Computational Intelligence and Its Applications in Healthcare

  • 4h 24m
  • Jitendra Kumar Verma, Prashant Johri, Sudip Paul
  • Elsevier Science and Technology Books, Inc.
  • 2021

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.

In this Book

  • The Impact of Internet of Things and Data Semantics on Decision Making for Outpatient Monitoring
  • Deep-Learning Approaches for Health Care—Patients in Intensive Care
  • Brain MRI Image Segmentation Using Nature-Inspired Black Hole Metaheuristic Clustering Approach
  • Blockchain for Public Health—Technology, Applications, and a Case Study
  • Compression and Multiplexing of Medical Images Using Optical Image Processing
  • Analysis of Skin Lesions Using Machine Learning Techniques
  • Computational Intelligence using Ontology—A Case Study on the Knowledge Representation in a Clinical Decision Support System
  • Neural Network-Based Abnormality Detection for Electrocardiogram Time Signals
  • Machine Learning Approaches for Acetic Acid Test Based Uterine Cervix Image Analysis
  • Convolutional Neural Network for Biomedical Applications
  • Alzheimer's Disease Classification Using Deep Learning
  • Diabetic Retinopathy Identification using autoML
  • Knowledge-Based Systems in Medical Applications
  • Convolution Neural Network-Based Feature Learning Model for EEG-Based Driver Alertdd/Drowsy State Detection
  • Analysis on the Prediction of Central Line-Associated Bloodstream Infections (CLABSI) Using Deep Neural Network Classification
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