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
