Advertisement
| cognitive science and machine learning: Modern Approaches in Machine Learning and Cognitive Science: A Walkthrough Vinit Kumar Gunjan, Jacek M. Zurada, 2021-04-26 This book provides a systematic and comprehensive overview of machine learning with cognitive science methods and technologies which have played an important role at the core of practical solutions for a wide scope of tasks between handheld apps, industrial process control, autonomous vehicles, environmental policies, life sciences, playing computer games, computational theory, and engineering development. The chapters in this book focus on readers interested in machine learning, cognitive and neuro-inspired computational systems – theories, mechanisms, and architecture, which underline human and animal behaviour, and their application to conscious and intelligent systems. In the current version, it focuses on the successful implementation and step-by-step explanation of practical applications of the domain. It also offers a wide range of inspiring and interesting cutting-edge contributions to applications of machine learning and cognitive science such as healthcare products, medical electronics, and gaming. Overall, this book provides valuable information on effective, cutting-edge techniques and approaches for students, researchers, practitioners, and academicians working in the field of AI, neural network, machine learning, and cognitive science. Furthermore, the purpose of this book is to address the interests of a broad spectrum of practitioners, students, and researchers, who are interested in applying machine learning and cognitive science methods in their respective domains. |
| cognitive science and machine learning: Modern Approaches in Machine Learning and Cognitive Science: A Walkthrough Vinit Kumar Gunjan, Jacek M. Zurada, Balasubramanian Raman, G. R. Gangadharan, 2020-02-04 This book discusses various machine learning & cognitive science approaches, presenting high-throughput research by experts in this area. Bringing together machine learning, cognitive science and other aspects of artificial intelligence to help provide a roadmap for future research on intelligent systems, the book is a valuable reference resource for students, researchers and industry practitioners wanting to keep abreast of recent developments in this dynamic, exciting and profitable research field. It is intended for postgraduate students, researchers, scholars and developers who are interested in machine learning and cognitive research, and is also suitable for senior undergraduate courses in related topics. Further, it is useful for practitioners dealing with advanced data processing, applied mathematicians, developers of software for agent-oriented systems and developers of embedded and real-time systems. |
| cognitive science and machine learning: Cognitive Science, Computational Intelligence, and Data Analytics Vikas Khare, Sanjeet Kumar Dwivedi, Monica Bhatia, 2024-06-06 Cognitive Science, Computational Intelligence, and Data Analytics: Methods and Applications with Python introduces readers to the foundational concepts of data analysis, cognitive science, and computational intelligence, including AI and Machine Learning. The book's focus is on fundamental ideas, procedures, and computational intelligence tools that can be applied to a wide range of data analysis approaches, with applications that include mathematical programming, evolutionary simulation, machine learning, and logic-based models. It offers readers the fundamental and practical aspects of cognitive science and data analysis, exploring data analytics in terms of description, evolution, and applicability in real-life problems.The authors cover the history and evolution of cognitive analytics, methodological concerns in philosophy, syntax and semantics, understanding of generative linguistics, theory of memory and processing theory, structured and unstructured data, qualitative and quantitative data, measurement of variables, nominal, ordinals, intervals, and ratio scale data. The content in this book is tailored to the reader's needs in terms of both type and fundamentals, including coverage of multivariate analysis, CRISP methodology and SEMMA methodology. Each chapter provides practical, hands-on learning with real-world applications, including case studies and Python programs related to the key concepts being presented. - Demystifies the theory of data analytics using a step-by-step approach - Covers the intersection of cognitive science, computational intelligence, and data analytics by providing examples and case studies with applied algorithms, mathematics, and Python programming code - Introduces foundational data analytics techniques such as CRISP-DM, SEMMA, and Object Detection Models in the context of computational intelligence methods and tools - Covers key concepts of multivariate and cognitive data analytics such as factor analytics, principal component analytics, linear regression analysis, logistic regression analysis, and value chain applications |
| cognitive science and machine learning: Machine Learning and Cognitive Science Applications in Cyber Security Khan, Muhammad Salman, 2019-05-15 In the past few years, with the evolution of advanced persistent threats and mutation techniques, sensitive and damaging information from a variety of sources have been exposed to possible corruption and hacking. Machine learning, artificial intelligence, predictive analytics, and similar disciplines of cognitive science applications have been found to have significant applications in the domain of cyber security. Machine Learning and Cognitive Science Applications in Cyber Security examines different applications of cognition that can be used to detect threats and analyze data to capture malware. Highlighting such topics as anomaly detection, intelligent platforms, and triangle scheme, this publication is designed for IT specialists, computer engineers, researchers, academicians, and industry professionals interested in the impact of machine learning in cyber security and the methodologies that can help improve the performance and reliability of machine learning applications. |
| cognitive science and machine learning: Cognitive Science and Artificial Intelligence Sasikumar Gurumoorthy, Bangole Narendra Kumar Rao, Xiao-Zhi Gao, 2017-12-22 This book presents interdisciplinary research on cognition, mind and behavior from an information processing perspective. It includes chapters on Artificial Intelligence, Decision Support Systems, Machine Learning, Data Mining and Support Vector Machines, chiefly with regard to the data obtained and analyzed in Medical Informatics, Bioinformatics and related disciplines. The book reflects the state-of-the-art in Artificial Intelligence and Cognitive Science, and covers theory, algorithms, numerical simulation, error and uncertainty analysis, as well novel applications of new processing techniques in Biomedical Informatics, Computer Science and its applied areas. As such, it offers a valuable resource for students and researchers from the fields of Computer Science and Engineering in Medicine and Biology. |
| cognitive science and machine learning: Artificial Intelligence D. Sleeman, N. O. Bernsen, 2019-12-06 Originally published in 1992, this title reviews seven major subareas in artificial intelligence at that time: knowledge acquisition; logic programming and representation; machine learning; natural language; vision; the design of an AI programming environment; and medicine, a major application area of AI. This volume was an attempt primarily to inform fellow AI workers of recent European work in AI. It was hoped that researchers in ‘sister’ disciplines, such as computer science and linguistics would gain a deeper understanding of the assumptions, techniques and tools of contemporary AI. |
| cognitive science and machine learning: Modern Approaches in Machine Learning & Cognitive Science: A Walkthrough Vinit Kumar Gunjan, Jacek M. Zurada, 2022-04-22 This book provides a systematic and comprehensive overview of AI and machine learning which have got the ability to identify patterns in large and complex data sets. A remarkable success has been experienced in the last decade by emulating the brain computer interface. It presents the cognitive science methods and technologies that have played an important role at the core of practical solutions for a wide scope of tasks between handheld apps, industrial process control, autonomous vehicles, environmental policies, life sciences, playing computer games, computational theory, and engineering development. The chapters in this book focuses on audiences interested in machine learning, cognitive and neuro-inspired computational systems, their theories, mechanisms, and architecture, which underline human and animal behaviour, and their application to conscious and intelligent systems. In the current version, it focuses on the successful implementation and step-by-step explanation of practical applications of the domain. It also offers a wide range of inspiring and interesting cutting-edge contributions on applications of machine learning and cognitive science such as healthcare products, medical electronics, and gaming. |
| cognitive science and machine learning: Modern Approaches in Machine Learning and Cognitive Science Vinit Kumar Gunjan, Jacek M. Zurada, Balasubramanian Raman, G. R. Gangadharan, 2020 This book discusses various machine learning & cognitive science approaches, presenting high-throughput research by experts in this area. Bringing together machine learning, cognitive science and other aspects of artificial intelligence to help provide a roadmap for future research on intelligent systems, the book is a valuable reference resource for students, researchers and industry practitioners wanting to keep abreast of recent developments in this dynamic, exciting and profitable research field. It is intended for postgraduate students, researchers, scholars and developers who are interested in machine learning and cognitive research, and is also suitable for senior undergraduate courses in related topics. Further, it is useful for practitioners dealing with advanced data processing, applied mathematicians, developers of software for agent-oriented systems and developers of embedded and real-time systems. |
| cognitive science and machine learning: Modern Approaches in Machine Learning and Cognitive Science: A Walkthrough Vinit Kumar Gunjan, Jacek M. Zurada, Ninni Singh, 2024-01-13 This book provides a systematic and comprehensive overview of cognitive intelligence and AI-enabled IoT ecosystem and machine learning, capable of recognizing the object pattern in complex and large data sets. A remarkable success has been experienced in the last decade by emulating the brain–computer interface. It presents the applied cognitive science methods and AI-enabled technologies that have played a vital role at the core of practical solutions for a wide scope of tasks between handheld apps and industrial process control, autonomous vehicles, IoT, intelligent learning environment, game theory, human computer interaction, environmental policies, life sciences, playing computer games, computational theory, and engineering development. The book contains contents highlighting artificial neural networks that are analogous to the networks of neurons that comprise the brain and have given computers the ability to distinguish an image of a cat from one of a coconut, to spot pedestrians with enough accuracy to direct a self-driving car, and to recognize and respond to the spoken word. The chapters in this book focus on audiences interested in artificial intelligence, machine learning, fuzzy, cognitive and neurofuzzy-inspired computational systems, their theories, mechanisms, and architecture, which underline human and animal behavior, and their application to conscious and intelligent systems. In the current version, it focuses on the successful implementation and step-by-step execution and explanation of practical applications of the domain. It also offers a wide range of inspiring and interesting cutting-edge contributions on applications of machine learning, artificial intelligence, and cognitive science such as healthcare products, AI-enabled IoT, gaming, medical, and engineering. Overall, this book provides valuable information on effective, cutting-edge techniques, and approaches for students, researchers, practitioners, and academics in the field of machine learning and cognitive science. Furthermore, the purpose of this book is to address the interests of a broad spectrum of practitioners, students, and researchers, who are interested in applying machine learning and cognitive science methods in their respective domains. |
| cognitive science and machine learning: Advances in Cybernetics, Cognition, and Machine Learning for Communication Technologies Vinit Kumar Gunjan, Sabrina Senatore, Amit Kumar, Xiao-Zhi Gao, Suresh Merugu, 2021-04-29 This book highlights recent advances in Cybernetics, Machine Learning and Cognitive Science applied to Communications Engineering and Technologies, and presents high-quality research conducted by experts in this area. It provides a valuable reference guide for students, researchers and industry practitioners who want to keep abreast of the latest developments in this dynamic, exciting and interesting research field of communication engineering, driven by next-generation IT-enabled techniques. The book will also benefit practitioners whose work involves the development of communication systems using advanced cybernetics, data processing, swarm intelligence and cyber-physical systems; applied mathematicians; and developers of embedded and real-time systems. Moreover, it shares insights into applying concepts from Machine Learning, Cognitive Science, Cybernetics and other areas of artificial intelligence to wireless and mobile systems, control systems and biomedical engineering. |
| cognitive science and machine learning: Cybernetics, Cognition and Machine Learning Applications Vinit Kumar Gunjan, P. N. Suganthan, Jan Haase, Amit Kumar, Balasubramanian Raman, 2020-04-20 This book provides a collection of selected papers presented at the International Conference on Cybernetics, Cognition and Machine Learning Applications (ICCCMLA 2019), which was held in Goa, India, on 16–17 August 2019. It covers the latest research trends and advances in the areas of data science, artificial intelligence, neural networks, cognitive science and machine learning applications, cyber-physical systems, and cybernetics. |
| cognitive science and machine learning: Cybernetics, Cognition and Machine Learning Applications Vinit Kumar Gunjan, P. N. Suganthan, Jan Haase, Amit Kumar, 2021-03-30 This book includes the original, peer reviewed research articles from the 2nd International Conference on Cybernetics, Cognition and Machine Learning Applications (ICCCMLA 2020), held in August, 2020 at Goa, India. It covers the latest research trends or developments in areas of data science, artificial intelligence, neural networks, cognitive science and machine learning applications, cyber physical systems and cybernetics. |
| cognitive science and machine learning: Modern Approaches in Machine Learning and Cognitive Science: A Walkthrough Vinit Kumar Gunjan, Jacek M. Zurada, 2021 This book provides a systematic and comprehensive overview of machine learning with cognitive science methods and technologies which have played an important role at the core of practical solutions for a wide scope of tasks between handheld apps, industrial process control, autonomous vehicles, environmental policies, life sciences, playing computer games, computational theory, and engineering development. The chapters in this book focus on readers interested in machine learning, cognitive and neuro-inspired computational systems - theories, mechanisms, and architecture, which underline human and animal behaviour, and their application to conscious and intelligent systems. In the current version, it focuses on the successful implementation and step-by-step explanation of practical applications of the domain. It also offers a wide range of inspiring and interesting cutting-edge contributions to applications of machine learning and cognitive science such as healthcare products, medical electronics, and gaming. Overall, this book provides valuable information on effective, cutting-edge techniques and approaches for students, researchers, practitioners, and academicians working in the field of AI, neural network, machine learning, and cognitive science. Furthermore, the purpose of this book is to address the interests of a broad spectrum of practitioners, students, and researchers, who are interested in applying machine learning and cognitive science methods in their respective domains. . |
| cognitive science and machine learning: Readings in Cognitive Science Allan Collins, Edward E. Smith, 2013-10-02 Readings in Cognitive Science: A Perspective from Psychology and Artificial Intelligence brings together important studies that fall in the intersection between artificial intelligence and cognitive psychology. This book is composed of six chapters, and begins with the complex anatomy and physiology of the human brain. The next chapters deal with the components of cognitive science, such as the semantic memory, similarity and analogy, and learning. These chapters also consider the application of mental models, which represent the domain-specific knowledge needed to understand a dynamic system or natural physical phenomena. The remaining chapters discuss the concept of reasoning, problem solving, planning, vision, and imagery. This book is of value to psychologists, psychiatrists, neurologists, and researchers who are interested in cognition. |
| cognitive science and machine learning: Modern Approaches in Machine Learning & Cognitive Science: A Walkthrough Vinit Kumar Gunjan, Jacek M. Zurada, 2022 This book provides a systematic and comprehensive overview of AI and machine learning which have got the ability to identify patterns in large and complex data sets. A remarkable success has been experienced in the last decade by emulating the brain computer interface. It presents the cognitive science methods and technologies that have played an important role at the core of practical solutions for a wide scope of tasks between handheld apps, industrial process control, autonomous vehicles, environmental policies, life sciences, playing computer games, computational theory, and engineering development. The chapters in this book focuses on audiences interested in machine learning, cognitive and neuro-inspired computational systems, their theories, mechanisms, and architecture, which underline human and animal behaviour, and their application to conscious and intelligent systems. In the current version, it focuses on the successful implementation and step-by-step explanation of practical applications of the domain. It also offers a wide range of inspiring and interesting cutting-edge contributions on applications of machine learning and cognitive science such as healthcare products, medical electronics, and gaming. |
| cognitive science and machine learning: Machine Learning and Cognitive Computing for Mobile Communications and Wireless Networks Krishna Kant Singh, Akansha Singh, Korhan Cengiz, Dac-Nhuong Le, 2020-07-08 Communication and network technology has witnessed recent rapid development and numerous information services and applications have been developed globally. These technologies have high impact on society and the way people are leading their lives. The advancement in technology has undoubtedly improved the quality of service and user experience yet a lot needs to be still done. Some areas that still need improvement include seamless wide-area coverage, high-capacity hot-spots, low-power massive-connections, low-latency and high-reliability and so on. Thus, it is highly desirable to develop smart technologies for communication to improve the overall services and management of wireless communication. Machine learning and cognitive computing have converged to give some groundbreaking solutions for smart machines. With these two technologies coming together, the machines can acquire the ability to reason similar to the human brain. The research area of machine learning and cognitive computing cover many fields like psychology, biology, signal processing, physics, information theory, mathematics, and statistics that can be used effectively for topology management. Therefore, the utilization of machine learning techniques like data analytics and cognitive power will lead to better performance of communication and wireless systems. |
| cognitive science and machine learning: Cloud Computing for Machine Learning and Cognitive Applications Kai Hwang, 2017-06-16 The first textbook to teach students how to build data analytic solutions on large data sets using cloud-based technologies. This is the first textbook to teach students how to build data analytic solutions on large data sets (specifically in Internet of Things applications) using cloud-based technologies for data storage, transmission and mashup, and AI techniques to analyze this data. This textbook is designed to train college students to master modern cloud computing systems in operating principles, architecture design, machine learning algorithms, programming models and software tools for big data mining, analytics, and cognitive applications. The book will be suitable for use in one-semester computer science or electrical engineering courses on cloud computing, machine learning, cloud programming, cognitive computing, or big data science. The book will also be very useful as a reference for professionals who want to work in cloud computing and data science. Cloud and Cognitive Computing begins with two introductory chapters on fundamentals of cloud computing, data science, and adaptive computing that lay the foundation for the rest of the book. Subsequent chapters cover topics including cloud architecture, mashup services, virtual machines, Docker containers, mobile clouds, IoT and AI, inter-cloud mashups, and cloud performance and benchmarks, with a focus on Google's Brain Project, DeepMind, and X-Lab programs, IBKai HwangM SyNapse, Bluemix programs, cognitive initiatives, and neurocomputers. The book then covers machine learning algorithms and cloud programming software tools and application development, applying the tools in machine learning, social media, deep learning, and cognitive applications. All cloud systems are illustrated with big data and cognitive application examples. |
| cognitive science and machine learning: Artificial Intelligence and Cognitive Science Lorcan Coyle, Jill Freyne, 2010-11-16 This book constitutes the refereed proceedings of the 20th Irish Conference on Artificial Intelligence and Cognitive Science, AICS 2009, held in Dublin, Ireland in August 2009. The 32 papers presented were carefully reviewed and selected for inclusion in the book. The topics covered are classification techniques, biologically-inspired computation, natural language processing, and applications of AI techniques for the social Web and financial markets. |
| cognitive science and machine learning: Artificial Intelligence and Creativity T. Dartnall, 2013-04-17 Creativity is one of the least understood aspects of intelligence and is often seen as `intuitive' and not susceptible to rational enquiry. Recently, however, there has been a resurgence of interest in the area, principally in artificial intelligence and cognitive science, but also in psychology, philosophy, computer science, logic, mathematics, sociology, and architecture and design. This volume brings this work together and provides an overview of this rapidly developing field. It addresses a range of issues. Can computers be creative? Can they help us to understand human creativity? How can artificial intelligence (AI) enhance human creativity? How, in particular, can it contribute to the `sciences of the artificial', such as design? Does the new wave of AI (connectionism, geneticism and artificial life) offer more promise in these areas than classical, symbol-handling AI? What would the implications be for AI and cognitive science if computers could not be creative? These issues are explored in five interrelated parts, each of which is introducted and explained by a leading figure in the field. - Prologue (Margaret Boden) - Part I: Foundational Issues (Terry Dartnall) - Part II: Creativity and Cognition (Graeme S. Halford and Robert Levinson) - Part III: Creativity and Connectionism (Chris Thornton) - Part IV: Creativity and Design (John Gero) - Part V: Human Creativity Enhancement (Ernest Edmonds) - Epilogue (Douglas Hofstadter) For researchers in AI, cognitive science, computer science, philosophy, psychology, mathematics, logic, sociology, and architecture and design; and anyone interested in the rapidly growing field of artificial intelligence and creativity. |
| cognitive science and machine learning: Cybernetics, Human Cognition, and Machine Learning in Communicative Applications Vinit Kumar Gunjan, Sabrina Senatore, Amit Kumar, 2024-12-18 |
| cognitive science and machine learning: Advances in Neural Computation, Machine Learning, and Cognitive Research Boris Kryzhanovsky, Witali Dunin-Barkowski, Vladimir Redko, 2018-05-12 This book describes new theories and applications of artificial neural networks, with a special focus on neural computation, cognitive science and machine learning. It discusses cutting-edge research at the intersection between different fields, from topics such as cognition and behavior, motivation and emotions, to neurocomputing, deep learning, classification and clustering. Further topics include signal processing methods, robotics and neurobionics, and computer vision alike. The book includes selected papers from the XIX International Conference on Neuroinformatics, held on October 2-6, 2017, in Moscow, Russia. |
| cognitive science and machine learning: Machine Learning in Cognitive IoT Neeraj Kumar, Aaisha Makkar, 2020-08-20 This book covers the different technologies of Internet, and machine learning capabilities involved in Cognitive Internet of Things (CIoT). Machine learning is explored by covering all the technical issues and various models used for data analytics during decision making at different steps. It initiates with IoT basics, its history, architecture and applications followed by capabilities of CIoT in real world and description of machine learning (ML) in data mining. Further, it explains various ML techniques and paradigms with different phases of data pre-processing and feature engineering. Each chapter includes sample questions to help understand concepts of ML used in different applications. Explains integration of Machine Learning in IoT for building an efficient decision support system Covers IoT, CIoT, machine learning paradigms and models Includes implementation of machine learning models in R Help the analysts and developers to work efficiently with emerging technologies such as data analytics, data processing, Big Data, Robotics Includes programming codes in Python/Matlab/R alongwith practical examples, questions and multiple choice questions |
| cognitive science and machine learning: Cognitive Science: Recent Advances and Recurring Problems Fred Adams, Osvaldo Pessoa Jr., Joao E. Kogler Jr., 2019-04-18 This book consists of an edited collection of original essays of the highest academic quality by seasoned experts in their fields of cognitive science. The essays are interdisciplinary, drawing from many of the fields known collectively as “the cognitive sciences.” Topics discussed represent a significant cross-section of the most current and interesting issues in cognitive science. Specific topics include matters regarding machine learning and cognitive architecture, the nature of cognitive content, the relationship of information to cognition, the role of language and communication in cognition, the nature of embodied cognition, selective topics in visual cognition, brain connectivity, computation and simulation, social and technological issues within the cognitive sciences, and significant issues in the history of neuroscience. This book will be of interest to both professional researchers and newer students and graduate students in the fields of cognitive science—including computer science, linguistics, philosophy, psychology and neuroscience. The essays are in English and are designed to be as free as possible of technical jargon and therefore accessible to young scholars and to scholars who are new to the cognitive neurosciences. In addition to several entries by single authors, the book contains several interesting roundtables where researchers contribute answers to a central question presented to those in the focus group on one of the core areas listed above. This exciting approach provides a variety of perspectives from across disciplines on topics of current concern in the cognitive sciences. |
| cognitive science and machine learning: Advances in Cybernetics, Cognition, and Machine Learning for Communication Technologies Vinit Kumar Gunjan, Sabrina Senatore, Amit Kumar, Xiao-Zhi Gao, Suresh Merugu, 2020-04-28 This book highlights recent advances in Cybernetics, Machine Learning and Cognitive Science applied to Communications Engineering and Technologies, and presents high-quality research conducted by experts in this area. It provides a valuable reference guide for students, researchers and industry practitioners who want to keep abreast of the latest developments in this dynamic, exciting and interesting research field of communication engineering, driven by next-generation IT-enabled techniques. The book will also benefit practitioners whose work involves the development of communication systems using advanced cybernetics, data processing, swarm intelligence and cyber-physical systems; applied mathematicians; and developers of embedded and real-time systems. Moreover, it shares insights into applying concepts from Machine Learning, Cognitive Science, Cybernetics and other areas of artificial intelligence to wireless and mobile systems, control systems and biomedical engineering. |
| cognitive science and machine learning: Human-Like Machine Intelligence Stephen Muggleton, Nicholas Chater, 2021 This book, authored by an array of internationally recognised researchers, is of direct relevance to all those involved in Academia and Industry wanting to obtain insights into the topics at the forefront of the revolution in Artificial Intelligence and Cognitive Science. |
| cognitive science and machine learning: Intelligence Science Zhongzhi Shi, 2021-04-16 Intelligence Science: Leading the Age of Intelligence covers the emerging scientific research on the theory and technology of intelligence, bringing together disciplines such as neuroscience, cognitive science, and artificial intelligence to study the nature of intelligence, the functional simulation of intelligent behavior, and the development of new intelligent technologies. The book presents this complex, interdisciplinary area of study in an accessible volume, introducing foundational concepts and methods, and presenting the latest trends and developments. Chapters cover the Foundations of neurophysiology, Neural computing, Mind models, Perceptual intelligence, Language cognition, Learning, Memory, Thought, Intellectual development and cognitive structure, Emotion and affect, and more. This volume synthesizes a very rich and complex area of research, with an aim of stimulating new lines of enquiry. - Presents a complex, interdisciplinary area in an accessible way, including the latest trends and developments - Brings together disciplines such as neuroscience, cognitive science and artificial intelligence - Gives the latest methods and theories in the development of new intelligent technologies - Reflects upon the most important achievements in the study of natural and artificial intelligence - Contextualizes intelligence research within the history and progress of twenty-first century science |
| cognitive science and machine learning: Deep Learning and Linguistic Representation Shalom Lappin, 2021-04-27 The application of deep learning methods to problems in natural language processing has generated significant progress across a wide range of natural language processing tasks. For some of these applications, deep learning models now approach or surpass human performance. While the success of this approach has transformed the engineering methods of machine learning in artificial intelligence, the significance of these achievements for the modelling of human learning and representation remains unclear. Deep Learning and Linguistic Representation looks at the application of a variety of deep learning systems to several cognitively interesting NLP tasks. It also considers the extent to which this work illuminates our understanding of the way in which humans acquire and represent linguistic knowledge. Key Features: combines an introduction to deep learning in AI and NLP with current research on Deep Neural Networks in computational linguistics. is self-contained and suitable for teaching in computer science, AI, and cognitive science courses; it does not assume extensive technical training in these areas. provides a compact guide to work on state of the art systems that are producing a revolution across a range of difficult natural language tasks. |
| cognitive science and machine learning: Cognitive Electronic Warfare: An Artificial Intelligence Approach Karen Haigh, Julia Andrusenko, 2021-07-31 This comprehensive book gives an overview of how cognitive systems and artificial intelligence (AI) can be used in electronic warfare (EW). Readers will learn how EW systems respond more quickly and effectively to battlefield conditions where sophisticated radars and spectrum congestion put a high priority on EW systems that can characterize and classify novel waveforms, discern intent, and devise and test countermeasures. Specific techniques are covered for optimizing a cognitive EW system as well as evaluating its ability to learn new information in real time. The book presents AI for electronic support (ES), including characterization, classification, patterns of life, and intent recognition. Optimization techniques, including temporal tradeoffs and distributed optimization challenges are also discussed. The issues concerning real-time in-mission machine learning and suggests some approaches to address this important challenge are presented and described. The book covers electronic battle management, data management, and knowledge sharing. Evaluation approaches, including how to show that a machine learning system can learn how to handle novel environments, are also discussed. Written by experts with first-hand experience in AI-based EW, this is the first book on in-mission real-time learning and optimization. |
| cognitive science and machine learning: AI and Cognitive Science ’90 Michael F. McTear, Norman Creaney, 2013-03-14 This book contains the edited versions of papers presented at the 3rd Irish Conference on Artificial Intelligence and Cognitive Science, which was held at the University of Ulster at Jordanstown, Northern Ireland on 20-21 September 1990. The main aims of this annual conference are to promote AI research in Ireland, to provide a forum for the exchange of ideas amongst the different disciplines concerned with the study of cognition, and to provide an opportunity for industry to see what research is being carried out in Ireland and how they might benefit from the results of this research. Although most of the partiCipants at the conference came from universities and companies within Ireland, a positive feature of the conference was the extent of interest shown outside of Ireland, resulting in partiCipants from USA, Canada, Austria, and England. The keynote speakers were Professor David Chin, University of Hawaii, and Professor Derek Partridge, University of Exeter, and the topics included machine learning, AI tools and methods, expert systems, speech, vision, natural language, reasoning with uncertain information, and explanation. The sponsors of the conference were Digital Equipment Co (Galway) and the Industrial Development Board for Northern Ireland. |
| cognitive science and machine learning: Data Intelligence and Cognitive Informatics I. Jeena Jacob, Selvanayaki Kolandapalayam Shanmugam, Selwyn Piramuthu, Przemyslaw Falkowski-Gilski, 2021-01-08 This book discusses new cognitive informatics tools, algorithms and methods that mimic the mechanisms of the human brain which lead to an impending revolution in understating a large amount of data generated by various smart applications. The book is a collection of peer-reviewed best selected research papers presented at the International Conference on Data Intelligence and Cognitive Informatics (ICDICI 2020), organized by SCAD College of Engineering and Technology, Tirunelveli, India, during 8–9 July 2020. The book includes novel work in data intelligence domain which combines with the increasing efforts of artificial intelligence, machine learning, deep learning and cognitive science to study and develop a deeper understanding of the information processing systems. |
| cognitive science and machine learning: Cognitive Informatics, Computer Modelling, and Cognitive Science G. R. Sinha, Jasjit Suri, 2020-04-08 Cognitive Informatics, Computer Modelling, and Cognitive Science: Theory, Case Studies, and Applications presents the theoretical background and history of cognitive science to help readers understand its foundations, philosophical and psychological aspects, and applications in a wide range of engineering and computer science case studies. Cognitive science, a cognitive model of the brain, knowledge representation, and information processing in the human brain are discussed, as is the theory of consciousness, neuroscience, intelligence, decision-making, mind and behavior analysis, and the various ways cognitive computing is used for information manipulation, processing and decision-making. Mathematical and computational models, structures and processes of the human brain are also covered, along with advances in machine learning, artificial intelligence, cognitive knowledge base, deep learning, cognitive image processing and suitable data analytics. |
| cognitive science and machine learning: Advances in Neural Computation, Machine Learning, and Cognitive Research Boris Kryzhanovsky, Witali Dunin-Barkowski, Vladimir Redko, 2017-08-28 This book describes new theories and applications of artificial neural networks, with a special focus on neural computation, cognitive science and machine learning. It discusses cutting-edge research at the intersection between different fields, from topics such as cognition and behavior, motivation and emotions, to neurocomputing, deep learning, classification and clustering. Further topics include signal processing methods, robotics and neurobionics, and computer vision alike. The book includes selected papers from the XIX International Conference on Neuroinformatics, held on October 2-6, 2017, in Moscow, Russia. |
| cognitive science and machine learning: Mind as Machine Margaret A. Boden, 2006 Cognitive science is among the most fascinating intellectual achievements of the modern era. The quest to understand the mind is an ancient one. But modern science has offered new insights and techniques that have revolutionized this enquiry. Oxford University Press now presents a masterlyhistory of the field, told by one of its most eminent practitioners.Psychology is the thematic heart of cognitive science, which aims to understand human (and animal) minds. But its core theoretical ideas are drawn from cybernetics and artificial intelligence, and many cognitive scientists try to build functioning models of how the mind works. In that sense,Margaret Boden suggests, its key insight is that mind is a (very special) machine. Because the mind has many different aspects, the field is highly interdisciplinary. It integrates psychology not only with cybernetics/AI, but also with neuroscience and clinical neurology; with the philosophy ofmind, language, and logic; with linguistic work on grammar, semantics, and communication; with anthropological studies of cultures; and with biological (and A-Life) research on animal behaviour, evolution, and life itself. Each of these disciplines, in its own way, asks what the mind is, what itdoes, how it works, how it develops---and how it is even possible.Boden traces the key questions back to Descartes's revolutionary writings, and to the ideas of his followers--and his radical critics--through the eighteenth and nineteenth centuries. Her story shows how controversies in the development of experimental physiology, neurophysiology, psychology,evolutionary biology, embryology, and logic are still relevant today. Then she guides the reader through the complex interlinked paths along which the study of mind developed in the twentieth century. Cognitive science covers all mental phenomena: not just 'cognition' (knowledge), but also emotion,personality, psychopathology, social communication, religion, motor action, and consciousness. In each area, Boden introduces the key ideas and researchers and discusses those philosophical critics who see cognitive science as fundamentally misguided. And she sketches the waves of resistance andacceptance on the part of the media and general public, showing how these have affected the development of the field.No one else could tell this story as Boden can: she has been a member of the cognitive science community since the late-1950s, and has known many of its key figures personally. Her narrative is written in a lively, swift-moving style, enriched by the personal touch of someone who knows the story atfirst hand. Her history looks forward as well as back: besides asking how state-of-the-art research compares with the hopes of the early pioneers, she identifies the most promising current work. Mind as Machine will be a rich resource for anyone working on the mind, in any academic discipline, whowants to know how our understanding of mental capacities has advanced over the years. |
| cognitive science and machine learning: Neural-Symbolic Cognitive Reasoning Artur S. D'Avila Garcez, Luís C. Lamb, Dov M. Gabbay, 2009 This book explores why, regarding practical reasoning, humans are sometimes still faster than artificial intelligence systems. It is the first to offer a self-contained presentation of neural network models for many computer science logics. |
| cognitive science and machine learning: Cognitive Computing for Human-Robot Interaction Mamta Mittal, Rajiv Ratn Shah, Sudipta Roy, 2021-08-13 Cognitive Computing for Human-Robot Interaction: Principles and Practices explores the efforts that should ultimately enable society to take advantage of the often-heralded potential of robots to provide economical and sustainable computing applications. This book discusses each of these applications, presents working implementations, and combines coherent and original deliberative architecture for human–robot interactions (HRI). Supported by experimental results, it shows how explicit knowledge management promises to be instrumental in building richer and more natural HRI, by pushing for pervasive, human-level semantics within the robot's deliberative system for sustainable computing applications. This book will be of special interest to academics, postgraduate students, and researchers working in the area of artificial intelligence and machine learning. Key features: - Introduces several new contributions to the representation and management of humans in autonomous robotic systems; - Explores the potential of cognitive computing, robots, and HRI to generate a deeper understanding and to provide a better contribution from robots to society; - Engages with the potential repercussions of cognitive computing and HRI in the real world. - Introduces several new contributions to the representation and management of humans in an autonomous robotic system - Explores cognitive computing, robots and HRI, presenting a more in-depth understanding to make robots better for society - Gives a challenging approach to those several repercussions of cognitive computing and HRI in the actual global scenario |
| cognitive science and machine learning: Applied Biomedical Engineering Using Artificial Intelligence and Cognitive Models Jorge Garza Ulloa, 2021-11-30 Applied Biomedical Engineering Using Artificial Intelligence and Cognitive Models focuses on the relationship between three different multidisciplinary branches of engineering: Biomedical Engineering, Cognitive Science and Computer Science through Artificial Intelligence models. These models will be used to study how the nervous system and musculoskeletal system obey movement orders from the brain, as well as the mental processes of the information during cognition when injuries and neurologic diseases are present in the human body. The interaction between these three areas are studied in this book with the objective of obtaining AI models on injuries and neurologic diseases of the human body, studying diseases of the brain, spine and the nerves that connect them with the musculoskeletal system. There are more than 600 diseases of the nervous system, including brain tumors, epilepsy, Parkinson's disease, stroke, and many others. These diseases affect the human cognitive system that sends orders from the central nervous system (CNS) through the peripheral nervous systems (PNS) to do tasks using the musculoskeletal system. These actions can be detected by many Bioinstruments (Biomedical Instruments) and cognitive device data, allowing us to apply AI using Machine Learning-Deep Learning-Cognitive Computing models through algorithms to analyze, detect, classify, and forecast the process of various illnesses, diseases, and injuries of the human body. Applied Biomedical Engineering Using Artificial Intelligence and Cognitive Models provides readers with the study of injuries, illness, and neurological diseases of the human body through Artificial Intelligence using Machine Learning (ML), Deep Learning (DL) and Cognitive Computing (CC) models based on algorithms developed with MATLAB® and IBM Watson®. - Provides an introduction to Cognitive science, cognitive computing and human cognitive relation to help in the solution of AI Biomedical engineering problems - Explain different Artificial Intelligence (AI) including evolutionary algorithms to emulate natural evolution, reinforced learning, Artificial Neural Network (ANN) type and cognitive learning and to obtain many AI models for Biomedical Engineering problems - Includes coverage of the evolution Artificial Intelligence through Machine Learning (ML), Deep Learning (DL), Cognitive Computing (CC) using MATLAB® as a programming language with many add-on MATLAB® toolboxes, and AI based commercial products cloud services as: IBM (Cognitive Computing, IBM Watson®, IBM Watson Studio®, IBM Watson Studio Visual Recognition®), and others - Provides the necessary tools to accelerate obtaining results for the analysis of injuries, illness, and neurologic diseases that can be detected through the static, kinetics and kinematics, and natural body language data and medical imaging techniques applying AI using ML-DL-CC algorithms with the objective of obtaining appropriate conclusions to create solutions that improve the quality of life of patients |
| cognitive science and machine learning: Intelligent Systems Crina Grosan, Ajith Abraham, 2011-07-29 Computational intelligence is a well-established paradigm, where new theories with a sound biological understanding have been evolving. The current experimental systems have many of the characteristics of biological computers (brains in other words) and are beginning to be built to perform a variety of tasks that are difficult or impossible to do with conventional computers. As evident, the ultimate achievement in this field would be to mimic or exceed human cognitive capabilities including reasoning, recognition, creativity, emotions, understanding, learning and so on. This book comprising of 17 chapters offers a step-by-step introduction (in a chronological order) to the various modern computational intelligence tools used in practical problem solving. Staring with different search techniques including informed and uninformed search, heuristic search, minmax, alpha-beta pruning methods, evolutionary algorithms and swarm intelligent techniques; the authors illustrate the design of knowledge-based systems and advanced expert systems, which incorporate uncertainty and fuzziness. Machine learning algorithms including decision trees and artificial neural networks are presented and finally the fundamentals of hybrid intelligent systems are also depicted. Academics, scientists as well as engineers engaged in research, development and application of computational intelligence techniques, machine learning and data mining would find the comprehensive coverage of this book invaluable. |
| cognitive science and machine learning: AI Margaret A. Boden, 2016-05-19 The applications of Artificial Intelligence lie all around us; in our homes, schools and offices, in our cinemas, in art galleries and - not least - on the Internet. The results of Artificial Intelligence have been invaluable to biologists, psychologists, and linguists in helping to understand the processes of memory, learning, and language from a fresh angle. As a concept, Artificial Intelligence has fuelled and sharpened the philosophical debates concerning the nature of the mind, intelligence, and the uniqueness of human beings. Margaret A. Boden reviews the philosophical and technological challenges raised by Artificial Intelligence, considering whether programs could ever be really intelligent, creative or even conscious, and shows how the pursuit of Artificial Intelligence has helped us to appreciate how human and animal minds are possible. |
| cognitive science and machine learning: Human and Machine Problem Solving K.J. Gilhooly, 1989 Problem solving is a central topic for both cognitive psychology and artificial intelligence (AI). Psychology seeks to analyze naturally occur ring problem solving into hypothetical processes, while AI seeks to synthesize problem-solving performance from well-defined processes. Psychology may suggest possible processes to AI and, in turn, AI may suggest plausible hypotheses to psychology. It should be useful for both sides to have some idea of the other's contribution-hence this book, which brings together overviews of psychological and AI re search in major areas of problem solving. At a more general level, this book is intended to be a contribution toward comparative cognitive science. Cognitive science is the study of intelligent systems, whether natural or artificial, and treats both organ isms and computers as types of information-processing systems. Clearly, humans and typical current computers have rather different functional or cognitive architectures. Thus, insights into the role of cognitive ar chitecture in performance may be gained by comparing typical human problem solving with efficient machine problem solving over a range of tasks. Readers may notice that there is little mention of connectionist ap proaches in this volume. This is because, at the time of writing, such approaches have had little or no impact on research at the problem solving level. Should a similar volume be produced in ten years or so, of course, a very different story may need to be told. |
| cognitive science and machine learning: Handbook of Research on Emerging Trends and Applications of Machine Learning Solanki, Arun, Kumar, Sandeep, Nayyar, Anand, 2019-12-13 As today’s world continues to advance, Artificial Intelligence (AI) is a field that has become a staple of technological development and led to the advancement of numerous professional industries. An application within AI that has gained attention is machine learning. Machine learning uses statistical techniques and algorithms to give computer systems the ability to understand and its popularity has circulated through many trades. Understanding this technology and its countless implementations is pivotal for scientists and researchers across the world. The Handbook of Research on Emerging Trends and Applications of Machine Learning provides a high-level understanding of various machine learning algorithms along with modern tools and techniques using Artificial Intelligence. In addition, this book explores the critical role that machine learning plays in a variety of professional fields including healthcare, business, and computer science. While highlighting topics including image processing, predictive analytics, and smart grid management, this book is ideally designed for developers, data scientists, business analysts, information architects, finance agents, healthcare professionals, researchers, retail traders, professors, and graduate students seeking current research on the benefits, implementations, and trends of machine learning. |
COGNITIVE Definition & Meaning - Merriam-Webster
The meaning of COGNITIVE is of, relating to, being, or involving conscious intellectual activity (such as thinking, reasoning, or remembering). How to use cognitive in a sentence.
COGNITIVE Definition & Meaning | Dictionary.com
Cognitive definition: of or relating to cognition; concerned with the act or process of knowing, perceiving, etc. .. See examples of COGNITIVE used in a sentence.
COGNITIVE | English meaning - Cambridge Dictionary
COGNITIVE definition: 1. connected with thinking or conscious mental processes: 2. connected with thinking or conscious…. Learn more.
Cognitive Definition and Meaning in Psychology - Verywell Mind
Apr 21, 2024 · Cognitive psychology seeks to understand all of the mental processes involved in human thought and behavior. It focuses on cognitive processes such as decision-making, …
Cognition - Wikipedia
It encompasses all aspects of intellectual functions and processes such as: perception, attention, thought, imagination, intelligence, the formation of knowledge, memory and working memory, …
Cognition | Definition, Psychology, Examples, & Facts | Britannica
May 15, 2025 · cognition, the states and processes involved in knowing, which in their completeness include perception and judgment. Cognition includes all conscious and …
Cognitive Approach In Psychology
May 12, 2025 · The cognitive approach in psychology studies mental processes—such as how we perceive, think, remember, learn, make decisions, and solve problems. Cognitive …
What does Cognitive mean? - Definitions.net
Cognitive refers to the mental processes and activities related to acquiring, processing, storing, and using information. It involves various abilities such as perception, attention, memory, …
Cognitive - Definition, Meaning & Synonyms | Vocabulary.com
The adjective, cognitive, comes from the Latin cognoscere "to get to know" and refers to the ability of the brain to think and reason as opposed to feel. A child's cognitive development is the …
Cognitive - definition of cognitive by The Free Dictionary
1. of or pertaining to cognition. 2. of or pertaining to the mental processes of perception, memory, judgment, and reasoning, as contrasted with emotional and volitional processes. cog`ni•tiv′i•ty, …
COGNITIVE Definition & Meaning - Merriam-Webster
The meaning of COGNITIVE is of, relating to, being, or involving conscious intellectual activity (such as thinking, reasoning, or remembering). How to …
COGNITIVE Definition & Meaning | Dictionary.com
Cognitive definition: of or relating to cognition; concerned with the act or process of knowing, perceiving, etc. .. See examples of COGNITIVE used in a …
COGNITIVE | English meaning - Cambridge Dictionary
COGNITIVE definition: 1. connected with thinking or conscious mental processes: 2. connected with thinking or …
Cognitive Definition and Meaning in Psychology - Verywell Mind
Apr 21, 2024 · Cognitive psychology seeks to understand all of the mental processes involved in human thought and behavior. It focuses on cognitive processes such as …
Cognition - Wikipedia
It encompasses all aspects of intellectual functions and processes such as: perception, attention, thought, imagination, intelligence, the formation of …
