Computational Intelligence and Its Applications in Healthcare

1st Edition - July 29, 2020
Editors: Jitendra Kumar Verma, Sudip Paul, Prashant Johri
Language: English
Paperback ISBN:
9 7 8 - 0 - 1 2 - 8 2 0 6 0 4 - 1
eBook ISBN:
9 7 8 - 0 - 1 2 - 8 2 0 6 1 9 - 5

Computational Intelligence and Its Applications in Healthcare presents rapidly growing applications of computational intelligence for healthcare systems, including intell… Read more

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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.

Cover image
Title page
Table of Contents
Copyright
Contributors
1: The impact of Internet of Things and data semantics on decision making for outpatient monitoring
Abstract
Acknowledgments
1: Introduction
2: Related works
3: Scenarios and states in the measurement process
4: Describing the measurement and its underlying semantics
5: Perspectives on IoT devices in data-stream processing
6: Monitoring outdoor activities of a patient: Application case
7: Conclusions
2: Deep-learning approaches for health care: Patients in intensive care
Abstract
1: Introduction
2: Literature review
3: Material and methods
4: Implementation and results
5: Discussion and conclusion
3: Brain MRI image segmentation using nature-inspired Black Hole metaheuristic clustering approach
Abstract
1: Introduction
2: Related work
3: Proposed framework
4: Experimental results and discussion
5: Conclusion
4: Blockchain for public health: Technology, applications, and a case study
Abstract
1: Introduction
2: What is blockchain?
3: Benefits of blockchain application in public health
4: A use case from Estonia
5: Conclusion and challenges
5: Compression and multiplexing of medical images using optical image processing
Abstract
1: Introduction
2: Theory
3: Methodology
4: Result
5: Conclusion
6: Analysis of skin lesions using machine learning techniques
Abstract
1: Introduction
2: Related works
3: Materials and methods
4: Results and discussion
5: Conclusion
7: Computational intelligence using ontology—A case study on the knowledge representation in a clinical decision support system
Abstract
1: Introduction
2: Clinical decision support systems
3: Computational semantics
4: Discussion and conclusion
8: Neural network-based abnormality detection for electrocardiogram time signals
Abstract
1: Introduction
2: Electrocardiogram signal analysis
3: Deep recurrent neural network model
4: Network architecture of long short-term neural network
5: Training of data
6: Result analysis
7: Conclusion
9: Machine learning approaches for acetic acid test based uterine cervix image analysis
Abstract
1: Introduction
2: Related work
3: Methodology
4: Results and discussions
5: Conclusion
10: Convolutional neural network for biomedical applications
Abstract
1: Introduction
2: Introduction to ML techniques
3: Why DL algorithm?
4: Medical images and neural networks
5: Types of neural networks
6: Deep learning approach in medical area
7: Building blocks of neural network
8: Deep learning and medical imaging
9: Conclusion
11: Alzheimer’s disease classification using deep learning
Abstract
1: Computational intelligence
2: Artificial intelligence vs computational intelligence
3: Artificial intelligence and the evolution toward deep learning
4: Alzheimer’s disease
5: Technical limitations and scope of Alzheimer’s disease diagnosis
6: Relevance of deep learning in Alzheimer’s disease diagnosis
7: Deep learning
8: Convolutional neural network
9: Applications of deep learning
10: A review of Alzheimer’s disease classification using deep learning
11: Supporting software
12: Conclusions
12: Diabetic retinopathy identification using autoML
Abstract
1: Introduction
2: Related work
3: Materials and methods
4: Results and discussion
5: Conclusion
13: Knowledge-based systems in medical applications
Abstract
1: Introduction
2: Data in health care
3: Factors influencing medical decisions
4: Structure of medical decisions
5: Knowledge-based systems in medicine: Architecture and working
6: Case studies of medical knowledge-based systems
7: Examples of renowned medical knowledge-based systems
8: Knowledge-based medical systems—Pros and cons
9: Conclusion
14: REMOVED: Convolution neural network-based feature learning model for EEG-based driver alert/drowsy state detection
Abstract
15: Analysis on the prediction of central line-associated bloodstream infections (CLABSI) using deep neural network classification
Abstract
1: Introduction
2: Related works
3: Proposed method
4: Results
5: Conclusions and future work
Index

Jitendra Kumar Verma

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.

Affiliations and expertise

Assistant Professor, School of Computing Science and Engineering, Galgotias University, India

Sudip Paul

Dr. Sudip Paul, Post-Doctoral Fellow and PhD, is currently an Assistant 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.

Affiliations and expertise

Assistant Professor, Department of Biomedical Engineering, School of Technology, North-Eastern Hill University, Shillong, India

Prashant Johri

Dr. Prashant Johri. Professor in School of Computing Science & Engineering, Galgotias University, Greater Noida, India. He completed his B.Sc.(H) in 1992 and M.C.A. in 1992 from A.M.U, Aligarh and Ph.D. in Computer Science from Jiwaji University, Gwalior in 2011, India. He has also worked as a Professor and Director (M.C.A.), Galgotias Institute of Management and Technology and Noida Institute of Engineering and Technology, Gr. Noida. He has served as Chair in many conferences in India and Abroad. He has supervised 2 PhD students and M. Tech. students. He published more than 100 research papers in National and International Journals and Conferences. He has published edited books in Elsevier and Springer. He organized several Conferences / Workshops/Seminars at the national and international levels. His research interest includes Artificial Intelligence, Machine Learning, Data Science, Deep Reinforcement Learning, Information Security, Cloud Computing, Block Chain, Healthcare, Agriculture, Image Processing, Software Reliability.

Affiliations and expertise

Professor, School of Computing Science and Engineering, Galgotias University, Greater Noida, India