Machine Learning and the Internet of Medical Things in Healthcare
Coordonnateurs : Singh Krishna Kant, Elhoseny Mohamed, Singh Akansha, Elngar Ahmed A.
Machine Learning and the Internet of Medical Things in Healthcare discusses the applications and challenges of machine learning for healthcare applications. The book provides a platform for presenting machine learning-enabled healthcare techniques and offers a mathematical and conceptual background of the latest technology. It describes machine learning techniques along with the emerging platform of the Internet of Medical Things used by practitioners and researchers worldwide.
The book includes deep feed forward networks, regularization, optimization algorithms, convolutional networks, sequence modeling, and practical methodology. It also presents the concepts of the Internet of Things, the set of technologies that develops traditional devices into smart devices. Finally, the book offers research perspectives, covering the convergence of machine learning and IoT. It also presents the application of these technologies in the development of healthcare frameworks.
1. Machine Learning Architecture and Framework 2. Machine Learning in Healthcare: Review, Opportunities and Challenges 3. Machine Learning for Biomedical Signal Processing 4. Artificial Intelligence in Medicine 5. Diagnosing of Disease Using Machine Learning 6. A Novel Approach of Telemedicine for Managing Fetal Condition based on Machine Learning Technology from Iot Based Wearable Medical Device 7. Iot Based Healthcare Delivery Services to Promote Transparency and Patient Satisfaction in a Corporate Hospital 8. Examining Diabetic Subjects on Their Correlation with TTH and CAD: A Statistical Approach on Exploratory Results 9. Cancer Prediction and Diagnosis Hinged on HCML in IOMT Environment 10. Parameterization Techniques for Automatic Speech Recognition System 11. Impact of Big Data in Healthcare System: A Quick Look into Electronic Health Record Systems
Dr. Mohamed Elhoseny is an Associate Professor at the University of Sharjah, UAE. Dr. Elhoseny is an ACM Distinguished Speaker and IEEE Senior Member. His research interests include Smart Cities, Network Security, Artificial Intelligence, Internet of Things, and Intelligent Systems. Dr. Elhoseny is the founder and the Editor-in-Chief of the IJSSTA journal published by IGI Global, as well as Associate Editor at several Q1 journals such as IEEE Access, Scientific Reports, IEEE Future Directions, Remote Sensing, International Journal of E-services and Mobile Applications and Human-centric Computing and Information Sciences. He has also served as the co-chair, publication chair, program chair, and a track chair for several international conferences published by recognized publishers. Dr. Elhoseny is Editor-in-Chief of two book series, on Sensor Communication for Urban Intelligence and Distributed Sensing and Intelligent Systems.
Dr. Akansha Singh is B.Tech, M.Tech and PhD in Computer Science. She received her PhD from IIT Roorkee in the area of image processing and machine learning. Currently, she is working as Associate Professor in Department of Computer Science and Engineering, ASET, Amity University, Noida. She has to her credit more than 70 research papers, 20 books and numerous conferen
- Provides an introduction to the Internet of Medical Things through the principles and applications of machine learning
- Explains the functions and applications of machine learning in various applications such as ultrasound imaging, biomedical signal processing, robotics, and biomechatronics
- Includes coverage of the evolution of healthcare applications with machine learning, including Clinical Decision Support Systems, artificial intelligence in biomedical engineering, and AI-enabled connected health informatics, supported by real-world case studies
Date de parution : 04-2021
Ouvrage de 290 p.
19x23.3 cm
Thèmes de Machine Learning and the Internet of Medical Things in... :
Mots-clés :
?AI framework; Accuracy; Adaptive filter; Autoimmune disease; Big data; Big data analytics; Biomedical signal processing; CAD; Classification; Classification techniques; Cloud computing; Correlation; Cost effectiveness; Decision tree; Degenerative diseases; Diabetes; Diagnosis; Disease; Electrocardiogram (ECG); Electronic health record; Feature extraction; Fetus health using machine learning algorithms; HCML; HTN; Health care systems; Healthcare; Infectious diseases; Intellectual disability; Internet of Things; Internet of things (IoT); IoT; KNN; Linear predictive analysis; Machine learning; Medical field; Medical records; Mel-frequency cepstral coefficients; Mental diseases; NB; Patient satisfaction; Performance; Physical diseases; Power spectrum density; Prognosis; Role of big data in healthcare; SVM; Smart systems; Social diseases; Speech pathologies; Stress; Supervised learning; TTH; Transparency; Unsupervised learning; Use of machine learning in healthcare industry; Use of wearable devices in tracking fetal health; Value-chain
