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Computational Intelligence and Its Applications in Healthcare

  • ID: 5007950
  • Book
  • July 2020
  • 264 Pages
  • Elsevier Science and Technology
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.

- Provides coverage of fuzzy logic, neural networks, evolutionary computation, learning theory, probabilistic methods, telemedicine, and robotics applications - Includes coverage of artificial intelligence and biological applications, soft computing, image and signal processing, and genetic algorithms - Presents the latest developments in computational methods in healthcare - Bridges the gap between obsolete literature and current literature

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  1. The impact of Internet of Things and data semantics on decision making for outpatient monitoring
  2. Deep-learning approaches for health care: Patients in intensive care
  3. Brain MRI image segmentation using nature-inspired Black Hole metaheuristic clustering approach
  4. Blockchain for public health: Technology, applications, and a case study
  5. Compression and multiplexing of medical images using optical image processing
  6. Analysis of skin lesions using machine learning techniques
  7. Computational intelligence using ontology-A case study on the knowledge representation in a clinical decision support system
  8. Neural network-based abnormality detection for electrocardiogram time signals
  9. Machine learning approaches for acetic acid test based uterine cervix image analysis
  10. Convolutional neural network for biomedical applications
  11. Alzheimer's disease classification using deep learning
  12. Diabetic retinopathy identification using autoML
  13. Knowledge-based systems in medical applications
  14. Convolution neural network-based feature learning model for EEG-based driver alert/drowsy state detection
  15. Analysis on the prediction of central line-associated bloodstream infections (CLABSI) using deep neural network classification
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Verma, Jitendra Kumar
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.
Paul, Sudip
Dr Sudip Paul completed his B.Tech and M.Tech degree in biomedical engineering. He received his PhD from Indian Institute of Technology, Banaras Hindu University, Varanasi, India, with specialization in electrophysiology and brain signal analysis. He received first prize of the Sushruta Innovation Award in 2011, sponsored by the Indian government's Department of Science and Technology. He also organized many workshops and conferences, including the 29th Annual Meeting of the Society for Neurochemistry in India. He worked as a postdoc fellow at Biotechnology Overseas Associateship for the Scientists in North Eastern States of India, supported by Department of Department of Biotechnology, Government of India, from 2017-18. Dr. Sudip has published over 70 international journal and conference papers and completed one patent. He also wrote two books published by LAP LAMBERT Academic Publishing, Germany, and published three book chapters in different international publishers.
Johri, Prashant
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.
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