Handbook of Computational Intelligence in Biomedical Engineering and Healthcare

1st Edition - April 8, 2021
Editors: Janmenjoy Nayak, Bighnaraj Naik, Danilo Pelusi, Asit Kumar Das
Language: English
Paperback ISBN:
9 7 8 - 0 - 1 2 - 8 2 2 2 6 0 - 7
eBook ISBN:
9 7 8 - 0 - 1 2 - 8 2 2 2 6 1 - 4

Handbook of Computational Intelligence in Biomedical Engineering and Healthcare helps readers analyze and conduct advanced research in specialty healthcare applications surroundi… Read more

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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.

Cover image
Title page
Table of Contents
Copyright
Contributors
Biographies
Preface
Chapter 1. Application of dynamical systems based deep learning algorithms to model emergent characteristics for healthcare diagnostics
1. Introduction
2. Deep learning applications for brainwaves monitoring
3. Healthcare Modeling and simulation using feedback hybrid artificial neural networks
4. Derivative estimation using feedback networks
5. Usage of deep learning knowledge mining in Hybrid Inference Networks
6. Conclusions
Chapter 2. Computational intelligence in healthcare and biosignal processing
1. Introduction
2. Investigation on various deep clustering algorithms
3. Investigation on clustering algorithms for the unsupervised learning methodology
4. Conclusion
Chapter 3. A semi-supervised approach for automatic detection and segmentation of optic disc from retinal fundus image
1. Introduction
2. State-of-the-art
3. Proposed method
4. Experimentations and results
5. Conclusions
Chapter 4. Medical decision support system using data mining: an intelligent health care monitoring system for guarded travel
1. Introduction
2. Related works
3. Proposed system
4. Performance analysis
5. Conclusion
Chapter 5. Deep learning in gastroenterology: a brief review
1. Introduction
2. Anomalies in GI-tract and medical image modalities for GE
3. Conventional-ML in gastroenterology
4. DL based GI-tract diagnosis system
5. Critical analysis and discussions
6. Conclusion
Chapter 6. Application of soft computing techniques to calculation of medicine dose during the treatment of patient: a fuzzy logic approach
1. Introduction
2. Soft computing
3. Fuzzy logic
4. Fuzzy logic based intelligent system
5. Comparison of drug doses suggested by expert doctor and proposed fuzzy based intelligent system
6. Conclusion
Chapter 7. Multiobjective optimization technique for gene selection and sample categorization
1. Introduction
2. Gene subset selection
3. Results and discussions
4. Conclusion and future work
Chapter 8. Medical decision support system using data mining semicircular-based angle-oriented facial recognition using neutrosophic logic
1. Introduction
2. Semicircular model based angle oriented images
3. Angle-oriented fuzzy rough sets
4. Ternary relationship with angle-oriented face recognition
5. K-means fuzzy rough angle-oriented clusters
6. Neutrosophic logic
7. Hyperplane
8. Evolutionary optimization method
9. Rotation and reduction procedure (R2 procedure)
10. Experimental result
11. Conclusion
Chapter 9. Preservation module prediction by weighted differentially coexpressed gene network analysis (WDCGNA) of HIV-1 disease: a case study for cancer
1. Introduction
2. Related work
3. Material and methods
4. Result and analysis
5. KEGG pathway analysis
6. Conclusion
Chapter 10. Computational intelligence for genomic data: a network biology approach
1. Introduction
2. Next generation sequencing overview
3. Different sequencing platforms
4. Different scores and parameters involved in biological network
5. Genomic data mining and biological network analysis: a case study
6. Summary and conclusions
Chapter 11. A Kinect-based motor rehabilitation system for stroke recovery
1. Introduction
2. Literature survey
3. Proposed work
4. Experimental results
5. Conclusion and future work
Chapter 12. Empirical study on Uddanam chronic kidney diseases (UCKD) with statistical and machine learning analysis including probabilistic neural networks
1. Introduction
2. Literature survey
3. Proposal model and materials
4. Results and discussions
5. Conclusion and social benefits
Chapter 13. Enhanced brain tumor detection using fractional wavelet transform and artificial neural network
1. Introduction
2. Literature survey
3. Fractional wavelet transform
4. Principal component analysis
5. Artificial neural network
6. Proposed method
7. Experimental results
8. Conclusion
Chapter 14. A study on smartphone sensor-based Human Activity Recognition using deep learning approaches
1. Introduction
2. Literature survey
3. Dataset description
4. Architecture of different deep networks
5. Results and discussion
6. Conclusion and future work
Index

Janmenjoy Nayak

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.

Affiliations and expertise

Associate Professor, Department of Computer Science and Engineering, Aditya Institute of Technology and Management, India

Bighnaraj Naik

Bighnaraj Naik is an Assistant Professor in the Department of Computer Application, Veer Surendra Sai University of Technology (formerly UCE Burla), Odisha, India. He has published more than 100 research articles in various peer reviewed international journals, conferences, and book chapters. He has edited 10 books for publishers including Elsevier, Springer, and IGI Global. At present, he has more than 10 years of teaching experience in the field of computer science and information technology. He is a member of the Institute of Electrical and Electronics Engineers (IEEE) and his areas of interest include data science, data mining, machine learning, deep learning, computational intelligence (CI), and CI’s applications in science and engineering. He has served as Guest Editor of various special issues of journals such as Information Fusion (Elsevier), Neural Computing and Applications (Springer), Evolutionary Intelligence (Springer), International Journal of Computational Intelligence Studies (Inderscience), and International Journal of Swarm Intelligence (Inderscience). He is an active reviewer of various journals from publishers including IEEE Transactions, Elsevier, Springer, and Inderscience. Currently, he is undertaking a major research project as Principal Investigator, which is funded by the Science and Engineering Research Board (SERB), Department of Science and Technology (DST), Government of India.

Affiliations and expertise

Assistant Professor, Department of Computer Application, Veer Surrendra Sai University of Technology, Burla, India

Danilo Pelusi

Danilo Pelusi is an Associate Professor in the Department of Communication Sciences, University of Teramo, where he received his PhD in Computational Astrophysics. He is an Editor of books for Springer and Elsevier, and an Associate Editor of IEEE Transactions on Emerging Topics in Computational Intelligence, and IEEE Access, and was an Associate Editor of International Journal of Machine Learning and Cybernetics. He is a Guest Editor for Elsevier, Springer, and Inderscience journals and keynote speaker in several IEEE conferences; he is also an editorial board member of many journals. His research interests include fuzzy logic, neural networks, information theory, machine learning, and evolutionary algorithms.

Affiliations and expertise

Associate Professor, Faculty of Communication Sciences, University of Teramo, Teramo, Italy

Asit Kumar Das

Asit Kumar Das is Professor of Computer Science and Technology, at the Indian Institute of Engineering Science and Technology Shibpur, Howrah. He is also the Head of the Center of Healthcare Science and Technology of the Institute. His area of research interest includes data mining and pattern recognition, social networks, bioinformatics, machine learning and soft computing, text, audio and video data analysis.

Affiliations and expertise

Indian Institute of Engineering Science and Technology, Shibpur, India

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Physical Sciences & Engineering

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Handbook of Computational Intelligence in Biomedical Engineering and Healthcare

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Janmenjoy Nayak

Bighnaraj Naik

Danilo Pelusi

Asit Kumar Das

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