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 |
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ix | |
Biographies |
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xi | |
Preface |
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xiii | |
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1 Application of dynamical systems based deep learning algorithms to model emergent characteristics for healthcare diagnostics |
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1 | (1) |
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2 Deep learning applications for brainwaves monitoring |
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2 | (8) |
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3 Healthcare Modeling and simulation using feedback hybrid artificial neural networks |
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10 | (7) |
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4 Derivative estimation using feedback networks |
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17 | (5) |
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5 Usage of deep learning knowledge mining in Hybrid Inference Networks |
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22 | (6) |
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28 | (3) |
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28 | (3) |
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2 Computational intelligence in healthcare and biosignal processing |
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31 | (5) |
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2 Investigation on various deep clustering algorithms |
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36 | (16) |
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3 Investigation on clustering algorithms for the unsupervised learning methodology |
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52 | (10) |
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62 | (3) |
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62 | (3) |
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3 A semi-supervised approach for automatic detection and segmentation of optic disc from retinal fundus image |
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65 | (2) |
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67 | (4) |
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71 | (11) |
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4 Experimentations and results |
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82 | (6) |
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88 | (5) |
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88 | (5) |
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4 Medical decision support system using data mining: an intelligent health care monitoring system for guarded travel |
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93 | (1) |
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94 | (5) |
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99 | (9) |
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108 | (9) |
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117 | (4) |
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117 | (4) |
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5 Deep learning in gastroenterology: a brief review |
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121 | (3) |
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2 Anomalies in GI-tract and medical image modalities for GE |
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124 | (5) |
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3 Conventional-ML in gastroenterology |
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129 | (1) |
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4 DL based GI-tract diagnosis system |
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129 | (13) |
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5 Critical analysis and discussions |
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142 | (2) |
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144 | (7) |
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144 | (7) |
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6 Application of soft computing techniques to calculation of medicine dose during the treatment of patient: a fuzzy logic approach |
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151 | (3) |
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154 | (1) |
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154 | (9) |
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4 Fuzzy logic based intelligent system |
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163 | (11) |
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5 Comparison of drug doses suggested by expert doctor and proposed fuzzy based intelligent system |
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174 | (3) |
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177 | (2) |
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178 | (1) |
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7 Multiobjective optimization technique for gene selection and sample categorization |
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179 | (3) |
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182 | (6) |
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3 Results and discussions |
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188 | (1) |
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4 Conclusion and future work |
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189 | (6) |
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192 | (3) |
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8 Medical decision support system using data mining semicircular-based angle-oriented facial recognition using neutrosophic logic |
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195 | (2) |
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2 Semicircular model based angle oriented images |
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197 | (3) |
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3 Angle-oriented fuzzy rough sets |
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200 | (1) |
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4 Ternary relationship with angle-oriented face recognition |
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201 | (1) |
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5 K-means fuzzy rough angle-oriented clusters |
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202 | (1) |
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202 | (2) |
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204 | (1) |
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8 Evolutionary optimization method |
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205 | (1) |
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9 Rotation and reduction procedure (R2 procedure) |
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205 | (1) |
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206 | (4) |
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210 | (3) |
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210 | (3) |
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9 Preservation module prediction by weighted differentially coexpressed gene network analysis (WDCGNA) of HIV-1 disease: a case study for cancer |
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213 | (1) |
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214 | (1) |
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215 | (10) |
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225 | (5) |
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230 | (15) |
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245 | (2) |
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245 | (2) |
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10 Computational intelligence for genomic data: a network biology approach |
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247 | (1) |
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2 Next generation sequencing overview |
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248 | (2) |
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3 Different sequencing platforms |
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250 | (4) |
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4 Different scores and parameters involved in biological network |
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254 | (1) |
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5 Genomic data mining and biological network analysis: a case study |
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255 | (1) |
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6 Summary and conclusions |
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256 | (5) |
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257 | (4) |
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11 A Kinect-based motor rehabilitation system for stroke recovery |
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261 | (1) |
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262 | (1) |
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262 | (7) |
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269 | (7) |
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5 Conclusion and future work |
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276 | (7) |
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277 | (2) |
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279 | (2) |
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281 | (1) |
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282 | (1) |
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12 Empirical study on Uddanam chronic kidney diseases (UCKD) with statistical and machine learning analysis including probabilistic neural networks |
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283 | (2) |
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285 | (4) |
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3 Proposal model and materials |
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289 | (4) |
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4 Results and discussions |
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293 | (19) |
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5 Conclusion and social benefits |
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312 | (3) |
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313 | (2) |
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13 Enhanced brain tumor detection using fractional wavelet transform and artificial neural network |
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315 | (4) |
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319 | (2) |
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3 Fractional wavelet transform |
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321 | (7) |
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4 Principal component analysis |
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328 | (1) |
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5 Artificial neural network |
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329 | (1) |
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330 | (3) |
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333 | (4) |
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337 | (6) |
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340 | (3) |
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14 A study on smartphone sensor-based Human Activity Recognition using deep learning approaches |
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343 | (2) |
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345 | (1) |
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346 | (2) |
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4 Architecture of different deep networks |
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348 | (5) |
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353 | (14) |
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6 Conclusion and future work |
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367 | (1) |
References |
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367 | (4) |
Index |
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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.