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COGNITIVE INTELLIGENCE AND BIG DATA IN HEALTHCARE
Applications of cognitive intelligence, advanced communication, and computational methods can drive healthcare research and enhance existing traditional methods in disease detection and management and prevention.
As health is the foremost factor affecting the quality of human life, it is necessary to understand how the human body is functioning by processing health data obtained from various sources more quickly. Since an enormous amount of data is generated during data processing, a cognitive computing system could be applied to respond to queries, thereby assisting in customizing intelligent recommendations. This decision-making process could be improved by the deployment of cognitive computing techniques in healthcare, allowing for cutting-edge techniques to be integrated into healthcare to provide intelligent services in various healthcare applications.
This book tackles all these issues and provides insight into these diversified topics in the healthcare sector and shows the range of recent innovative research, in addition to shedding light on future directions in this area.
Audience
The book will be very useful to a wide range of specialists including researchers, engineers, and postgraduate students in artificial intelligence, bioinformatics, information technology, as well as those in biomedicine.
Sommario
Preface xv
1 Era of Computational Cognitive Techniques in Healthcare Systems 1
Deependra Rastogi, Varun Tiwari, Shobhit Kumar and Prabhat Chandra Gupta
1.1 Introduction 2
1.2 Cognitive Science 3
1.3 Gap Between Classical Theory of Cognition 4
1.4 Cognitive Computing's Evolution 6
1.5 The Coming Era of Cognitive Computing 7
1.6 Cognitive Computing Architecture 9
1.6.1 The Internet-of-Things and Cognitive Computing 10
1.6.2 Big Data and Cognitive Computing 11
1.6.3 Cognitive Computing and Cloud Computing 13
1.7 Enabling Technologies in Cognitive Computing 13
1.7.1 Reinforcement Learning and Cognitive Computing 13
1.7.2 Cognitive Computing with Deep Learning 15
1.7.2.1 Relational Technique and Perceptual Technique 15
1.7.2.2 Cognitive Computing and Image Understanding 16
1.8 Intelligent Systems in Healthcare 17
1.8.1 Intelligent Cognitive System in Healthcare (Why and How) 20
1.9 The Cognitive Challenge 32
1.9.1 Case Study: Patient Evacuation 32
1.9.2 Case Study: Anesthesiology 32
1.10 Conclusion 34
References 35
2 Proposal of a Metaheuristic Algorithm of Cognitive Computing for Classification of Erythrocytes and Leukocytes in Healthcare Informatics 41
Ana Carolina Borges Monteiro, Reinaldo Padilha França, Rangel Arthur and Yuzo Iano
2.1 Introduction 42
2.2 Literature Concept 44
2.2.1 Cognitive Computing Concept 44
2.2.2 Neural Networks Concepts 47
2.2.3 Convolutional Neural Network 49
2.2.4 Deep Learning 52
2.3 Materials and Methods (Metaheuristic Algorithm Proposal) 55
2.4 Case Study and Discussion 57
2.5 Conclusions with Future Research Scopes 60
References 61
3 Convergence of Big Data and Cognitive Computing in Healthcare 67
R. Sathiyaraj, U. Rahamathunnisa, M.V. Jagannatha Reddy and T. Parameswaran
3.1 Introduction 68
3.2 Literature Review 70
3.2.1 Role of Cognitive Computing in Healthcare Applications 70
3.2.2 Research Problem Study by IBM 73
3.2.3 Purpose of Big Data in Healthcare 74
3.2.4 Convergence of Big Data with Cognitive Computing 74
3.2.4.1 Smart Healthcare 74
3.2.4.2 Big Data and Cognitive Computing-Based Smart Healthcare 75
3.3 Using Cognitive Computing and Big Data, a Smart Healthcare Framework for EEG Pathology Detection and Classification 76
3.3.1 EEG Pathology Diagnoses 76
3.3.2 Cognitive-Big Data-Based Smart Healthcare 77
3.3.3 System Architecture 79
3.3.4 Detection and Classification of Pathology 80
3.3.4.1 EEG Preprocessing and Illustration 80
3.3.4.2 CNN Model 80
3.3.5 Case Study 81
3.4 An Approach to Predict Heart Disease Using Integrated Big Data and Cognitive Computing in Cloud 83
3.4.1 Cloud Computing with Big Data in Healthcare 86
3.4.2 Heart Diseases 87
3.4.3 Healthcare Big Data Techniques 88
3.4.3.1 Rule Set Classifiers 88
3.4.3.2 Neuro Fuzzy Classifiers 89
3.4.3.3 Experimental Results 91
3.5 Conclusion 92
References 93
4 IoT for Health, Safety, Well-Being, Inclusion, and Active Aging 97
R. Indrakumari, Nilanjana Pradhan, Shrddha Sagar and Kiran Singh
4.1 Introduction 98
4.2 The Role of Technology in an Aging Society 99
4.3 Literature Survey 100
4.4 Health Monitoring 101
4.5 Nutrition Monitoring 105
4.6 Stress-Log: An IoT-Based Smart Monitoring
Info autore
D. Sumathi, PhD, is an associate professor at VIT-AP University, Andhra Pradesh. She has an overall experience of 21 years out of which six years in the industry, and 15 years in the teaching field. Her research interests include cloud computing, network security, data mining, natural language processing, and the theoretical foundations of computer science.
T. Poongodi, PhD, is an associate professor in the Department of Computer Science and Engineering at Galgotias University, Delhi - NCR, India. She has more than 15 years of experience working in teaching and research.
B. Balamurugan, PhD, is a professor in the School of Computing Science and Engineering at Galgotias University, Delhi - NCR, India. His focus is on engineering education, blockchain, and data sciences. He has published more than 30 books on various technologies and more than 150 research articles in SCI journals, conferences, and book chapters.
Lakshmana Kumar Ramasamy, PhD, is leading the Machine Learning for Cyber Security team at Hindusthan College of Engineering and Technology, Coimbatore. Tamil Nadu, India. He is also allied with a company conducting specific training for Infosys Campus Connect, Oracle WDP, and Palo Alto Networks. He holds the Gold level partnership award from Infosys, India for bridging the gap between industry and academia in 2017.
Riassunto
COGNITIVE INTELLIGENCE AND BIG DATA IN HEALTHCARE
Applications of cognitive intelligence, advanced communication, and computational methods can drive healthcare research and enhance existing traditional methods in disease detection and management and prevention.
As health is the foremost factor affecting the quality of human life, it is necessary to understand how the human body is functioning by processing health data obtained from various sources more quickly. Since an enormous amount of data is generated during data processing, a cognitive computing system could be applied to respond to queries, thereby assisting in customizing intelligent recommendations. This decision-making process could be improved by the deployment of cognitive computing techniques in healthcare, allowing for cutting-edge techniques to be integrated into healthcare to provide intelligent services in various healthcare applications.
This book tackles all these issues and provides insight into these diversified topics in the healthcare sector and shows the range of recent innovative research, in addition to shedding light on future directions in this area.
Audience
The book will be very useful to a wide range of specialists including researchers, engineers, and postgraduate students in artificial intelligence, bioinformatics, information technology, as well as those in biomedicine.
