Green Computing and Predictive Analytics for Healthcare
Seiten
2024
Chapman & Hall/CRC (Verlag)
978-0-367-62607-5 (ISBN)
Chapman & Hall/CRC (Verlag)
978-0-367-62607-5 (ISBN)
The emergent trends in Green Cloud Computing lead to new developments in various application domains, mainly in healthcare. The aim of this book is to collect innovative and high-quality research contributions related to the advances in the energy-aware cloud-enabled healthcare domain.
Green Computing and Predictive Analytics for Healthcare excavates the rudimentary concepts of Green Computing, Big Data and the Internet of Things along with the latest research development in the domain of healthcare. It also covers various applications and case studies in the field of computer science with state-of-the-art tools and technologies. The rapid growth of the population is a challenging issue in maintaining and monitoring various experiences of quality of service in healthcare. The coherent usage of these limited resources in connection with optimum energy consumption has been becoming more important. The major healthcare nodes are gradually becoming Internet of Things-enabled, and sensors, work data and the involvement of networking are creating smart campuses and smart houses. The book includes chapters on the Internet of Things and Big Data technologies.
Features:
Biomedical data monitoring under the Internet of Things
Environment data sensing and analyzing
Big data analytics and clustering
Machine learning techniques for sudden cardiac death prediction
Robust brain tissue segmentation
Energy-efficient and green Internet of Things for healthcare applications
Blockchain technology for the healthcare Internet of Things
Advanced healthcare for domestic medical tourism system
Edge computing for data analytics
This book on Green Computing and Predictive Analytics for Healthcare aims to promote and facilitate the exchange of research knowledge and findings across different disciplines on the design and investigation of healthcare data analytics. It can also be used as a textbook for a master’s course in biomedical engineering. This book will also present new methods for medical data evaluation and the diagnosis of different diseases to improve quality-of-life in general and for better integration of Internet of Things into society.
Dr. Sourav Banerjee is an Assistant Professor at the Department of Computer Science and Engineering of Kalyani Government Engineering College, Kalyani, West Bengal, India. His research interests include Big Data, Cloud Computing, Distributed Computing and Mobile Communications.
Dr. Chinmay Chakraborty is an Assistant Professor at the Department of Electronics and Communication Engineering, Birla Institute of Technology, Mesra, India. His main research interests include the Internet of Medical Things, WBAN, Wireless Networks, Telemedicine, m-Health/e-Health and Medical Imaging.
Dr. Kousik Dasgupta is an Assistant Professor at the Department of Computer Science and Engineering, Kalyani Government Engineering College, India. His research interests include Computer Vision, AI/ML, Cloud Computing, Big Data and Security.
Green Computing and Predictive Analytics for Healthcare excavates the rudimentary concepts of Green Computing, Big Data and the Internet of Things along with the latest research development in the domain of healthcare. It also covers various applications and case studies in the field of computer science with state-of-the-art tools and technologies. The rapid growth of the population is a challenging issue in maintaining and monitoring various experiences of quality of service in healthcare. The coherent usage of these limited resources in connection with optimum energy consumption has been becoming more important. The major healthcare nodes are gradually becoming Internet of Things-enabled, and sensors, work data and the involvement of networking are creating smart campuses and smart houses. The book includes chapters on the Internet of Things and Big Data technologies.
Features:
Biomedical data monitoring under the Internet of Things
Environment data sensing and analyzing
Big data analytics and clustering
Machine learning techniques for sudden cardiac death prediction
Robust brain tissue segmentation
Energy-efficient and green Internet of Things for healthcare applications
Blockchain technology for the healthcare Internet of Things
Advanced healthcare for domestic medical tourism system
Edge computing for data analytics
This book on Green Computing and Predictive Analytics for Healthcare aims to promote and facilitate the exchange of research knowledge and findings across different disciplines on the design and investigation of healthcare data analytics. It can also be used as a textbook for a master’s course in biomedical engineering. This book will also present new methods for medical data evaluation and the diagnosis of different diseases to improve quality-of-life in general and for better integration of Internet of Things into society.
Dr. Sourav Banerjee is an Assistant Professor at the Department of Computer Science and Engineering of Kalyani Government Engineering College, Kalyani, West Bengal, India. His research interests include Big Data, Cloud Computing, Distributed Computing and Mobile Communications.
Dr. Chinmay Chakraborty is an Assistant Professor at the Department of Electronics and Communication Engineering, Birla Institute of Technology, Mesra, India. His main research interests include the Internet of Medical Things, WBAN, Wireless Networks, Telemedicine, m-Health/e-Health and Medical Imaging.
Dr. Kousik Dasgupta is an Assistant Professor at the Department of Computer Science and Engineering, Kalyani Government Engineering College, India. His research interests include Computer Vision, AI/ML, Cloud Computing, Big Data and Security.
Sourav Banerjee, Chinmay Chakraborty, Kousik Dasgupta
Healthcare Data Monitoring under Internet of Things. A Framework for Emergency Remote Care and Monitoring using Internet of things. Big data Analytics and k-means Clustering Headed for Patient Health Records for a Healthier. Machine learning based Rapid Prediction of Sudden Cardiac Death (SCD) using precise Statistical Features of Heart rate variability for Single Lead ECG signa. Computer Vision for Brain Tissue Segmentatio. A Study on Energy Efficient and Green IoT for Healthcare Application. Cyber Security in terms of IoT System and Block-Chain Technologies in E-health Care System. Domestic Medical Tourism in India. Study on Edge Computing using Machine Learning Approaches in IoT Framework
| Erscheinungsdatum | 12.09.2024 |
|---|---|
| Zusatzinfo | 18 Tables, black and white; 39 Illustrations, black and white |
| Sprache | englisch |
| Maße | 178 x 254 mm |
| Gewicht | 376 g |
| Themenwelt | Mathematik / Informatik ► Informatik ► Netzwerke |
| Naturwissenschaften ► Biologie | |
| ISBN-10 | 0-367-62607-1 / 0367626071 |
| ISBN-13 | 978-0-367-62607-5 / 9780367626075 |
| Zustand | Neuware |
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