Description
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
Table of Contents
- 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 alert/drowsy state detection- Analysis on the prediction of central line-associated bloodstream infections (CLABSI) using deep neural network classification
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- 電子書籍
- どうだ貫一（３）
