Computational Intelligence and Its Applications in Healthcare
Editat de Jitendra Kumar Verma, Sudip Paul, Prashant Johrien Limba Engleză Paperback – 28 iul 2020
- 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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Specificații
ISBN-13: 9780128206041
ISBN-10: 0128206047
Pagini: 264
Ilustrații: Approx. 160 illustrations
Dimensiuni: 191 x 235 mm
Greutate: 0.47 kg
Editura: ELSEVIER SCIENCE
ISBN-10: 0128206047
Pagini: 264
Ilustrații: Approx. 160 illustrations
Dimensiuni: 191 x 235 mm
Greutate: 0.47 kg
Editura: ELSEVIER SCIENCE
Public țintă
Engineers, biomedical engineers, and researchers in computational intelligence, as well as computer scientists researching computational intelligence and its applications in healthcareCuprins
- 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