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Intelligent data analysis for biomedical applications : challenges and solutions
Title
Intelligent data analysis for biomedical applications : challenges and solutions / edited by Jude Hemanth, Deepak Gupta, Valentina Emilia Balas.
ISBN
9780128156438
0128156430
9780128155530
0128155531
Publication
London : Academic Press, 2019.
Physical Description
1 online resource.
Local Notes
Access is available to the Yale community.
Access and use
Access restricted by licensing agreement.
Summary
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.
Variant and related titles
Elsevier ScienceDirect All Books. OCLC KB.
Format
Books / Online
Language
English
Added to Catalog
May 28, 2019
Series
Intelligent data centric systems
Bibliography
Includes bibliographical references and index.
Contents
Front Cover; Intelligent Data Analysis for Biomedical Applications; Copyright Page; Contents; List of Contributors; 1 IoT-Based Intelligent Capsule Endoscopy System: A Technical Review; 1.1 Introduction; 1.2 Data Acquisition; 1.2.1 Image Sensor; 1.2.2 Optical Sensor; 1.2.3 Pressure, Temperature, and pH-Monitoring Sensor; 1.2.4 Other Ingestible Sensors; 1.3 On-Chip Data-Processing Unit; 1.3.1 Image Compression; 1.3.2 Application Specific Integrated Circuit Design; 1.3.3 Radiofrequency Transmission; 1.3.4 Power Management; 1.4 Data Management of Wireless Capsule Endoscopy Systems
1.5 IoT-Based Wireless Capsule Endoscopy System1.5.1 Intelligence in the System; 1.5.2 Real-Time Sensing; 1.5.3 Internet of Things Protocol; 1.5.4 Connectivity; 1.5.5 Security; 1.5.6 Improved Outcomes of Treatment; 1.6 Future Challenges; 1.7 Conclusion; References; 2 Optimization of Methods for Image-Texture Segmentation Using Ant Colony Optimization; 2.1 Introduction; 2.2 Implementation of Ant Colony Optimization Algorithm; 2.2.1 Isula Framework; 2.2.2 Ant Route Construction; 2.2.3 Ant Pheromone Update; 2.3 Image Segmentation Techniques; 2.3.1 Threshold-Based Segmentation
2.3.1.1 Otsu' Algorithm2.3.1.2 Ant Colony Optimization-Based Multilevel Thresholds Selection; 2.3.1.3 Algorithm for Ant Colony Optimization; 2.3.2 Edge-Based Segmentation; 2.3.2.1 Ant Colony Optimization-Based Edge Detection Initialization; 2.3.2.2 Ant Colony Optimization-Based Structuring Process; 2.3.2.3 Ant Colony Optimization-Based Updating Process; 2.3.2.4 Decision Process; 2.4 Evaluation of Segmentation Techniques; 2.4.1 Mean-Square Error; 2.4.2 Root-Mean-Square-Error; 2.4.3 Signal-to-Noise Ratio; 2.4.4 Peak Signal-to-Noise Ratio; 2.5 Experiments and Results
2.5.1 Ant Colony Optimization-Image-Segmentation Using the Isula Framework2.5.2 Performance Testing Ant Colony Optimization Image Segmentation Algorithm; 2.5.3 Application of Ant Colony Optimization on Segmentation of Brain MRI; 2.5.4 Ant Colony Optimization-Image Segmentation on Iris Images; 2.5.5 Comparison of Results; 2.6 Conclusion; References; Further Reading; 3 A Feature Fusion-Based Discriminant Learning Model for Diagnosis of Neuromuscular Disorders Using Single-Channel Needle E...; 3.1 Introduction; 3.2 State-of-Art-Methods; 3.3 Theoretical Modeling of Learning from Big Data
3.3.1 Strategy Statement3.3.2 Discriminant Feature Fusion Framework; 3.3.3 Generalized Multidomain Learning; 3.4 Medical Measurements and Data Analysis; 3.4.1 Electromyogram Signal Recording Setup; 3.4.2 Electromyogram Datasets; 3.5 Results and Discussion; 3.5.1 Correlation Analysis; 3.5.2 Performance Investigation of Discriminant Learning Scheme; 3.5.3 Comparative Study; 3.6 Conclusion; References; Further Reading; 4 Evolution of Consciousness Systems With Bacterial Behaviour; 4.1 Introduction; 4.2 Proposal; 4.2.1 Working Assumptions?; 4.2.2 Real Life Assumptions; 4.2.3 Consciousness Theory
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