Handbook of Computational Intelligence in Biomedical Engineering and Healthcare helps readers analyze and conduct advanced research in specialty healthcare applications surrounding oncology, genomics and genetic data, ontologies construction, bio-memetic systems, biomedical electronics, protein structure prediction, and biomedical data analysis. The book provides the reader with a comprehensive guide to advanced computational intelligence, spanning deep learning, fuzzy logic, connectionist systems, evolutionary computation, cellular automata, self-organizing systems, soft computing, and hybrid intelligent systems in biomedical and healthcare applications. Sections focus on important biomedical engineering applications, including biosensors, enzyme immobilization techniques, immuno-assays, and nanomaterials for biosensors and other biomedical techniques.
Other sections cover gene-based solutions and applications through computational intelligence techniques and the impact of nonlinear/unstructured data on experimental analysis.
- Presents a comprehensive handbook that covers an Introduction to Computational Intelligence in Biomedical Engineering and Healthcare, Computational Intelligence Techniques, and Advanced and Emerging Techniques in Computational Intelligence
- Helps readers analyze and do advanced research in specialty healthcare applications
- Includes links to websites, videos, articles and other online content to expand and support primary learning objectives
Part 1: Computational Intelligence in Bioengineering and Health Care: An Introduction 1. Data Analysis in Bioengineering and Health Care: Advances and Challenges 2. Impact of Data Type and Analysis on Nature of Data 3. Computational Intelligence in Healthcare: Real Life Applications
Part 2: Computational Intelligence Techniques 4. Computational Intelligence: Past to Present 5. Computational Intelligence: Methods and Tools 6. Computational Intelligence: Trends and Applications 7. Computational Intelligence: Issues and Future Challenges
Part 3: Computational Intelligence in Bioengineering: A step towards the Next 8. Advance Computational Intelligence Techniques in bioengineering 9. A Case Study 10. New Technologies for biosensors 11. Performance Analysis: Statistical Approach
Dr. Janmenjoy Nayak is an Associate Professor in the Department of Computer Science and Engineering at Aditya Institute of Technology and Management, India. He has presented over 100 research articles in reputed international journals, conferences and books.
Bighnaraj Naik is an Assistant Professor in the Department of Computer Application, Veer SurendraSai University of Technology (Formerly UCE Burla), Odisha, India. He has published more than 90 research papers in various reputed peer reviewed International Journals, Conferences and Book Chapters. He has edited eleven books from various publishers such as Elsevier, Springer and IGI Global. At present, he has more than ten years of teaching experience in the field of Computer Science and IT. He is a member of IEEE. His area of interest includes Data Mining, Computational Intelligence, Soft Computing and its applications.
Danilo Pelusi is working as an Associate Professor at the Faculty of Communication Sciences, University of Teramo. Associate Editor of IEEE Transactions on Emerging Topics in Computational Intelligence, IEEE Access, International Journal of Machine Learning and Cybernetics (Springer) and Array (Elsevier), he served as guest editor for Elsevier, Springer and Inderscience journals, as program member of many conferences and as editorial board member of many journals. Reviewer of reputed journals such as IEEE Transactions on Fuzzy Systems and IEEE Transactions on Neural Networks and Machine Learning, his research interests include Fuzzy Logic, Neural Networks, Information Theory and Evolutionary Algorithms..
Das, Asit Kumar
Asit Kumar Das is a Professor of the Department of Computer Science and Technology, Indian Institute of Engineering Science and Technology Shibpur, Howrah and currently acting as the Head of the Center of Healthcare Science and Technology of his Institute. He has published one research monograph, three edited books, many book chapters and over 100 research articles in peer-reviewed journals and international conferences. His current research interests include data mining and pattern recognition, social network analysis, evolutionary computing, text, audio and video processing. Prof. Das has already guided five PhD scholars and seven more scholars are currently working under him