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Intelligent data analysis for biomed...

Hemanth, D. Jude,

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Intelligent data analysis for biomedical applications = challenges and solutions /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Intelligent data analysis for biomedical applications/ edited by Jude Hemanth, Deepak Gupta, Valentina Emilia Balas.
其他題名:	challenges and solutions /
其他作者:	Hemanth, D. Jude,
出版者:	London :Academic Press, : 2019.,
面頁冊數:	1 online resource.
內容註:	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
內容註:	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
內容註:	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
內容註:	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
內容註:	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; 3.5Results 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
標題:	Bioinformatics. -
電子資源:	https://www.sciencedirect.com/science/book/9780128155530
ISBN:	9780128156438 (electronic bk.)

Intelligent data analysis for biomedical applications = challenges and solutions /
Intelligent data analysis for biomedical applicationschallenges and solutions /[electronic resource] :edited by Jude Hemanth, Deepak Gupta, Valentina Emilia Balas. - London :Academic Press,2019. - 1 online resource. - Intelligent data centric systems. - Intelligent data centric systems..

Includes bibliographical references and index.

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

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 discoveryof 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.

ISBN: 9780128156438 (electronic bk.)Subjects--Topical Terms:

553671
Bioinformatics.
Index Terms--Genre/Form:

542853
Electronic books.

LC Class. No.: QH324.2

Dewey Class. No.: 005.7

Intelligent data analysis for biomedical applications = challenges and solutions /
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505 0 $a 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
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505 8 $a 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
505 8 $a 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
505 8 $a 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; 3.5Results 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
520 $a 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 discoveryof 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.
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