Computational Intelligence and Its Applications in Healthcare presents rapidly growing applications of computational intelligence for healthcare systems, including intelligent synthetic characters, man-machine interface, menu generators, user acceptance analysis, pictures archiving, and communication systems. Computational intelligence is the study of the design of intelligent agents, which are systems that act intelligently: they do what they think are appropriate for their circumstances and goals; they're flexible to changing environments and goals; they learn from experience; and they make appropriate choices given perceptual limitations and finite computation. Computational intelligence paradigms offer many advantages in maintaining and enhancing the field of healthcare.
- Provides coverage of fuzzy logic, neural networks, evolutionary computation, learning theory, probabilistic methods, telemedicine, and robotics applications
- Includes coverage of artificial intelligence and biological applications, soft computing, image and signal processing, and genetic algorithms
- Presents the latest developments in computational methods in healthcare
- Bridges the gap between obsolete literature and current literature
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Chapter 1 The impact of Internet of Things and data semantics on decision making for outpatient monitoring |
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Maria Laura Sanchez-Reynoso |
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3 Scenarios and states in the measurement process |
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4 Describing the measurement and its underlying semantics |
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5 Perspectives on IoT devices in data-stream processing |
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6 Monitoring outdoor activities of a patient: Application case |
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Chapter 2 Deep-learning approaches for health care: Patients in intensive care |
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4 Implementation and results |
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5 Discussion and conclusion |
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Chapter 3 Brain MRI image segmentation using nature-inspired Black Hole metaheuristic clustering approach |
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4 Experimental results and discussion |
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Chapter 4 Blockchain for public health: Technology, applications, and a case study |
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3 Benefits of blockchain application in public health |
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4 A use case from Estonia |
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5 Conclusion and challenges |
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Chapter 5 Compression and multiplexing of medical images using optical image processing |
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Chapter 6 Analysis of skin lesions using machine learning techniques |
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Chapter 7 Computational intelligence using ontology--A case study on the knowledge representation in a clinical decision support system |
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2 Clinical decision support systems |
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3 Computational semantics |
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4 Discussion and conclusion |
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Chapter 8 Neural network-based abnormality detection for electrocardiogram time signals |
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2 Electrocardiogram signal analysis |
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3 Deep recurrent neural network model |
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4 Network architecture of long short-term neural network |
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Chapter 9 Machine learning approaches for acetic acid test based uterine cervix image analysis |
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4 Results and discussions |
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Chapter 10 Convolutional neural network for biomedical applications |
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2 Introduction to ML techniques |
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4 Medical images and neural networks |
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5 Types of neural networks |
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6 Deep learning approach in medical area |
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7 Building blocks of neural network |
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8 Deep learning and medical imaging |
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Chapter 11 Alzheimer's disease classification using deep learning |
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1 Computational intelligence |
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2 Artificial intelligence vs computational intelligence |
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3 Artificial intelligence and the evolution toward deep learning |
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5 Technical limitations and scope of Alzheimer's disease diagnosis |
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6 Relevance of deep learning in Alzheimer's disease diagnosis |
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8 Convolutional neural network |
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9 Applications of deep learning |
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10 A review of Alzheimer's disease classification using deep learning |
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Chapter 12 Diabetic retinopathy identification using autoML |
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Chapter 13 Knowledge-based systems in medical applications |
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Saurabh Ranjan Srivastava |
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3 Factors influencing medical decisions |
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4 Structure of medical decisions |
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5 Knowledge-based systems in medicine: Architecture and working |
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6 Case studies of medical knowledge-based systems |
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7 Examples of renowned medical knowledge-based systems |
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8 Knowledge-based medical systems--Pros and cons |
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Chapter 14 Convolution neural network-based feature learning model for EEG-based driver alert/drowsy state detection |
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3 Experimentation and results |
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6 Conclusion and future work |
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Chapter 15 Analysis on the prediction of central line-associated bloodstream infections (CLABSI) using deep neural network classification |
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5 Conclusions and future work |
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| Index |
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Dr. Jitendra Kumar Verma is an Assistant Professor in the School of Computing Science and Engineering, Galgotias University, India. He holds a Ph.D. in Computer Science and Technology from Jawaharlal Nehru University, India. He has been a Visiting Research Scholar at Julius-Maximillian University, Wurzburg, Germany. His research interests include cloud computing, mobile cloud, machine learning, soft computing, fuzzy systems, pattern recognition, bio-inspired phenomena, and advanced optimization models and computation. Dr. Sudip Paul, Post-Doctoral Fellow and PhD, is currently an Associate Professor & Teacher In-Charge in the Department of Biomedical Engineering, School of Technology, North-Eastern Hill University (NEHU), Shillong, India. He has published over 40 journal papers, over 35 conference papers, and has contributed his knowledge as editorial board member and reviewer for multiple international journals. He has been granted one patent of eight filled and completed more than ten book projects. Dr. Sudip has presented his research accomplishments in countries around the world. He is a member of multiple societies and professional bodies, including APSN, ISN, IBRO, SNCI, SfN, IEEE, IAS. Dr. Sudip has received many awards, including the World Federation of Neurology (WFN) traveling fellowship, Young Investigator Award, IBRO Travel Awardee, and ISN Travel Awardee. Dr. Prashant Johri is a Professor in the School of Computing Science & Engineering, Galgotias University, Greater Noida, India. He received his B.Sc.(H) and M.C.A. from Aligarh Muslim University, Aligarh, and a Ph.D. in Computer Science from Jiwaji University, Gwalior, India. He has also worked as a Professor and Director (M.C.A.), Galgotias Institute of Management and Technology (G.I.M.T.), and Noida Institute of Engineering and Technology (N.I.E.T.) Greater Noida. He has served as Chair in many conferences and affiliated as a member of the program committee in many conferences in India and abroad. He has supervised 10 PhD students and many PG and U G Students for their theses and projects. He has published over 200 scientific articles, including journal papers, book chapters, and conference papers. He has published many edited books with reputable publications. He has organized several conferences/Workshops/Seminars at the national and international levels. He voluntarily served as a reviewer for various International Journals and conferences. His research interests include Artificial Intelligence, Machine Learning, Data Science, Blockchain, Healthcare, Agriculture, Entrepreneurship, Sustainable Development, Image Processing, Software Reliability, and Cloud Computing. He is actively publishing in these areas.
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