence in Business and Industry
The learning process through which an AI analyses the training dataset (usually structured), builds, and refines a prediction model. Machine learning may be supervised or unsupervised.
Published in Chapter: Responsible Machine Learning for Ethical Artificial Intelligence in Business and Industry; From: Handbook of Research on Applied Data Science and Artificial Intelligence in Business and Industry
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Chapter 7
Ethical and Regulatory Challenges of Emerging Health Technologies
Is a computer algorithm program which automatically learns and adapts to new data without human interference.
Published in Chapter: Ethical and Regulatory Challenges of Emerging Health Technologies; From: Applied Ethics in a Digital World
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Chapter 4
Behavior Classification of Egyptian Fruit Bat (Rousettus aegyptiacus) From Calls With Deep Learning
Imitates the way the human learns with the use of algorithms and data, and learning accuracy improves automatically through experience.
Published in Chapter: Behavior Classification of Egyptian Fruit Bat (Rousettus aegyptiacus) From Calls With Deep Learning; From: Handbook of Research on New Investigations in Artificial Life, AI, and Machine Learning
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Chapter 16
Image Processing and Machine Learning Techniques for Facial Expression Recognition
The purpose of machine learning is to extract information from several types of data automatically, using computational and statistical methods. It is the use of computer algorithms which improve automatically using experience
Published in Chapter: Image Processing and Machine Learning Techniques for Facial Expression Recognition; From: Handbook of Research on Advanced Techniques in Diagnostic Imaging and Biomedical Applications
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Chapter 9
The Use of Artificial Intelligence in Gerodontology in the Age of Digital Technology
A subset of artificial intelligence, machine learning enables computers to improve their performance on a particular task without being explicitly programmed. It entails creating algorithms and statistical models that enable systems to identify patterns, predict outcomes, and accumulate knowledge.
Published in Chapter: The Use of Artificial Intelligence in Gerodontology in the Age of Digital Technology; From: Geriatric Dentistry in the Age of Digital Technology
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Chapter 9
Intelligent Cities: A Compendious and Multidisciplinary Approach – Issues and Opportunities
This is the ability of machines to learn and store knowledge that then enables them to act and think like humans in solving problems.
Published in Chapter: Intelligent Cities: A Compendious and Multidisciplinary Approach – Issues and Opportunities; From: Developing and Monitoring Smart Environments for Intelligent Cities
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Chapter 8
Artificial Intelligence Effects on Contracts and Contracting
Machine learning is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed.
Published in Chapter: Artificial Intelligence Effects on Contracts and Contracting; From: Innovative and Agile Contracting for Digital Transformation and Industry 4.0
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Chapter 1
Developing an Effective Classification Model for Medical Data Analysis
It is sub-field of artificial intelligence that often uses statistical techniques to make computers capable enough to “learn” with data.
Published in Chapter: Developing an Effective Classification Model for Medical Data Analysis; From: Advanced Classification Techniques for Healthcare Analysis
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Chapter 5
Artificial Intelligence in Marketing: A Study on Tools, Use, and Impacts
Area of Artificial Intelligence that consists of the continuous machine learning process, where a human being inserts data or the machine collects data from human behavior online and it, the machine, can learn from this data.
Published in Chapter: Artificial Intelligence in Marketing: A Study on Tools, Use, and Impacts; From: Contemporary Trends in Innovative Marketing Strategies
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Chapter 39
Machine Learning and Exploratory Data Analysis in Cross-Sell Insurance
Machine Learning makes computers to learn something. It uses massive data and learning algorithms. The algorithms train themselves using this data in order to learn without being specifically programmed.
Published in Chapter: Machine Learning and Exploratory Data Analysis in Cross-Sell Insurance; From: Encyclopedia of Data Science and Machine Learning
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Chapter 8
Leveraging Generative AI for Cross-Cultural Knowledge Exchange in Higher Education
A subset of AI where computer systems learn and improve performance from data without being explicitly programmed, adapting, and evolving based on patterns and experiences.
Published in Chapter: Leveraging Generative AI for Cross-Cultural Knowledge Exchange in Higher Education; From: Facilitating Global Collaboration and Knowledge Sharing in Higher Education With Generative AI
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Chapter 8
What Are Students Saying About AI as an Academic Tool
In the context of language education, machine learning refers to the application of artificial intelligence techniques and algorithms to enhance language learning experiences (Godwin-Jones, 2022). This chapter briefly visits the development of systems that optimize and customize the language learning process for students.
Published in Chapter: What Are Students Saying About AI as an Academic Tool; From: AI in Language Teaching, Learning, and Assessment
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Chapter 5
Perceptions and New Realities for the 21st Century Learner
Is the process of presenting the computer program with a large set of training data where the data consists of a set of variables and what the outcome was for that data. An example would be pixel values for an image of a person’s face, the outcome (or classifier) would be a unique number that represents who that was. So, the input array would have the pixels and the expected outcome that the output neurons should show is the unique identifier of the person. The machine learning algorithm would have many sets of input data for each item (like a face) it is supposed to learn.
Published in Chapter: Perceptions and New Realities for the 21st Century Learner; From: Handbook of Research on Literacy and Digital Technology Integration in Teacher Education
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Chapter 22
Neo-Orientalist Approaches in XR (Extended Reality) Applications
Is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed.
Published in Chapter: Neo-Orientalist Approaches in XR (Extended Reality) Applications; From: Handbook of Research on Contemporary Approaches to Orientalism in Media and Beyond
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Chapter 16
Classification of Product Backlog Items in Agile Software Development Using Machine Learning
Machine learning (ML) is an artificial intelligence (AI) science that enables machines to automatically learn from data and previous experiences while finding patterns to generate predictions with minimum human interaction. Machine learning approaches allow computers to function independently without requiring explicit programming. ML apps are fed new data and may learn, grow, evolve, and adapt independently.
Published in Chapter: Classification of Product Backlog Items in Agile Software Development Using Machine Learning; From: Handbook of Research on Technological Advances of Library and Information Science in Industry 5.0
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Chapter 9
Research Journey of Hate Content Detection From Cyberspace
Machine Learning is a statistical or mathematical model that performs data analysis, prediction, and clustering. This science is a subfield of Artificial Intelligence.
Published in Chapter: Research Journey of Hate Content Detection From Cyberspace; From: Natural Language Processing for Global and Local Business
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Chapter 11
The Future of Business-to-Business Selling
Machine learning (ML) is a subset of AI that focuses on the development of algorithms and statistical models that enable computer systems to improve their performance on a specific task over time. Instead of being explicitly programmed for a task, machines use data to learn patterns and make decisions or predictions. The primary goal of machine learning is to develop models that can generalize well to new, unseen data, allowing them to make accurate predictions or decisions in various domains, such as image recognition, natural language processing, and recommendation systems. ML encompasses various techniques, including supervised learning, unsupervised learning, and reinforcement learning (OpenAI, 2023 AU45: The in-text citation "OpenAI, 2023" is not in the reference list. Please correct the citation, add the reference to the list, or delete the citation. ).
Published in Chapter: The Future of Business-to-Business Selling; From: A Critical Examination of the Recent Evolution of B2B Sales
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Chapter 12
Undergraduate Computer Science Capstone Projects: Experiences and Examples in Data Science
Usually means to using data samples to obtain a mathematical model(s) for prediction and classification of unknown data. It is a branch of artificial intelligence.
Published in Chapter: Undergraduate Computer Science Capstone Projects: Experiences and Examples in Data Science; From: Handbook of Research on Foundations and Applications of Intelligent Business Analytics
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Chapter 13
Integrated Continuous Healthcare Team Computer System Architecture
The scientific area that studies algorithms and mathematical models that computer systems may use to perform tasks relying on pattern recognition and inference.
Published in Chapter: Integrated Continuous Healthcare Team Computer System Architecture; From: Exploring the Role of ICTs in Healthy Aging
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Chapter 1
Applying Artificial Intelligence to Financial Investing
A method of automatically learning patterns from data in order to make future predictions.
Published in Chapter: Applying Artificial Intelligence to Financial Investing; From: Encyclopedia of Information Science and Technology, Fourth Edition
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Chapter 12
Encoded Memory: Artificial Intelligence and Deep Learning in Architecture
is The scientific study of algorithms and statistical models that computer systems use to effectively perform a specific task without using explicit instructions, relying on patterns and inference instead. It is seen as a subset of artificial intelligence.
Published in Chapter: Encoded Memory: Artificial Intelligence and Deep Learning in Architecture; From: Impact of Industry 4.0 on Architecture and Cultural Heritage
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Chapter 1
Downscaling of Open Coarse Precipitation Data Using a Machine Learning Algorithm
Is an algorithmic-based technique known as training data. Machine learning is an algorithmic-based on a mathematical model that analyzes and computes data in a way to make predictions or decisions. This concept, a significant method used widespread among data scientists. There are two types of machine learning techniques: supervised and unsupervised learning.
Published in Chapter: Downscaling of Open Coarse Precipitation Data Using a Machine Learning Algorithm; From: Impacts of Climate Change on Agriculture and Aquaculture
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Chapter 28
Free Text to Standardized Concepts to Clinical Decisions
A method of data analysis that automates analytical model building. It is a branch of artificial intelligence based on the idea that systems can learn from data, identify patterns, and make decisions with minimal human intervention.
Published in Chapter: Free Text to Standardized Concepts to Clinical Decisions; From: Encyclopedia of Data Science and Machine Learning
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Chapter 381
Learnability
The processes used to fine-tune a program’s performance or to augment its knowledge and functionality.
Published in Chapter: Learnability; From: Encyclopedia of Information Science and Technology, Second Edition
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Chapter 2
Machine Learning in Radio Resource Scheduling
Category of algorithm that uses statistical techniques to analyze data and make predictions.
Published in Chapter: Machine Learning in Radio Resource Scheduling; From: Next-Generation Wireless Networks Meet Advanced Machine Learning Applications
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Chapter 22
Personal Health Systems for Diabetes Management, Early Diagnosis and Prevention
Scientific discipline which is oriented to the investigation and construction of algorithms that can learn from the data.
Published in Chapter: Personal Health Systems for Diabetes Management, Early Diagnosis and Prevention; From: Handbook of Research on Trends in the Diagnosis and Treatment of Chronic Conditions
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Chapter 21
Preferences, Utility, Value-Driven Modeling, and Decision Support
A computational methodology for automatic information collection and knowledge descriptions aimed at improving solutions of tasks from real practice and experience.
Published in Chapter: Preferences, Utility, Value-Driven Modeling, and Decision Support; From: Encyclopedia of Organizational Knowledge, Administration, and Technology
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Chapter 2
The Development of Single-Document Abstractive Text Summarizer During the Last Decade
The study of building systems that learn from itself and are adopted by the environment.
Published in Chapter: The Development of Single-Document Abstractive Text Summarizer During the Last Decade; From: Trends and Applications of Text Summarization Techniques
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Chapter 6
Modelling in Support of Decision Making in Business Intelligence
It is research field part of artificial intelligence that provides a system opportunity to automatically learn from data with aim predictive, classification and clustering tasks to be solved.
Published in Chapter: Modelling in Support of Decision Making in Business Intelligence; From: Integration Challenges for Analytics, Business Intelligence, and Data Mining
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Chapter 3
Freshness Grading of Agricultural Products Using Artificial Intelligence
Machine learning is the subset of artificial intelligence and superset of deep learning. Machine learning is used for imitating the acts of humans by teaching computer-based machines and robots.
Published in Chapter: Freshness Grading of Agricultural Products Using Artificial Intelligence; From: Artificial Intelligence Applications in Agriculture and Food Quality Improvement
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Chapter 5
Resulting Changes
Machine learning is a subset of artificial intelligence that involves the development of algorithms and models that enable computers to learn from data and improve their performance on a specific task without being explicitly programmed (OpenAI, 2023 AU42: The in-text citation "OpenAI, 2023" is not in the reference list. Please correct the citation, add the reference to the list, or delete the citation. ).
Published in Chapter: Resulting Changes; From: A Critical Examination of the Recent Evolution of B2B Sales
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Chapter 14
Building an Analytics Culture to Boost a Data-Driven Entrepreneur's Business Model
A method of designing a sequence of actions to solve a problem that optimizes automatically through experience and with limited or no human intervention.
Published in Chapter: Building an Analytics Culture to Boost a Data-Driven Entrepreneur's Business Model; From: Managerial Perspectives on Intelligent Big Data Analytics
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Chapter 4
Study and Application of Machine Learning Methods in Modern Additive Manufacturing Processes
Machine learning offers systems the facility to automatically study and recover from exercise without being openly programmed, it is also known as artificial intelligence (AI). Generally it emphases on the improvement of computer programs that can entrance data and practice it to study for themselves.
Published in Chapter: Study and Application of Machine Learning Methods in Modern Additive Manufacturing Processes; From: Applications of Artificial Intelligence in Additive Manufacturing
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Chapter 10
Fortifying Data Security in the Evolving Digital Landscape: Challenges and Solutions
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Chapter 13
Information Science in the Analytics of Healthcare Data
Concept that a computer program can learn and adapt to new data without human intervention.
Published in Chapter: Information Science in the Analytics of Healthcare Data; From: Integrating AI in IoT Analytics on the Cloud for Healthcare Applications
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Chapter 6
How Can Technology Advance the Public Administration Discipline in Higher Education?: A Comprehensive Analysis of the U.S. Scenario
It is a branch of artificial intelligence (AI) and computer science which focuses on the use of data and algorithms to imitate the way that humans learn, gradually improving its accuracy. Through the use of statistical methods, algorithms are trained to make classifications or predictions, uncovering key insights within data mining projects. This output drive decision-making within applications and businesses, ideally impacting key growth metrics.
Published in Chapter: How Can Technology Advance the Public Administration Discipline in Higher Education?: A Comprehensive Analysis of the U.S. Scenario; From: EdTech Economy and the Transformation of Education
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Chapter 9
Predicting Company Bankruptcy Using Machine Learning Techniques: A Step-by-Step Guide
A type of artificial intelligence that allows computers to learn from data without being explicitly programmed.
Published in Chapter: Predicting Company Bankruptcy Using Machine Learning Techniques: A Step-by-Step Guide; From: Advancement in Business Analytics Tools for Higher Financial Performance
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Chapter 71
E-Mental Health: Contributions, Challenges, and Research Opportunities from a Computer Science Perspective
Algorithms that allow to identify patterns in data to make predictions on new data based on old data.
Published in Chapter: E-Mental Health: Contributions, Challenges, and Research Opportunities from a Computer Science Perspective; From: Encyclopedia of E-Health and Telemedicine
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Chapter 3
Analyzing Big Data Using Recent Machine Learning Techniques to Assist Consumers in Online Purchase Decision
It is mainly a subset of artificial intelligence (AI) where systems (computers and machines) are trained to behave / work like a human. Systems learn from the available data and past experiences. Its examples are self-automated cars, cyber fraud detection, Facial emotion recognition etc. Machine learning further classified into supervised, unsupervised and reinforcement learning.
Published in Chapter: Analyzing Big Data Using Recent Machine Learning Techniques to Assist Consumers in Online Purchase Decision; From: Social Media and Online Consumer Decision Making in the Fashion Industry
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Chapter 5
Technical Considerations for Designing, Developing, and Implementing AI Systems in Africa
A phenomenon where a computer program is designed to learn from huge datasets, sensors, and other data sources and generate interesting patterns, make a suggestion, or make relevant decisions.
Published in Chapter: Technical Considerations for Designing, Developing, and Implementing AI Systems in Africa; From: Examining the Rapid Advance of Digital Technology in Africa
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Chapter 6
A Generalized Overview of the Biomedical Image Processing From the Big Data Perspective
It is an application of the artificial intelligence in which machines can automatically learn and solve problems using the learned experience.
Published in Chapter: A Generalized Overview of the Biomedical Image Processing From the Big Data Perspective; From: Applications of Advanced Machine Intelligence in Computer Vision and Object Recognition: Emerging Research and Opportunities
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Chapter 84
Decision-Making Approaches for Airport Surrounding Traffic Management
A subject of artificial intelligence that aims at the task of computational algorithms, which allow machines to learning objects automatically through historical data.
Published in Chapter: Decision-Making Approaches for Airport Surrounding Traffic Management; From: Encyclopedia of Data Science and Machine Learning
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Chapter 5
Artificial Intelligence (AI) in Academic Libraries: A Theoretical Study
Machine learning (ML) is a type of artificial intelligence (AI) focused on building computer systems that learn from data. The broad range of techniques ML encompasses enables software applications to improve their performance over time.
Published in Chapter: Artificial Intelligence (AI) in Academic Libraries: A Theoretical Study; From: Multidisciplinary Approach to Information Technology in Library and Information Science
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Chapter 6
User Sentiment Analysis and Review Rating Prediction for the Blended Learning Platform App
Is a sort of data analysis that uses artificial intelligence to automate the process of developing analytical models. It's a branch of AI based on the premise that robots can learn from data, discover patterns, and make judgments with little or no human involvement.
Published in Chapter: User Sentiment Analysis and Review Rating Prediction for the Blended Learning Platform App; From: Applying Data Science and Learning Analytics Throughout a Learner’s Lifespan
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Chapter 16
Efficiency Analysis of Genetic Algorithm and Genetic Programming in Data Mining and Image Processing
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Chapter 33
Decision Trees
Development of algorithms and techniques that allow computers to “learn”.
Published in Chapter: Decision Trees; From: Encyclopedia of Information Technology Curriculum Integration
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Chapter 11
Convergence of AI, ML, and DL for Enabling Smart Intelligence: Artificial Intelligence, Machine Learning, Deep Learning, Internet of Things
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Chapter 23
Implementation of Industry 4.0 in Transformation of Medical Device Maintenance Systems
Application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed.
Published in Chapter: Implementation of Industry 4.0 in Transformation of Medical Device Maintenance Systems; From: Handbook of Research on Integrating Industry 4.0 in Business and Manufacturing
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Chapter 5
Marketing and Artificial Intelligence: Personalization at Scale
Ability of a machine to learn from the data it is presented using different techniques that are supervised or non-supervised.
Published in Chapter: Marketing and Artificial Intelligence: Personalization at Scale; From: Handbook of Research on Applied AI for International Business and Marketing Applications
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Chapter 8
Development, Monitoring, and Management Approaches of Machine Learning Implementations for the Effective Delivery of Government Services
It is a branch of artificial intelligence (AI) trains the machines to automatically train and tests the data, learns from the past experiences with less human intervention.
Published in Chapter: Development, Monitoring, and Management Approaches of Machine Learning Implementations for the Effective Delivery of Government Services; From: Machine Learning and Data Science Techniques for Effective Government Service Delivery
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Chapter 1
The Nature of Intelligent Analytics
Is concerned about how computers can adapt to new circumstances and to detect and extrapolate patterns.
Published in Chapter: The Nature of Intelligent Analytics; From: Intelligent Analytics With Advanced Multi-Industry Applications
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Chapter 21
Towards an Effective Imaging-Based Decision Support System for Skin Cancer
Scientific area of Artificial Intelligence focused on the development of algorithms and/or systems with the ability to learn, modify in an automatic manner and reach decisions, based on data patterns.
Published in Chapter: Towards an Effective Imaging-Based Decision Support System for Skin Cancer; From: Handbook of Research on Applied Intelligence for Health and Clinical Informatics
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Chapter 16
Computational Approach for Personality Detection on Attributes: An IoT-MMBD-Enabled Environment
Machine learning is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed.
Published in Chapter: Computational Approach for Personality Detection on Attributes: An IoT-MMBD-Enabled Environment; From: Handbook of Research on Advancements of Artificial Intelligence in Healthcare Engineering
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Chapter 18
Machine Automation Making Cyber-Policy Violator More Resilient: A Proportionate Study
Learning and study of CPU algorithms through automation which improvises its actions from its own knowledges. More or less it is the subcategory of AI system.
Published in Chapter: Machine Automation Making Cyber-Policy Violator More Resilient: A Proportionate Study; From: Handbook of Research on Automated Feature Engineering and Advanced Applications in Data Science
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Chapter 3
Integrated Model for Asynchronous Learning and Predictive Analytics for Enhanced Learner Experience
A type of artificial intelligence (AI) focused on building computer systems that learn from data.
Published in Chapter: Integrated Model for Asynchronous Learning and Predictive Analytics for Enhanced Learner Experience; From: Emergent Practices of Learning Analytics in K-12 Classrooms
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Chapter 6
Demystifying Federated Learning in Artificial Intelligence With Human-Computer Interaction
Machine learning is a branch of artificial intelligence (AI) and computer science which focuses on the use of data and algorithms to imitate the way that humans learn, gradually improving its accuracy.
Published in Chapter: Demystifying Federated Learning in Artificial Intelligence With Human-Computer Interaction; From: Demystifying Federated Learning for Blockchain and Industrial Internet of Things
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Chapter 14
Understanding the Landscape of Online Deception
A subfield of Artificial Intelligence, it is the pursuit of providing computers with the ability to acquire knowledge through the analysis of data and other interactions, and to ultimately apply this knowledge to new settings.
Published in Chapter: Understanding the Landscape of Online Deception; From: Navigating Fake News, Alternative Facts, and Misinformation in a Post-Truth World
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Chapter 12
Technological Help to Prevent and Reduce Cyberbullying
A field of artificial intelligence (AI) that aims to provide systems with the ability to automatically learn and improve from experience without being explicitly programmed.
Published in Chapter: Technological Help to Prevent and Reduce Cyberbullying; From: Developing Safer Online Environments for Children: Tools and Policies for Combatting Cyber Aggression
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Chapter 3
How Business Intelligence and Data Analytics Can Leverage Business
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Chapter 8
A Blood Bank Management System-Based Internet of Things and Machine Learning Technologies
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Chapter 75
Face for Interface
A field of computer science concerned with the question of how to construct computer programs that automatically improve with experience. The key algorithms that form the core of machine learning include neural networks, genetic algorithms, support vector machines, Bayesian networks, and Markov models.
Published in Chapter: Face for Interface; From: Encyclopedia of Multimedia Technology and Networking, Second Edition
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Chapter 12
Self-Analysis Technology, Roles, and Cybersecurity in the Virtual Learning Environments
An automated analysis technique performed by advanced computing devices using pattern recognition methods.
Published in Chapter: Self-Analysis Technology, Roles, and Cybersecurity in the Virtual Learning Environments; From: Recent Advances in Applying Identity and Society Awareness to Virtual Learning
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Chapter 1
Intelligent Big Data Analytics: A Managerial Perspective
Is concerned with how computer can adapt to new circumstances and to detect and extrapolate patterns.
Published in Chapter: Intelligent Big Data Analytics: A Managerial Perspective; From: Managerial Perspectives on Intelligent Big Data Analytics
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Chapter 13
Emerging Trends in Ultrasound Education and Healthcare Clinical Applications: A Rapid Review
The field of computer science where algorithms and statistical models enable computer systems to improve their performance on a specific task through experience, without being explicitly programmed.
Published in Chapter: Emerging Trends in Ultrasound Education and Healthcare Clinical Applications: A Rapid Review; From: Emerging Technologies for Health Literacy and Medical Practice
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Chapter 5
A Review on Applications of Quantum Computing in Machine Learning
Machine learning (ML) is a type of artificial intelligence (AI) that allows software applications to become more accurate at predicting outcomes without being explicitly programmed to do so. Machine learning algorithms use historical data as input to predict new output values. Machine learning is important because it gives enterprises a view of trends in customer behavior and business operational patterns, as well as supports the development of new products. Many of today's leading companies, such as Facebook, Google, and Uber, make machine learning a central part of their operations. Machine learning has become a significant competitive differentiator for many companies.
Published in Chapter: A Review on Applications of Quantum Computing in Machine Learning; From: Technology Road Mapping for Quantum Computing and Engineering
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Chapter 4
The Global Role of Artificial Intelligence in the Learning Process
Is the study of computer algorithms that can improve automatically through experience and by the use of data.
Published in Chapter: The Global Role of Artificial Intelligence in the Learning Process; From: Digital Active Methodologies for Educative Learning Management
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Chapter 6
Creating and Analyzing Induced Decision Trees From Online Learning Data
The harnessing of statistical methods and computational methods to identify data patterns, without explicit programming.
Published in Chapter: Creating and Analyzing Induced Decision Trees From Online Learning Data; From: Methods for Analyzing and Leveraging Online Learning Data
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Chapter 3
Analytical Review of the Applications of Multi-Criteria Decision Making in Data Mining
ML is a branch of artificial intelligence that enable machines to work by using intelligent software. The statistical learning methods construct the backbone of ML algorithms (Mohammed et al., 2016 AU42: The in-text citation "Mohammed et al., 2016" is not in the reference list. Please correct the citation, add the reference to the list, or delete the citation. , p. 4).
Published in Chapter: Analytical Review of the Applications of Multi-Criteria Decision Making in Data Mining; From: Optimizing Big Data Management and Industrial Systems With Intelligent Techniques
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Chapter 4
Water Management for Rural Environments and IoT
The field of science where algorithms that learn from data are studied and used for the purpose of object classification. These algorithms are applied to data analysis and used for the construction of models applied to prediction.
Published in Chapter: Water Management for Rural Environments and IoT; From: Harnessing the Internet of Everything (IoE) for Accelerated Innovation Opportunities
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Chapter 14
Modelling and Forecasting Portfolio Inflows: A Comparative Study of Support Vector Regression, Artificial Neural Networks, and Structural VAR Models
A science of applying artificial intelligence that provides systems the ability to automatically learn and improve without being explicitly programmed.
Published in Chapter: Modelling and Forecasting Portfolio Inflows: A Comparative Study of Support Vector Regression, Artificial Neural Networks, and Structural VAR Models; From: Handbook of Research on Smart Technology Models for Business and Industry
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Chapter 7
ML, AI, and IoT as Driving Forces From Industry 5.0 Concepts for Better Global Food Security
ML, a branch of artificial intelligence, centers on empowering computers to learn and make decisions autonomously without direct programming. It entails creating algorithms that enable machines to analyze and understand data, recognize patterns and correlations, and enhance their capabilities through accumulated experience.
Published in Chapter: ML, AI, and IoT as Driving Forces From Industry 5.0 Concepts for Better Global Food Security; From: Integrating Intelligence and Sustainability in Supply Chains
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Chapter 4
Big Brothers Are Seducing You: Consumerism, Surveillance, and the Agency of Consumers
A type of software technology that allows computers to learn from data and develop a capability without being explicitly programmed.
Published in Chapter: Big Brothers Are Seducing You: Consumerism, Surveillance, and the Agency of Consumers; From: Handbook of Research on Consumption, Media, and Popular Culture in the Global Age
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Chapter 5
Automating Network Management With Artificial Intelligence: In Software Networks and Beyond
An application of the field of artificial intelligence that relies on statistical, mathematical, and computer-based techniques to learn from a given data without being specifically programmed.
Published in Chapter: Automating Network Management With Artificial Intelligence: In Software Networks and Beyond; From: Emerging Automation Techniques for the Future Internet
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Chapter 4
Technological Help to Prevent and Reduce Cyberbullying
A field of artificial intelligence (AI) that aims to provide systems with the ability to automatically learn and improve from experience without being explicitly programmed.
Published in Chapter: Technological Help to Prevent and Reduce Cyberbullying; From: Cyberbullying and the Critical Importance of Educational Resources for Prevention and Intervention
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Chapter 85
Ensemble of ANN for Traffic Sign Recognition
Computer Scientific field focused on the design, analysis and implementation, of algorithms that learn from experience.
Published in Chapter: Ensemble of ANN for Traffic Sign Recognition; From: Encyclopedia of Artificial Intelligence
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Chapter 5
Fog-IoT-Assisted-Based Smart Agriculture Application
Machine learning is a branch of artificial intelligence (AI) and computer science which focuses on the use of data and algorithms to imitate the way that humans learn, gradually improving its accuracy.
Published in Chapter: Fog-IoT-Assisted-Based Smart Agriculture Application; From: Demystifying Federated Learning for Blockchain and Industrial Internet of Things
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Chapter 11
Can Artificial Intelligence and Big Data Improve Gamified Healthcare Services and Devices?
It refers to the study of computer algorithms that improve automatically through experience. It is seen as a subset of artificial intelligence.
Published in Chapter: Can Artificial Intelligence and Big Data Improve Gamified Healthcare Services and Devices?; From: Next-Generation Applications and Implementations of Gamification Systems
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Chapter 16
Optimization Techniques for Solar Energy System Design and Operation
Machine learning in solar energy systems involves using computational models and algorithms to analyze data and make predictions, optimize energy production, improve forecasting accuracy, and enhance system performance and efficiency.
Published in Chapter: Optimization Techniques for Solar Energy System Design and Operation; From: Fostering Cross-Industry Sustainability With Intelligent Technologies
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Chapter 4
Classification of the Emotional State of a Subject Using Machine Learning Algorithms for RehabRoby
Machine learning is the construction and study of systems that can learn without being explicitly programmed.
Published in Chapter: Classification of the Emotional State of a Subject Using Machine Learning Algorithms for RehabRoby; From: Handbook of Research on Advancements in Robotics and Mechatronics
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Chapter 5
A Patient-Centered Data-Driven Analysis of Epidural Anesthesia
A method of data analysis that automates analytical model building. It is a branch of artificial intelligence based on the idea that systems can learn from data, identify patterns, and make decisions with minimal human intervention.
Published in Chapter: A Patient-Centered Data-Driven Analysis of Epidural Anesthesia; From: Encyclopedia of Data Science and Machine Learning
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Chapter 21
The Impact of Augmented Reality Experiential Marketing on Tourist Experience Satisfaction
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Chapter 3
Big Data Applications in Business
A method of designing a sequence of actions to solve a problem that optimizes automatically through experience and with limited or no human intervention.
Published in Chapter: Big Data Applications in Business; From: Big Data Analytics for Entrepreneurial Success
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Chapter 9
Tourism in the Metaverse: Opportunities and Challenges
Is a subfield of engineering and computer science that evolved from the study of pattern recognition and the theory of computational learning in artificial intelligence, which gives computers the ability to learn without being explicitly programmed.
Published in Chapter: Tourism in the Metaverse: Opportunities and Challenges; From: Service Innovations in Tourism: Metaverse, Immersive Technologies, and Digital Twin
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Chapter 14
Analytics of User Behaviors on Twitter Using Machine Learning
The process of training a machine based on a set of data that provides a mapping from input to output.
Published in Chapter: Analytics of User Behaviors on Twitter Using Machine Learning; From: Handbook of Research on Artificial Intelligence Applications in Literary Works and Social Media
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Chapter 3
The Future of Artificial Intelligence in Agricultural Field: A Bibliometric Analysis
a scientific branch of artificial intelligence is a computer programming that allows to computers to learn to learn.
Published in Chapter: The Future of Artificial Intelligence in Agricultural Field: A Bibliometric Analysis; From: Intelligent Solutions for Optimizing Agriculture and Tackling Climate Change: Current and Future Dimensions
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Chapter 125
Artificial Intelligence, Big Data, and Machine Learning in Industry 4.0
A sub-field of artificial intelligence that utilizes data and algorithms in order to imitate the manner in which humans perceive things and learn that is, learning through experience.
Published in Chapter: Artificial Intelligence, Big Data, and Machine Learning in Industry 4.0; From: Encyclopedia of Data Science and Machine Learning
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Chapter 8
Machine Learning and Deep Learning for Big Data Analysis
Machine learning is a computational method that enables systems to automatically learn and improve from experience without being explicitly programmed.
Published in Chapter: Machine Learning and Deep Learning for Big Data Analysis; From: Big Data Analytics Techniques for Market Intelligence
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Chapter 64
Data Mining Fundamental Concepts and Critical Issues
Concerned with the development of algorithms and techniques, which allow computers to “learn”.
Published in Chapter: Data Mining Fundamental Concepts and Critical Issues; From: Encyclopedia of Artificial Intelligence
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Chapter 8
Emerging Technologies to Increase Energy Efficiency and Decrease Indoor Pollution in University Campuses
It is a learning technique that gives machines the ability to learn without being explicitly programmed. It is seen as a subset of Artificial Intelligence.
Published in Chapter: Emerging Technologies to Increase Energy Efficiency and Decrease Indoor Pollution in University Campuses; From: Strategy, Policy, Practice, and Governance for AI in Higher Education Institutions
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Chapter 18
Machine Learning in Text Analysis
Machine learning is branch of data science which has concern with the design and development of algorithm to develop a system that can learn from data, identify the complex patterns and provide intelligent, reliable, repeatable decisions and results with minimal human interaction based on the provided input.
Published in Chapter: Machine Learning in Text Analysis; From: Handbook of Research on Emerging Trends and Applications of Machine Learning
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Chapter 12
The Fourth Revolution: Strategies to Manage Artificial Intelligence to Transform Economy and Society for the Digital Age
The application of artificial intelligence (AI) to provides systems the ability to automatically learn and improve from experience.
Published in Chapter: The Fourth Revolution: Strategies to Manage Artificial Intelligence to Transform Economy and Society for the Digital Age; From: Handbook of Research on Management and Strategies for Digital Enterprise Transformation
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Chapter 1
Using Artificial Intelligence in Agroforestry as an Economic Solution for Carbon Recycling in Tanzania
Is a process of programming a machine with application of computer science and data to imitate the way human beings learn through trial and error in order to improve the accuracy of machine performance.
Published in Chapter: Using Artificial Intelligence in Agroforestry as an Economic Solution for Carbon Recycling in Tanzania; From: Intelligent Solutions for Optimizing Agriculture and Tackling Climate Change: Current and Future Dimensions
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Chapter 15
Big Data Application of Breast Cancer Prediction: A Healthcare 5.0 Application for Smart Cities
Machine learning is a branch of artificial intelligence that focuses on developing algorithms and models that enable computers to learn and make predictions or decisions without being explicitly programmed. It involves the use of statistical techniques and computational models to analyze and interpret vast amounts of data.s
Published in Chapter: Big Data Application of Breast Cancer Prediction: A Healthcare 5.0 Application for Smart Cities; From: Pioneering Smart Healthcare 5.0 with IoT, Federated Learning, and Cloud Security
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Chapter 2
Self-Driving Networks
A field of computer science that uses statistical techniques to give computer systems the ability to “learn” (e.g., progressively improve performance on a specific task) with data, without being explicitly programmed.
Published in Chapter: Self-Driving Networks; From: Emerging Automation Techniques for the Future Internet
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Chapter 14
Navigating the Journey: How Chatbots Are Transforming Tourism and Hospitality
Machine Learning is a subset of artificial intelligence that focuses on the development of algorithms and statistical models that enable computers to perform tasks without being explicitly programmed. Machine learning algorithms use data to learn patterns, make predictions, and improve performance over time.
Published in Chapter: Navigating the Journey: How Chatbots Are Transforming Tourism and Hospitality; From: Design and Development of Emerging Chatbot Technology
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Chapter 1
Innovative Digital Marketing in Business
Automatic learning or also machine learning or machine learning is a subfield of engineering and computer science that evolved from the study of pattern recognition and the theory of computational learning in artificial intelligence.
Published in Chapter: Innovative Digital Marketing in Business; From: Contemporary Trends in Innovative Marketing Strategies
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Chapter 59
Effect of Large-Scale Performed Vedic Homa Therapy on AQI
Machine learning (ML) is the study of computer algorithms that improve automatically through experience. It is seen as a subset of artificial intelligence. Machine learning algorithms build a mathematical model based on sample data, known as “training data”, in order to make predictions or decisions without being explicitly programmed to do so. Machine learning algorithms are used in a wide variety of applications, such as email filtering and computer vision, where it is difficult or infeasible to develop conventional algorithms to perform the needed tasks.
Published in Chapter: Effect of Large-Scale Performed Vedic Homa Therapy on AQI; From: Encyclopedia of Data Science and Machine Learning
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Chapter 4
Framework Blockchain Education: Rupture in Higher Education
An application of artificial intelligence whereby a machine develops the ability to learn without specific coding to do so.
Published in Chapter: Framework Blockchain Education: Rupture in Higher Education; From: Blockchain Technology Applications in Education
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Chapter 18
The Evolution of AI and Data Science
Machine Learning is the field of Artificial Intelligence that deals with the study of developing algorithms that help AI machines learn and enhance from experience.
Published in Chapter: The Evolution of AI and Data Science; From: The Ethical Frontier of AI and Data Analysis
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Chapter 36
How Artificial Intelligence Is Impacting Marketing?
Machine learning can be defined as making technological software think and act without human intervention.
Published in Chapter: How Artificial Intelligence Is Impacting Marketing?; From: Encyclopedia of Data Science and Machine Learning
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Chapter 12
Prediction of Temperature in Buildings using Machine Learning Techniques
A subfield of computer science and artificial intelligence which aims to develop techniques that allow machines to solve problems automatically. This learning is done by generalizing problems based on training data.
Published in Chapter: Prediction of Temperature in Buildings using Machine Learning Techniques; From: Handbook of Research on Advanced Computational Techniques for Simulation-Based Engineering
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Chapter 9
Inclusive Host Language Teaching: Official Texts for Migrant and Refugee People
Subfield of computer science that studies and develops computation algorithms that improve automatically through experience, based on sample data.
Published in Chapter: Inclusive Host Language Teaching: Official Texts for Migrant and Refugee People; From: Handbook of Research on Policies and Practices for Assessing Inclusive Teaching and Learning
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Chapter 5
Applying Machine Learning to Online Data?: Beware! Computational Social Science Requires Care
A subfield of artificial intelligence where models can learn patterns from the data in a supervised or unsupervised fashion and tune themselves during the learning process.
Published in Chapter: Applying Machine Learning to Online Data?: Beware! Computational Social Science Requires Care; From: Opportunities and Challenges for Computational Social Science Methods
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Chapter 6
Chatbots in Education: Addressing Student Needs and Transforming Learning in the Post-COVID-19 Era
An integral part of artificial intelligence (AI) focused on creating algorithms that empower computers to learn from patterns in data, enabling them to make predictions or decisions without explicit programming.
Published in Chapter: Chatbots in Education: Addressing Student Needs and Transforming Learning in the Post-COVID-19 Era; From: Building Resiliency in Higher Education: Globalization, Digital Skills, and Student Wellness
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Chapter 8
Artificial Intelligence in the Delivery of Mobile Tourism Services
The discipline concerned with the implementation of computer software that can learn autonomously.
Published in Chapter: Artificial Intelligence in the Delivery of Mobile Tourism Services; From: Mobile Computing and Technology Applications in Tourism and Hospitality
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Chapter 4
Cardiovascular Applications of Artificial Intelligence in Research, Diagnosis, and Disease Management
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Chapter 8
Pattern Analysis in Marine Data Classification and Recognition: A Plea for Ontologies
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Chapter 15
Surviving COVID-19 Crisis by New Business Models: A Case Study of the Indian Restaurant Industry
The process by which a computer can improve its performance (as in analyzing image files) by continuously incorporating new data into an existing statistical model.
Published in Chapter: Surviving COVID-19 Crisis by New Business Models: A Case Study of the Indian Restaurant Industry; From: Handbook of Research on Entrepreneurship, Innovation, Sustainability, and ICTs in the Post-COVID-19 Era
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Chapter 6
Is AI in Your Future?: AI Considerations for Scholarly Publishers
Machine learning is the study of computer algorithms that improve automatically through experience and by the use of data. It is seen as a part of artificial intelligence.
Published in Chapter: Is AI in Your Future?: AI Considerations for Scholarly Publishers; From: Transforming Scholarly Publishing With Blockchain Technologies and AI
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Chapter 19
Digital Strategy for a Sustainable Civilization
A type of computational algorithm that improves its ability to analyze data automatically through data input.
Published in Chapter: Digital Strategy for a Sustainable Civilization; From: The Strategies of Informing Technology in the 21st Century
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Chapter 5
Practical Metrics for Error Assessment with Interactive Museum Installations
A branch of artificial intelligence, concerns the construction and study of systems that can learn from some acquired data.
Published in Chapter: Practical Metrics for Error Assessment with Interactive Museum Installations; From: Handbook of Research on Interactive Information Quality in Expanding Social Network Communications
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Chapter 12
A Rural Healthcare Mobile App: Urdu Voice-Enabled Mobile App for Disease Diagnosis
It is a branch of artificial intelligence that uses large amounts of data to learn how to classify it or gain some insights from it. In some aspects, it strives to mimic a human brain’s learning from data.
Published in Chapter: A Rural Healthcare Mobile App: Urdu Voice-Enabled Mobile App for Disease Diagnosis; From: Empowering Sustainable Industrial 4.0 Systems With Machine Intelligence
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Chapter 1
Intelligent CALL: Using Pattern Matching to Learn English
When computers use artificial intelligence to learn from data or experience.
Published in Chapter: Intelligent CALL: Using Pattern Matching to Learn English; From: New Technological Applications for Foreign and Second Language Learning and Teaching
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Chapter 3
Bibliographical Analysis of Artificial Intelligence Learning in Higher Education: Is the Role of the Human Educator and Educated a Thing of the Past?
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Chapter 13
Digital Transformation: Impact of 5G Technology in Supply Chain Industry
Is a subset of AI that include abstruse statistical techniques that enable machines to improve at task with experience.
Published in Chapter: Digital Transformation: Impact of 5G Technology in Supply Chain Industry; From: Technology Optimization and Change Management for Successful Digital Supply Chains
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Chapter 13
Application of Machine Learning In Forensic Science
Machine learning is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed.
Published in Chapter: Application of Machine Learning In Forensic Science; From: Critical Concepts, Standards, and Techniques in Cyber Forensics
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Chapter 149
Spatial Audio Coding and Machine Learning
Machine learning is a computer programming technique that uses statistical probabilities to give computers the ability to learn on their own without explicit programming.
Published in Chapter: Spatial Audio Coding and Machine Learning; From: Encyclopedia of Data Science and Machine Learning
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Chapter 9
Social Perspective of Suspicious Activity Detection in Facial Analysis: An ML-Based Approach for Digital Transformation
Machine learning (ML) is the scientific study of algorithms and statistical models that computer systems use to perform a specific task without using explicit instructions, relying on patterns and inference instead. It is seen as a subset of artificial intelligence.
Published in Chapter: Social Perspective of Suspicious Activity Detection in Facial Analysis: An ML-Based Approach for Digital Transformation; From: Applied AI and Multimedia Technologies for Smart Manufacturing and CPS Applications
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Chapter 8
AI-Driven Big Healthcare Analytics: Contributions and Challenges
A branch of artificial intelligence that is based on data acquisitions and algorithms developments to make decisions without explicit programs.
Published in Chapter: AI-Driven Big Healthcare Analytics: Contributions and Challenges; From: Intelligent Analytics With Advanced Multi-Industry Applications
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Chapter 8
Harnessing the Power of Machine Learning for Parkinson's Disease Detection
It is a subfield of artificial intelligence (AI) that focuses on using data and algorithms to mimic human learning processes and progressively increase accuracy.
Published in Chapter: Harnessing the Power of Machine Learning for Parkinson's Disease Detection; From: AIoT and Smart Sensing Technologies for Smart Devices
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Chapter 21
Machine Learning in Morphological Segmentation
As a broad subfield of artificial intelligence, machine learning is concerned with the design and development of algorithms and techniques that allow computers to “learn”.
Published in Chapter: Machine Learning in Morphological Segmentation; From: Handbook of Research on Advanced Techniques in Diagnostic Imaging and Biomedical Applications
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Chapter 70
Machine Learning and Cyber Security: Future Potential of the Research
Is the use of artificial intelligence (AI) that provides systems with the capability to learn and automatically improve from experience (data) without being explicitly programmed.
Published in Chapter: Machine Learning and Cyber Security: Future Potential of the Research; From: Encyclopedia of Criminal Activities and the Deep Web
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Chapter 16
Technology in Healthcare: Vision of Smart Hospitals
It is an application of artificial intelligence which makes the computer programs learn by themselves using the existing data and experiences.
Published in Chapter: Technology in Healthcare: Vision of Smart Hospitals; From: Handbook of Research on Engineering, Business, and Healthcare Applications of Data Science and Analytics
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Chapter 215
Support Vector Machine Models for Classification
Machine learning is a branch of artificial intelligence that constructs systems, such as mathematical equations, from available data and generalizes the constructed system to new data.
Published in Chapter: Support Vector Machine Models for Classification; From: Encyclopedia of Business Analytics and Optimization
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Chapter 9
Digital Recognition of Breast Cancer Using TakhisisNet: An Innovative Multi-Head Convolutional Neural Network for Classifying Breast Ultrasonic Images
Machine learning is an application of artificial intelligence that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. Machine learning focuses on the development of computer programs that can access data and use it learn for themselves.
Published in Chapter: Digital Recognition of Breast Cancer Using TakhisisNet: An Innovative Multi-Head Convolutional Neural Network for Classifying Breast Ultrasonic Images; From: Opportunities and Challenges in Digital Healthcare Innovation
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Chapter 11
Adaptive Clinical Treatments and Reinforcement Learning for Automatic Disease diagnosis
Machine learning (ML) is a type of artificial intelligence (AI) that allows software applications to become more accurate at predicting outcomes without being explicitly programmed to do so.
Published in Chapter: Adaptive Clinical Treatments and Reinforcement Learning for Automatic Disease diagnosis; From: AI-Enabled Multiple-Criteria Decision-Making Approaches for Healthcare Management
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Chapter 16
Tuning Drone Data Delivery and Analysis on the Public Cloud
The process in which a computer learns a little bit in every iteration of an algorithm.
Published in Chapter: Tuning Drone Data Delivery and Analysis on the Public Cloud; From: Encyclopedia of Information Science and Technology, Fifth Edition
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Chapter 1
Artificial Intelligence in Modern Medical Science: A Promising Practice
Machine learning is essentially the analysis of computer algorithms that automatically correct themselves through repetition. It is also referred to as an AI application.
Published in Chapter: Artificial Intelligence in Modern Medical Science: A Promising Practice; From: Recent Developments in Machine and Human Intelligence
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Chapter 18
Intelligent Slotting for the Warehouse
Machine learning explores the building and analysis of algorithms that can make predictions from the study of data. These algorithms function by creating a model from sample inputs to generate data-driven forecasts or conclusions instead of being limited to precise fixed program directives.
Published in Chapter: Intelligent Slotting for the Warehouse; From: Handbook of Research on Strategic Supply Chain Management in the Retail Industry
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Chapter 27
Data Mining: Payoffs and Pitfalls
Concerned with the development of algorithms and techniques which allow computers to “learn.”
Published in Chapter: Data Mining: Payoffs and Pitfalls; From: Encyclopedia of Information Technology Curriculum Integration
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Chapter 1
Implications of Digital Transformation on the Strategy Development Process for Business Leaders
Together with artificial intelligence, machine learning, ML, is being rated as the most general-purpose technology of the digital era, as both a disruptive technology and source of competitive advantage. Simply, machine learning is the ability to keep improving performance without human intervention on how tasks should be completed and is an excellent learner in that a wide range of tasks can be achieved at superhuman performance levels.
Published in Chapter: Implications of Digital Transformation on the Strategy Development Process for Business Leaders; From: Promoting Inclusive Growth in the Fourth Industrial Revolution
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Chapter 5
Leadership and Strategic Decision Making Under Exogenous Shocks Such as COVID-19
Is the use and development of computer systems that are able to learn and adapt without following explicit instructions, by using algorithms and statistical models to analyze and draw inferences from patterns in data.
Published in Chapter: Leadership and Strategic Decision Making Under Exogenous Shocks Such as COVID-19; From: Business Models to Promote Technology, Culture, and Leadership in Post-COVID-19 Organizations
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Chapter 11
Leveraging Machine Learning Techniques to Improve Learning and Recommendations Within Dairy Farms: Towards High Milk Yields for Small-Scale Farmers
A collection of methods and tools for creating software programmes that can analyses previous data and make precise predictions about the future.
Published in Chapter: Leveraging Machine Learning Techniques to Improve Learning and Recommendations Within Dairy Farms: Towards High Milk Yields for Small-Scale Farmers; From: Impact of Disruptive Technologies on the Socio-Economic Development of Emerging Countries
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Chapter 229
Teaching Machines to Find Names
An area of study concerning the development of techniques which allow machines or computers to improve their performance based on previous results and learning experience.
Published in Chapter: Teaching Machines to Find Names; From: Encyclopedia of Artificial Intelligence
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Chapter 6
Automatic Quality Assessment for Internet Pages
Machine learning is a subfield of artificial intelligence which provides algorithms for the discovery of relations or rules in large data sets. Machine learning leads to functions which can automatically classify or categorize objects based on their features. Inductive learning from labeled examples is the most well known application.
Published in Chapter: Automatic Quality Assessment for Internet Pages; From: Handbook of Research on Web Information Systems Quality
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Chapter 7
Smart Gateways for IOT-Factory Integration: Trends and Use Case
A scientific discipline within Artificial Intelligent which creates automatic learning systems.
Published in Chapter: Smart Gateways for IOT-Factory Integration: Trends and Use Case; From: Technological Developments in Industry 4.0 for Business Applications
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Chapter 8
Guaranteeing User Rates With Reinforcement Learning in 5G Radio Access Networks
Category of algorithm that uses statistical techniques to analyze data and make predictions.
Published in Chapter: Guaranteeing User Rates With Reinforcement Learning in 5G Radio Access Networks; From: Next-Generation Wireless Networks Meet Advanced Machine Learning Applications
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Chapter 3
Opinion Mining in Tourism: A Study on “Cappadocia Home Cooking” Restaurant
Machine Learning is the general name of computer algorithms (Decision Trees, Naïve Bayes, Logistic Regression, Random Forest) that model a given problem according to the data obtained from the problem environment. Since it is an intensively studied subject, many approaches and algorithms have been proposed.
Published in Chapter: Opinion Mining in Tourism: A Study on “Cappadocia Home Cooking” Restaurant; From: Handbook of Research on Smart Technology Applications in the Tourism Industry
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Chapter 7
Limitations and Implications of Doubling Time Approach in COVID-19 Infection Spreading Study: A Gradient Smoothing Technique
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Chapter 11
Role of Data Science and Data Analytics in Forensic Accounting and Fraud Detection
Is a type of artificial intelligence where computer teaches itself the solution to a query discovering patterns in sets of data and matching fresh parts of data the based on probability.
Published in Chapter: Role of Data Science and Data Analytics in Forensic Accounting and Fraud Detection; From: Handbook of Research on Engineering, Business, and Healthcare Applications of Data Science and Analytics
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Chapter 4
Corruption Levels and Country Cluster: A Comparative Analysis
Branch of statistics that involves developing algorithms that can learn from data and make predictions or decisions. It is used in a wide range of applications, from image recognition and natural language processing to fraud detection and recommendation systems.
Published in Chapter: Corruption Levels and Country Cluster: A Comparative Analysis; From: Management Strategies and Tools for Addressing Corruption in Public and Private Organizations
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Chapter 75
Big Data Visualization of Association Rules and Frequent Patterns
As a part of artificial intelligence (AI), machine learning aims to improve computer algorithms automatically through experience and by the use of data.
Published in Chapter: Big Data Visualization of Association Rules and Frequent Patterns; From: Encyclopedia of Data Science and Machine Learning
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Chapter 2
Viral Invasion Flow-Chart for Pathogens With Replication Target in a Host Cell
ML are methods inspired from Artificial Intelligence that leverage data to improve performance on some set of tasks, based on history.
Published in Chapter: Viral Invasion Flow-Chart for Pathogens With Replication Target in a Host Cell; From: Recent Advancements in Smart Remote Patient Monitoring, Wearable Devices, and Diagnostics Systems
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Chapter 12
Smart Medication Management, Current Technologies, and Future Directions
A field of computer science and artificial intelligence which enables computers to learn without being explicitly programmed.
Published in Chapter: Smart Medication Management, Current Technologies, and Future Directions; From: Handbook of Research on Healthcare Administration and Management
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Chapter 239
Web Page Classification Using MDAWkNN
Is a branch of artificial intelligence that deals with study and construction of intelligent systems by analyzing data. Data mining has its roots in machine learning.
Published in Chapter: Web Page Classification Using MDAWkNN; From: Encyclopedia of Business Analytics and Optimization
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Chapter 14
A Framework for Integrating Artificial Intelligence Into Library and Information Science Curricula
Refers to the concept that computer programs can automatically learn from and adapt to new data without being assisted by humans.
Published in Chapter: A Framework for Integrating Artificial Intelligence Into Library and Information Science Curricula; From: Innovative Technologies for Enhancing Knowledge Access in Academic Libraries
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Chapter 17
Simulation of Temperature and Precipitation under the Climate Change Scenarios: Integration of a GCM and Machine Learning Approaches
A statistical approach that deals with the modelling constructed from some algorithms that can learn from inputs for a dependent variable.
Published in Chapter: Simulation of Temperature and Precipitation under the Climate Change Scenarios: Integration of a GCM and Machine Learning Approaches; From: Handbook of Research on Advanced Computational Techniques for Simulation-Based Engineering
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Chapter 10
Online Social Networks Misuse, Cyber Crimes, and Counter Mechanisms
Machine learning is an application of artificial intelligence (AI) that provides systems with the ability to learn and improve from experience without being explicitly programmed automatically.
Published in Chapter: Online Social Networks Misuse, Cyber Crimes, and Counter Mechanisms; From: Analyzing Global Social Media Consumption
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Chapter 18
Retrieval of Information Through Botnet Attacks: The Importance of Botnet Detection in the Modern Era
The use and development of computer systems that can learn and adapt without following explicit instructions, by using algorithms and statistical models to analyze and draw inferences from patterns in data.
Published in Chapter: Retrieval of Information Through Botnet Attacks: The Importance of Botnet Detection in the Modern Era; From: Handbook of Research on Technical, Privacy, and Security Challenges in a Modern World
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Chapter 1
Engineering AI Systems: A Research Agenda
The study of computer algorithms that improve automatically through experience. It is seen as a subset of artificial intelligence. Machine learning algorithms build a mathematical model based on sample data, known as “training data”, to make predictions or decisions without being explicitly programmed to do so.
Published in Chapter: Engineering AI Systems: A Research Agenda; From: Artificial Intelligence Paradigms for Smart Cyber-Physical Systems
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Chapter 7
Machine Learning: A Revolution in Accounting
Machine learning, a subset of artificial intelligence, is a field devoted to the development of techniques that enable computer systems to learn from data. Unlike traditional programming, where explicit instructions are given to a computer to perform a task, machine learning involves the construction of statistical models that can be trained to recognize patterns and make decisions autonomously.
Published in Chapter: Machine Learning: A Revolution in Accounting; From: Artificial Intelligence Approaches to Sustainable Accounting
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Chapter 5
Implementation of Deep Learning Neural Network for Retinal Images
Machine learning is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. Machine learning focuses on the development of computer programs that can access data and use it learn for themselves.
Published in Chapter: Implementation of Deep Learning Neural Network for Retinal Images; From: Handbook of Research on Applications and Implementations of Machine Learning Techniques
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Chapter 9
Deep Learning Approach for Detecting Customer Churn in Telecommunication Industry
This is the case of algorithms that learn on their own through experience and data intake.
Published in Chapter: Deep Learning Approach for Detecting Customer Churn in Telecommunication Industry; From: Social Customer Relationship Management (Social-CRM) in the Era of Web 4.0
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Chapter 7
Identifying COVID-19 Cases Rapidly and Remotely Using Big Data Analytics
A subject of artificial intelligence that aims at the task of computational algorithms, which allow machines to learning objects automatically through historical data.
Published in Chapter: Identifying COVID-19 Cases Rapidly and Remotely Using Big Data Analytics; From: Applied Big Data Analytics and Its Role in COVID-19 Research
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Chapter 292
Support of Online Learning through Intelligent Programs
A sub-field of artificial intelligence. The idea is that a computing system could perhaps learn to solve problems in much the same way that humans do, that is to say, by example. A program is needed which learns the concepts of a domain under varying degrees of supervision from a human teacher. In one approach, the teacher presents the program with a set of examples of a concept, and the program’s task is to identify what collection of attributes and values defines the concept.
Published in Chapter: Support of Online Learning through Intelligent Programs; From: Encyclopedia of Distance Learning, Second Edition
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Chapter 2
Data Avalanche: Harnessing for Mobile Payment Fraud Detection Using Machine Learning
Machine learning focuses on the development of computer programs that can access data and use it to learn for themselves.
Published in Chapter: Data Avalanche: Harnessing for Mobile Payment Fraud Detection Using Machine Learning; From: Handbook of Research on Managing Information Systems in Developing Economies
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Chapter 11
Artificial Intelligence and Reliability Metrics in Medical Image Analysis
A sub-branch of AI is said to be the Machine Learning (ML) technology, which involves the system to learn from the occurred data that does not need any explicit programming applied in the field of imaging.
Published in Chapter: Artificial Intelligence and Reliability Metrics in Medical Image Analysis; From: Deep Neural Networks for Multimodal Imaging and Biomedical Applications
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Chapter 14
Artificial Intelligence in Education: A Closer Look Into Intelligent Tutoring Systems
Machine learning is an automatic computer program that allows the computer to learn without being programmed.
Published in Chapter: Artificial Intelligence in Education: A Closer Look Into Intelligent Tutoring Systems; From: Handbook of Research on Critical Issues in Special Education for School Rehabilitation Practices
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Chapter 7
Transformation of Marketing With Technology: Case Approach for Artificial Intelligence
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Chapter 1
Cyberbullying: A Research Overview
A subfield of artificial intelligence that focuses on the development of computer programs that can teach themselves without being explicitly programmed.
Published in Chapter: Cyberbullying: A Research Overview; From: Cyberbullying and the Critical Importance of Educational Resources for Prevention and Intervention
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Chapter 2
The Intersection Between Artificial Intelligence and Environmental Sustainability: A Bibliometric Analysis
The discipline that studies how to endow computer systems with autonomous learning capability.
Published in Chapter: The Intersection Between Artificial Intelligence and Environmental Sustainability: A Bibliometric Analysis; From: Exploring Ethical Dimensions of Environmental Sustainability and Use of AI
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Chapter 14
Assessment of Machine Learning Techniques for Improving Agriculture Crop Production
Machine learning optimizes tasks like irrigation and pest control in agriculture. Real-time decisions benefit agricultural production through adaptable machine learning models.
Published in Chapter: Assessment of Machine Learning Techniques for Improving Agriculture Crop Production; From: Handbook of Research on Innovative Approaches to Information Technology in Library and Information Science
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Chapter 169
Recommendation Systems
Machine learning is an artificial intelligence technology that allows computers to learn without being explicitly programmed for this purpose.
Published in Chapter: Recommendation Systems; From: Encyclopedia of Data Science and Machine Learning
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Chapter 8
A Blockchain-Based Federated Learning: Concepts and Applications
It is a subset of Artificial Intelligence in which Machines learns from the data rather than being programmed explicitly.
Published in Chapter: A Blockchain-Based Federated Learning: Concepts and Applications; From: Multidisciplinary Functions of Blockchain Technology in AI and IoT Applications
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Chapter 168
Hybrid Machine Learning for Matchmaking in Digital Business Ecosystems
Machine learning is a branch of artificial intelligence (AI) and computing that focuses on experience, the use of data, and algorithms to automate the building of analytical models. Machine learning algorithms build a model based on sample data, called “training data”, in order to make predictions or decisions without being explicitly programmed to do so and to mimic the way humans learn, gradually improving its accuracy.
Published in Chapter: Hybrid Machine Learning for Matchmaking in Digital Business Ecosystems; From: Encyclopedia of Data Science and Machine Learning
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Chapter 23
Prognostication of Crime Using Bagging Regression Model: A Case Study of London
Artificial intelligence, which is widely defined as a machine’s ability to mimic intelligent human behavior, includes the subfield of machine learning. Artificial intelligence (AI) systems are used to carry out complicated tasks like how people solve issues. Machine learning enables software programs to predict outcomes more accurately without being expressly designed. In order to forecast new output values, machine learning algorithms use historical data as input.
Published in Chapter: Prognostication of Crime Using Bagging Regression Model: A Case Study of London; From: Handbook of Research on Technological Advances of Library and Information Science in Industry 5.0
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Chapter 26
Data Science in Economics and Business: Roots and Applications
Use algorithms to parse data, learn from that data and make an informed decision based on what it has learned.
Published in Chapter: Data Science in Economics and Business: Roots and Applications; From: Handbook of Research on Applied Data Science and Artificial Intelligence in Business and Industry
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Chapter 17
Machine Learning and Sentiment Analysis for Analyzing Customer Feedback: A Review
It is a field of study in artificial intelligence that tries to make machines learn automatically without explicit programming.
Published in Chapter: Machine Learning and Sentiment Analysis for Analyzing Customer Feedback: A Review; From: Big Data Analytics Techniques for Market Intelligence
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Chapter 15
Precious Metal Prediction by Using XAI in the Perspective of Digital Transformation
Machine learning is an artificial intelligence (AI) application that gives systems the ability to automatically learn and develop from experience without being explicitly programmed.
Published in Chapter: Precious Metal Prediction by Using XAI in the Perspective of Digital Transformation; From: Emerging Challenges, Solutions, and Best Practices for Digital Enterprise Transformation
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Chapter 34
Business Intelligence Applied to Tourism
Specific aspect of Artificial Intelligence, which considers that systems can learn from data, identify patterns, and make decisions with a minimum of human intervention, gradually improving its accuracy.
Published in Chapter: Business Intelligence Applied to Tourism; From: Encyclopedia of Data Science and Machine Learning
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Chapter 4
Enhancing Self-Regulated Learning With Artificial Intelligence-Powered Learning Analytics
It refers to employing computer algorithms to discover and learn patterns and trends within a given dataset without explicit instruction.
Published in Chapter: Enhancing Self-Regulated Learning With Artificial Intelligence-Powered Learning Analytics; From: Emergent Practices of Learning Analytics in K-12 Classrooms
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Chapter 3
Generative AI and Generative Pre-Trained Transformer Applications: Challenges and Opportunities
Is a field of study in artificial intelligence concerned with the development and study of statistical algorithms that can learn from data and generalize to unseen data, and thus perform tasks without explicit instructions.
Published in Chapter: Generative AI and Generative Pre-Trained Transformer Applications: Challenges and Opportunities; From: Making Art With Generative AI Tools
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Chapter 184
Improving Transportation Planning Using Machine Learning
A computer programming technique that enables computers to learn from historical data and make predictions into the future.
Published in Chapter: Improving Transportation Planning Using Machine Learning; From: Encyclopedia of Data Science and Machine Learning
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Chapter 5
How Artificial Intelligence Can Help Accounting in Information Management
Computer science that focuses on using data and algorithms to mimic the way humans learn.
Published in Chapter: How Artificial Intelligence Can Help Accounting in Information Management; From: Artificial Intelligence Approaches to Sustainable Accounting
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Chapter 8
Machine Learning Techniques to Identify and Characterize Sleep Disorders Using Biosignals
It is defined as group of techniques that combine mathematics with computational processing to learn patterns in a set of data with different purposes: classification, prediction, etc.
Published in Chapter: Machine Learning Techniques to Identify and Characterize Sleep Disorders Using Biosignals; From: Advancing the Investigation and Treatment of Sleep Disorders Using AI
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Chapter 4
Development and Mitigation of Android Malware
Study and analysis of system data to teach computers to learn.
Published in Chapter: Development and Mitigation of Android Malware; From: Handbook of Research on Digital Crime, Cyberspace Security, and Information Assurance
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Chapter 4
First of All, Understand Data Analytics Context and Changes
A method of designing a sequence of actions to solve a problem that optimizes automatically through experience and with limited or no human intervention.
Published in Chapter: First of All, Understand Data Analytics Context and Changes; From: Big Data Analytics for Entrepreneurial Success
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Chapter 1
The Elements of Intelligent Business Analytics: Principles, Techniques, and Tools
Concerned about how computer can adapt to new circumstances and to detect and extrapolate patterns.
Published in Chapter: The Elements of Intelligent Business Analytics: Principles, Techniques, and Tools; From: Handbook of Research on Foundations and Applications of Intelligent Business Analytics
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Chapter 1
The Role and Applications of Machine Learning in Future Self-Organizing Cellular Networks
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Chapter 12
Machine Learning Approach to Detect Online Shopping Addiction and Study the Influencing Factors for Addiction
Machine learning (ML) is a subset of artificial intelligence (AI) that enables software programmes to grow increasingly effective at predicting outcomes without explicitly programming them.
Published in Chapter: Machine Learning Approach to Detect Online Shopping Addiction and Study the Influencing Factors for Addiction; From: Handbook of Research on Technological Advances of Library and Information Science in Industry 5.0
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Chapter 53
Machine Learning Algorithms in Human Gait Analysis
The creation and utilisation of computer algorithms that identify patterns in data without explicit instructions through the use of statistical and optimisation methods.
Published in Chapter: Machine Learning Algorithms in Human Gait Analysis; From: Encyclopedia of Data Science and Machine Learning
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Chapter 6
Evolution and Tendency on the Feature Extraction Process for Diagnostic Aid in Healthcare
Branch of artificial intelligence (AI) and computer science that focuses on using data and algorithms to mimic the way humans learn, with gradually improving accuracy.
Published in Chapter: Evolution and Tendency on the Feature Extraction Process for Diagnostic Aid in Healthcare; From: Recent Advancements in Smart Remote Patient Monitoring, Wearable Devices, and Diagnostics Systems
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Chapter 5
Use of Big Data Analytics by Tax Authorities
Where a computer program or algorithm ‘learns’ how to select or filter input data by assessing previous cases or instances and making correlations or judgements.
Published in Chapter: Use of Big Data Analytics by Tax Authorities; From: Legal Regulations, Implications, and Issues Surrounding Digital Data
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Chapter 10
The Impact of Decentralized Technologies on Social Media Megacorporations
Is a branch of artificial intelligence (AI) and computer science which focuses on the use of algorithms to make inferences from patterns in data.
Published in Chapter: The Impact of Decentralized Technologies on Social Media Megacorporations; From: Applied Ethics in a Digital World
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Chapter 7
Advanced Machine Learning Techniques in Spam Medical Events: Review of Literature
Is a form of artificial intelligence (AI) that focuses on creating systems that learn, or improve their performance, based on the data they process.
Published in Chapter: Advanced Machine Learning Techniques in Spam Medical Events: Review of Literature; From: Advanced Bioinspiration Methods for Healthcare Standards, Policies, and Reform
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Chapter 8
Nature-Inspired Techniques for Data Security in Big Data
Field of study of algorithms and statistical models which enables the computer to perform tasks without being specifically programmed.
Published in Chapter: Nature-Inspired Techniques for Data Security in Big Data; From: Security, Privacy, and Forensics Issues in Big Data
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Chapter 4
How to Implement the Supply Chain Digital Segmentation Strategy Successfully
According to Wikipedia: Machine learning is a subset of artificial intelligence in the field of computer science that often uses statistical techniques to give computers the ability to “learn” (i.e., progressively improve performance on a specific task) with data, without being explicitly programmed.
Published in Chapter: How to Implement the Supply Chain Digital Segmentation Strategy Successfully; From: Digital Supply Chain, Disruptive Environments, and the Impact on Retailers
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Chapter 1
Overview of Big Data and Its Visualization
A process that gives machine the ability to learn without being explicitly programmed.
Published in Chapter: Overview of Big Data and Its Visualization; From: Handbook of Research on Big Data Storage and Visualization Techniques
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Chapter 235
Affect Awareness Support and Communication Technologies in Education
It is a scientific field belonging to artificial intelligence and it mainly concerns mathematical processes and classification algorithms which are getting trained from data.
Published in Chapter: Affect Awareness Support and Communication Technologies in Education; From: Encyclopedia of Information Science and Technology, Third Edition
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Chapter 25
Toward the 4th Agenda 2030 Goal: AI Support to Executive Functions for Inclusions
A facet of AI that focuses on algorithms, allowing machines to learn without being programmed and change when exposed to new data.
Published in Chapter: Toward the 4th Agenda 2030 Goal: AI Support to Executive Functions for Inclusions; From: Handbook of Research on Teaching With Virtual Environments and AI
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Chapter 15
Artificial Intelligence Methods in E-Learning
One of the branches of artificial intelligence developing algorithms and technologies that makes the computer able to learn from experiences.
Published in Chapter: Artificial Intelligence Methods in E-Learning; From: Handbook of Research on Faculty Development for Digital Teaching and Learning
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Chapter 626
User Profile Modeling and Learning
The method for processing a training input and offering support for decision based on this input.
Published in Chapter: User Profile Modeling and Learning; From: Encyclopedia of Information Science and Technology, Second Edition
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Chapter 8
Thermography in Biomedicine: History and Breakthrough
Study of algorithms and statistical models that computers use to perform tasks for which they do not have instructions. Instead, they rely on patterns and they use acquired data to make predictions or decisions. This is one of the steps of artificial intelligence.
Published in Chapter: Thermography in Biomedicine: History and Breakthrough; From: Biomedical Computing for Breast Cancer Detection and Diagnosis
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Chapter 1
Artificial Intelligence and Machine Learning Education and Literacy: Teacher Training for Primary and Secondary Education Teachers
Machine Learning is a field of Artificial Intelligence through which a computing process progressively adapts and improves its performance in a specific task or set of tasks, by analyzing large amounts of data. It largely involves the paradigms of unsupervised, supervised and reinforcement learning.
Published in Chapter: Artificial Intelligence and Machine Learning Education and Literacy: Teacher Training for Primary and Secondary Education Teachers; From: Handbook of Research on Integrating ICTs in STEAM Education
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Chapter 17
Prediction of Remaining Useful Life of Batteries Using Machine Learning Models
It is a branch of artificial intelligence that enables computers to learn and make predictions or decisions without explicit programming. It uses algorithms and statistical techniques to analyse data, identify patterns, and improve performance over time. There are three main types: supervised learning (using labelled data), unsupervised learning (finding patterns in unlabelled data), and reinforcement learning (learning through trial and error). Machine learning finds applications in various fields, such as image recognition, natural language processing, and fraud detection, by training models to make accurate predictions and decisions based on data analysis.
Published in Chapter: Prediction of Remaining Useful Life of Batteries Using Machine Learning Models; From: Enhancing Performance, Efficiency, and Security Through Complex Systems Control
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Chapter 7
Protect Nature and Reduce the Carbon Footprint With an Application of Blockchain for IIoT
Machine learning is a branch of artificial intelligence (AI) and computer science which focuses on the use of data and algorithms to imitate the way that humans learn, gradually improving its accuracy.
Published in Chapter: Protect Nature and Reduce the Carbon Footprint With an Application of Blockchain for IIoT; From: Demystifying Federated Learning for Blockchain and Industrial Internet of Things
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Chapter 11
Why Is Data So Hard?: Challenges, Solutions, and Future Directions
The concept that a computer learns something after being fed a great deal of data from which to acquire knowledge.
Published in Chapter: Why Is Data So Hard?: Challenges, Solutions, and Future Directions; From: Technology Leadership for Innovation in Higher Education
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Chapter 8
An Overview of Biomedical Image Analysis From the Deep Learning Perspective
It is an application of the artificial intelligence in which machines can automatically learn and solve problems using the learned experience.
Published in Chapter: An Overview of Biomedical Image Analysis From the Deep Learning Perspective; From: Applications of Advanced Machine Intelligence in Computer Vision and Object Recognition: Emerging Research and Opportunities
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Chapter 70
Data Science Methodology
One of the concepts for obtaining artificial intelligence is machine learning. A machine should be enabled to independently gain and process knowledge through algorithms and statistical models.
Published in Chapter: Data Science Methodology; From: Encyclopedia of Data Science and Machine Learning
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Chapter 2
An Enterprise Integration Method for Machine Learning-Driven Business Systems
A field of study that gives computers the ability to learn without being explicitly programmed.
Published in Chapter: An Enterprise Integration Method for Machine Learning-Driven Business Systems; From: AI-Driven Intelligent Models for Business Excellence
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Chapter 11
Challenges of Developing AI Applications in the Evolving Digital World and Recommendations to Mitigate Such Challenges: A Conceptual View
The study of algorithms and methods that help machines to learn implicitly without the help of instructions from the patterns in the data fed to the algorithms. This is an emerging trend of automation and is an integral part of artificial intelligence.
Published in Chapter: Challenges of Developing AI Applications in the Evolving Digital World and Recommendations to Mitigate Such Challenges: A Conceptual View; From: Confluence of AI, Machine, and Deep Learning in Cyber Forensics
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Chapter 13
Analysis of System Marginal Price in the Turkish Electricity Market
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Chapter 9
Deep Learning Approach for Extracting Catch Phrases from Legal Documents
A field of study of algorithms and statistical methods that allows software application to predict the accurate result.
Published in Chapter: Deep Learning Approach for Extracting Catch Phrases from Legal Documents; From: Neural Networks for Natural Language Processing
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Chapter 11
Libraries in a World of Data: How to Move Forward While Protecting Users
The use of “smart” (advanced and specialized) computer algorithms that “learn” (improve operations) from existing data and use this knowledge (refined operations) to process future data inputs.
Published in Chapter: Libraries in a World of Data: How to Move Forward While Protecting Users; From: Technological Advancements in Library Service Innovation
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Chapter 19
Review on Artificial Intelligence and Robots in STEAM Education for Early Childhood Development: The State-of-the-Art Tools and Applications
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Chapter 8
Cognitive Integrated Business Planning
Machine learning is a field of artificial intelligence that uses statistical techniques to understand the patterns behind the data, establish co-relation between those patterns and “learn” from data, without being explicitly programmed.
Published in Chapter: Cognitive Integrated Business Planning; From: Technology Optimization and Change Management for Successful Digital Supply Chains
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Chapter 1
Educational Data Mining: A Guide for Educational Researchers
A discipline focused on the development and evaluation of algorithms that permit computers to use patterns, trends, and associations in data to perform tasks without being programmed by a human.
Published in Chapter: Educational Data Mining: A Guide for Educational Researchers; From: Advancing Educational Research With Emerging Technology
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Chapter 6
Natural Language Processing (NLP) in Chatbot Design: NLP's Impact on Chatbot Architecture
Machine Learning (ML) is a branch of artificial intelligence that lets computers learn from their mistakes and get better over time without being told to do so. Algorithms for machine learning look at data to find trends, make predictions, and change as needed.
Published in Chapter: Natural Language Processing (NLP) in Chatbot Design: NLP's Impact on Chatbot Architecture; From: Design and Development of Emerging Chatbot Technology
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Chapter 12
Human Motion Analysis and Simulation Tools: A Survey
Is a discipline that focuses on the study of algorithms that are able to learn from data and self-improve through experience.
Published in Chapter: Human Motion Analysis and Simulation Tools: A Survey; From: Handbook of Research on Computational Simulation and Modeling in Engineering
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Chapter 1
Artificial Intelligence in Computer Science
It is a subfield of AI, which is generally characterised as a machine's capacity to replicate intelligent human behaviour.
Published in Chapter: Artificial Intelligence in Computer Science; From: Advances in Artificial and Human Intelligence in the Modern Era
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Chapter 3
The Effect of Industrial Automation and Artificial Intelligence on Supply Chains With the Onset of COVID-19
The branch of computer science that is related to training models on data set and uses advance mathematics and algorithms for prediction.
Published in Chapter: The Effect of Industrial Automation and Artificial Intelligence on Supply Chains With the Onset of COVID-19; From: Revolutionizing Industrial Automation Through the Convergence of Artificial Intelligence and the Internet of Things
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Chapter 2
Applications of Gene Expression Programming for Estimating CFRP Wrapping Effects on the Bond Strength After Elevated Temperature Exposure
Development and use of computer systems with the ability to learn and adapt without following explicit instructions, using various algorithms and statistical models to analyze and draw inferences from patterns in data.
Published in Chapter: Applications of Gene Expression Programming for Estimating CFRP Wrapping Effects on the Bond Strength After Elevated Temperature Exposure; From: Multidisciplinary Approach to Information Technology in Library and Information Science
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Chapter 16
Studying Individualized Transit Indicators Using a New Low-Cost Information System
Branch of artificial intelligence, concerns the construction and study of systems that can learn from data.
Published in Chapter: Studying Individualized Transit Indicators Using a New Low-Cost Information System; From: Handbook of Research on Embedded Systems Design
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Chapter 8
Intelligent Automation Using Machine and Deep Learning in Cybersecurity of Industrial IoT: CCTV Security and DDoS Attack Detection
It refers to an application of artificial intelligence focusing on algorithms which can be used for building models (e.g., based on statistics) from input data. Such automatic analytical models need to provide outputs based on the learning relations between input and output values. The algorithms are often categorized as supervised, semi-supervised or unsupervised.
Published in Chapter: Intelligent Automation Using Machine and Deep Learning in Cybersecurity of Industrial IoT: CCTV Security and DDoS Attack Detection; From: Cyber Security of Industrial Control Systems in the Future Internet Environment
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Chapter 14
Implementation of an Intelligent Model Based on Machine Learning in the Application of Macro-Ergonomic Methods in a Human Resources Process Based on ISO 12207
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Chapter 6
Big Data Is Decision Science: The Case of COVID-19 Vaccination
An area of artificial intelligence dedicated to algorithms that improve automatically through experience obtained via data exposure. Machine learning algorithms build a mathematical model from sample data, called a training set, to forecast or make decisions without being explicitly programmed by humans for that purpose. Machine learning algorithms are used in many different applications, such as image recognition, speech recognition, data analysis and classification, anti-spam etc.
Published in Chapter: Big Data Is Decision Science: The Case of COVID-19 Vaccination; From: Handbook of Research on Applied Data Science and Artificial Intelligence in Business and Industry
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Chapter 6
Overview of Machine Learning Approaches for Wireless Communication
The methods used to understand the patterns in the data and to obtain results from these patterns using various algorithms.
Published in Chapter: Overview of Machine Learning Approaches for Wireless Communication; From: Next-Generation Wireless Networks Meet Advanced Machine Learning Applications
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Chapter 5
Learning and Performance Innovation
A scientific discipline that is concerned with the design and development of algorithms that allow computers to evolve behaviors based on empirical data, such as from sensor data or databases.
Published in Chapter: Learning and Performance Innovation; From: Encyclopedia of E-Leadership, Counseling and Training
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Chapter 6
Leveraging Industry 4.0 for Entrepreneurial Success: The Role of Entrepreneurial Education
Teaching computers to improve by themselves as they get more data and experience.
Published in Chapter: Leveraging Industry 4.0 for Entrepreneurial Success: The Role of Entrepreneurial Education; From: Reshaping Entrepreneurial Education Within an Industry 4.0 Context
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Chapter 11
The Design and Evaluation of an Intelligent Pain Management System (IPMS) in Cancer Patient Care
Machine learning is a technology that relies on computer algorithms and statistical methods to automatically generate patterned models from sample data sets without human intervention. The sample data sets, also known as the training data , may be labeled (containing both inputs and known outputs) or unlabeled (only containing inputs without outputs). If a patterned model is generated from labeled data sets, the process is called supervised learning. If a patterned model is generated from unlabeled data sets, the process is called unsupervised learning.
Published in Chapter: The Design and Evaluation of an Intelligent Pain Management System (IPMS) in Cancer Patient Care; From: Impacts of Information Technology on Patient Care and Empowerment
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Chapter 46
Tech That, Bully!: Defeating Cyberbullying With Its Own Weapons
A method of data analysis based on setting automatic learning in technological tools to create analytical systems which are able to detect complex data.
Published in Chapter: Tech That, Bully!: Defeating Cyberbullying With Its Own Weapons; From: Encyclopedia of Criminal Activities and the Deep Web
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Chapter 3
How Does AI Make Libraries Smart?: A Case Study of Hangzhou Public Library
Machine learning is the core of artificial intelligence, specializing in studying how computers can emulate human learning behavior to acquire new knowledge or skill and reorganize the existing knowledge structure to improve performance continuously.
Published in Chapter: How Does AI Make Libraries Smart?: A Case Study of Hangzhou Public Library; From: Technological Advancements in Library Service Innovation
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Chapter 10
A Pharmaco-Cybernetics Approach to Patient Safety: Identifying Adverse Drug Reactions through Unsupervised Machine Learning
A field in artificial intelligence which designs computational algorithms and techniques capable of inducing knowledge from data. Generally, there are two main types: supervised and unsupervised learning.
Published in Chapter: A Pharmaco-Cybernetics Approach to Patient Safety: Identifying Adverse Drug Reactions through Unsupervised Machine Learning; From: Handbook of Research on Patient Safety and Quality Care through Health Informatics
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Chapter 89
Machine Learning Approach to Art Authentication
The application of statistical computer algorithms such that inference is used rather than explicit instruction to accomplish a task.
Published in Chapter: Machine Learning Approach to Art Authentication; From: Encyclopedia of Data Science and Machine Learning
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Chapter 17
Leveraging User-Specified Metadata to Personalize Image Search
A subfield of artificial intelligence that is concerned with algorithms and techniques for allowing computers to learn from data.
Published in Chapter: Leveraging User-Specified Metadata to Personalize Image Search; From: Handbook of Research on Web 2.0, 3.0, and X.0: Technologies, Business, and Social Applications
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