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This book explores the healthcare applications of sensor data analytics and also focuses on real-time and streaming analytics in healthcare. Covering sensor data analytics fundamentals, the book explores sensor data collection and data analysis applications. It also discusses healthcare applications of wearable devices.
List of contents
Chapter 1. Healthcare Sensor Data Analytics: Foundational Concepts, Architecture, Data Analysis Techniques, Data Quality, Errors, and Applications.
Chapter 2. Statistical Approximation of Streaming Sensor Data for Enhancing Healthcare Analytics.
Chapter 3. Sensor Data Analytics for Transformative Healthcare Solutions - A Comprehensive Insights.
Chapter 4. Healthcare Sensor Data Analysis for Real-Time Anomaly Detection: A Comprehensive Insights.
Chapter 5. Basic Concepts and Principles of Healthcare Sensor Data Analytics.
Chapter 6. Design and Development of Physiotherapy Robot to Aid Medical Practitioners for Human Upper-limb Rehabilitation.
Chapter 7. Mortality Prediction in Non-Alcoholic Fatty Liver Disease using Machine Learning.
Chapter 8. Predicting Cognitive Impairment through Handwriting Analysis: A Machine Learning Framework for Enhanced Clinical Decision-Making.
Chapter 9. Optimizing Deep Learning for Pneumonia Diagnosis using Chest X-Ray Data.
Chapter 10. Melanoma Detection Using Deep Convolutional Neural Networks: A High-Resolution Image-Based Approach.
Chapter 11. Advanced Skin Cancer Classification Using Xception Deep Learning Architecture using Dermoscopic Images.
Chapter 12. Neuro-Fuzzy Multimodal Ensemble Algorithm for Alzheimer's Disease Prediction.
Chapter 13. Optimizing Bidirectional Encoder Representations from Transformers for Medical Healthcare: Unveiling Insights from Fine-Tuning for Efficient Question Answering with Gen AI.
Chapter 14. Utilizing Cosine Wavelet Geometric Guided Sparse Representation Transform for Lossless Medical Image Compression to Enhance Image Quality.
Chapter 15. A Comprehensive Analysis on Advanced Techniques in Healthcare Sensor Data Analytics.
Chapter 16. Internet of Medical Things in Healthcare: Enhancing Patient Care and Monitoring through Connected Devices.
About the author
G.S.Karthick is currently working as an Assistant Professor in the Department of Software Systems, PSG College of Arts & Science, Coimbatore, India. Previously, he worked as a Research Fellow under the DST-ICPS Project from April 2019 to March 2022 in the Department of Computer Science, School of Computer Science and Engineering, Bharathiar University, Coimbatore, India. Also, he worked as an Assistant Professor in the Department of Computer Science, Sri Ramakrishna College of Arts and Science, Coimbatore, India. He received his Ph.D. in Computer Science from Bharathiar University (University Department) in February 2023. He topped the Post-Graduation with Gold Medal in 2017 from Bharathiar University, Coimbatore, India. He topped Under-Graduation with an exemplary performance from SNR Sons College, Coimbatore, India, and also aced three subjects to receive gold medals in 2015. He has qualified UGC-NET Computer Science and Applications in December 2020. Further, he has visited many institutions and universities as a resource person at various events. He has received an Indian Patent Grant for his innovative contribution to the field of Wireless Networks and the Internet of Things. He has published research papers in international journals and presented papers at International and National Conferences. He has published many book chapters and in addition, he has edited four books and authored two books. He is an Associate Editor of IJHISI, IJITN and IJEHMC. Also, he is an active reviewer in high-standard journals and many peer-reviewed journals. He has received many awards and rewards for his achievements in academic and research careers. His areas of specialization are Machine Learning, the Internet of Things, Wireless Sensor Networks, and Analysis of Algorithms. His current research interests focus on the recent developments in the healthcare internet of things and machine learning algorithms.
