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E-raamat: Handbook of Computational Intelligence in Biomedical Engineering and Healthcare

Edited by (Associate Professor, Department of Computer Science and Engineering, Aditya Institute of Technology and Management, India), Edited by , Edited by , Edited by (Assistant Professor, Department of Computer Application, Veer Surrendra Sai University of Technology, Burla, I)
  • Formaat: EPUB+DRM
  • Ilmumisaeg: 08-Apr-2021
  • Kirjastus: Academic Press Inc
  • Keel: eng
  • ISBN-13: 9780128222614
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  • Formaat: EPUB+DRM
  • Ilmumisaeg: 08-Apr-2021
  • Kirjastus: Academic Press Inc
  • Keel: eng
  • ISBN-13: 9780128222614
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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.

  • 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
Contributors ix
Biographies xi
Preface xiii
1 Application of dynamical systems based deep learning algorithms to model emergent characteristics for healthcare diagnostics
David Al-Dabass
Lela Mirtskhulava
1 Introduction
1(1)
2 Deep learning applications for brainwaves monitoring
2(8)
3 Healthcare Modeling and simulation using feedback hybrid artificial neural networks
10(7)
4 Derivative estimation using feedback networks
17(5)
5 Usage of deep learning knowledge mining in Hybrid Inference Networks
22(6)
6 Conclusions
28(3)
References
28(3)
2 Computational intelligence in healthcare and biosignal processing
Nagaraj Balakrishnan
Valentina E. Balas
Arunkumar Rajendran
1 Introduction
31(5)
2 Investigation on various deep clustering algorithms
36(16)
3 Investigation on clustering algorithms for the unsupervised learning methodology
52(10)
4 Conclusion
62(3)
References
62(3)
3 A semi-supervised approach for automatic detection and segmentation of optic disc from retinal fundus image
Susovan Jana
Ranjan Parekh
Bijan Sarkar
1 Introduction
65(2)
2 State-of-the-art
67(4)
3 Proposed method
71(11)
4 Experimentations and results
82(6)
5 Conclusions
88(5)
References
88(5)
4 Medical decision support system using data mining: an intelligent health care monitoring system for guarded travel
L. Jegatha Deborah
S.C. Rajkumar
P. Vijayakumar
1 Introduction
93(1)
2 Related works
94(5)
3 Proposed system
99(9)
4 Performance analysis
108(9)
5 Conclusion
117(4)
References
117(4)
5 Deep learning in gastroenterology: a brief review
Subhashree Mohapatra
Tripti Swamkar
Manohar Misbra
David Al-Dabass
Raffaele Mascella
1 Introduction
121(3)
2 Anomalies in GI-tract and medical image modalities for GE
124(5)
3 Conventional-ML in gastroenterology
129(1)
4 DL based GI-tract diagnosis system
129(13)
5 Critical analysis and discussions
142(2)
6 Conclusion
144(7)
References
144(7)
6 Application of soft computing techniques to calculation of medicine dose during the treatment of patient: a fuzzy logic approach
Ramjeet Singh Yadav
1 Introduction
151(3)
2 Soft computing
154(1)
3 Fuzzy logic
154(9)
4 Fuzzy logic based intelligent system
163(11)
5 Comparison of drug doses suggested by expert doctor and proposed fuzzy based intelligent system
174(3)
6 Conclusion
177(2)
References
178(1)
7 Multiobjective optimization technique for gene selection and sample categorization
Sunanda Das
Asit Kumar Das
1 Introduction
179(3)
2 Gene subset selection
182(6)
3 Results and discussions
188(1)
4 Conclusion and future work
189(6)
References
192(3)
8 Medical decision support system using data mining semicircular-based angle-oriented facial recognition using neutrosophic logic
R.N.V. Jagan Mohan
1 Introduction
195(2)
2 Semicircular model based angle oriented images
197(3)
3 Angle-oriented fuzzy rough sets
200(1)
4 Ternary relationship with angle-oriented face recognition
201(1)
5 K-means fuzzy rough angle-oriented clusters
202(1)
6 Neutrosophic logic
202(2)
7 Hyperplane
204(1)
8 Evolutionary optimization method
205(1)
9 Rotation and reduction procedure (R2 procedure)
205(1)
10 Experimental result
206(4)
11 Conclusion
210(3)
References
210(3)
9 Preservation module prediction by weighted differentially coexpressed gene network analysis (WDCGNA) of HIV-1 disease: a case study for cancer
Ria Kanjilal
Bandana Barman
Mainak Kumar Kundu
1 Introduction
213(1)
2 Related work
214(1)
3 Material and methods
215(10)
4 Result and analysis
225(5)
5 KEGG pathway analysis
230(15)
6 Conclusion
245(2)
References
245(2)
10 Computational intelligence for genomic data: a network biology approach
Parameswar Sahu
Fahmida Khan
Suhrat Kumar Pattanayak
1 Introduction
247(1)
2 Next generation sequencing overview
248(2)
3 Different sequencing platforms
250(4)
4 Different scores and parameters involved in biological network
254(1)
5 Genomic data mining and biological network analysis: a case study
255(1)
6 Summary and conclusions
256(5)
References
257(4)
11 A Kinect-based motor rehabilitation system for stroke recovery
Sriparna Saha
Ncha Das
1 Introduction
261(1)
2 Literature survey
262(1)
3 Proposed work
262(7)
4 Experimental results
269(7)
5 Conclusion and future work
276(7)
References
277(2)
Appendix
279(2)
Screenshot
281(1)
Screenshot
282(1)
12 Empirical study on Uddanam chronic kidney diseases (UCKD) with statistical and machine learning analysis including probabilistic neural networks
T. PanduRanga Vital
1 Introduction
283(2)
2 Literature survey
285(4)
3 Proposal model and materials
289(4)
4 Results and discussions
293(19)
5 Conclusion and social benefits
312(3)
References
313(2)
13 Enhanced brain tumor detection using fractional wavelet transform and artificial neural network
Bhakri Kaushal
Mukcsh D. Patil
Gajanan K. Birajdar
1 Introduction
315(4)
2 Literature survey
319(2)
3 Fractional wavelet transform
321(7)
4 Principal component analysis
328(1)
5 Artificial neural network
329(1)
6 Proposed method
330(3)
7 Experimental results
333(4)
8 Conclusion
337(6)
References
340(3)
14 A study on smartphone sensor-based Human Activity Recognition using deep learning approaches
Riktim Mondal
Dihycndu Mukhopadhyay
Sayanwita Barua
Pawan Kumar Singh
Ram Sarkar
Dehotosh Bhattacharjee
1 Introduction
343(2)
2 Literature survey
345(1)
3 Dataset description
346(2)
4 Architecture of different deep networks
348(5)
5 Results and discussion
353(14)
6 Conclusion and future work
367(1)
References 367(4)
Index 371
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 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 CIs 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. 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. 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.