
Intelligent Data Analysis for Biomedical Applications
Challenges and Solutions
- 294 pages
- English
- ePUB (mobile friendly)
- Available on iOS & Android
Intelligent Data Analysis for Biomedical Applications
Challenges and Solutions
About this book
Intelligent Data Analysis for Biomedical Applications: Challenges and Solutions presents specialized statistical, pattern recognition, machine learning, data abstraction and visualization tools for the analysis of data and discovery of mechanisms that create data. It provides computational methods and tools for intelligent data analysis, with an emphasis on problem-solving relating to automated data collection, such as computer-based patient records, data warehousing tools, intelligent alarming, effective and efficient monitoring, and more. This book provides useful references for educational institutions, industry professionals, researchers, scientists, engineers and practitioners interested in intelligent data analysis, knowledge discovery, and decision support in databases.- Provides the methods and tools necessary for intelligent data analysis and gives solutions to problems resulting from automated data collection- Contains an analysis of medical databases to provide diagnostic expert systems- Addresses the integration of intelligent data analysis techniques within biomedical information systems
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Information
Table of contents
- Cover image
- Title page
- Table of Contents
- Copyright
- List of Contributors
- Chapter 1. IoT-Based Intelligent Capsule Endoscopy System: A Technical Review
- Chapter 2. Optimization of Methods for Image-Texture Segmentation Using Ant Colony Optimization
- Chapter 3. A Feature Fusion-Based Discriminant Learning Model for Diagnosis of Neuromuscular Disorders Using Single-Channel Needle Electromyogram Signals
- Chapter 4. Evolution of Consciousness Systems With Bacterial Behaviour
- Chapter 5. Analysis of Transform-Based Compression Techniques for MRI and CT Images
- Chapter 6. A Medical Image Retrieval System in PACS Environment for Clinical Decision Making
- Chapter 7. A Neuro-Fuzzy Inference Model for Diabetic Retinopathy Classification
- Chapter 8. Computational Automated System for Red Blood Cell Detection and Segmentation
- Chapter 9. Evolutionary Algorithm With Memetic Search Capability for Optic Disc Localization in Retinal Fundus Images
- Chapter 10. Classification of Myocardial Ischemia in Delayed Contrast Enhancement Using Machine Learning
- Chapter 11. Simple-Link Sensor Network-Based Remote Monitoring of Multiple Patients
- Chapter 12. Hybrid Approach for Classification of Electroencephalographic Signals Using Time–Frequency Images With Wavelets and Texture Features
- Index