Academic Press Machine Learning, Big Data, and IoT for Medical Informatics (Intelligent Data-Centric Systems)

Product Description Machine Learning, Big Data, and IoT for Medical Informatics focuses on the latest techniques adopted in the field of medical informatics. In medical informatics, machine learning, big data, and IOT-based techniques play a significant role in disease diagnosis and its prediction. In the medical field, the structure of data is equally important for accurate predictive analytics due to heterogeneity of data such as ECG data, X-ray data, and image data. Thus, this book focuses on the usability of machine learning, big data, and IOT-based techniques in handling structured and unstructured data. It also emphasizes on the privacy preservation techniques of medical data. This volume can be used as a reference book for scientists, researchers, practitioners, and academicians working in the field of intelligent medical informatics. In addition, it can also be used as a reference book for both undergraduate and graduate courses such as medical informatics, machine learning, big data, and IoT. To learn more about Elsevier’s Series, Intelligent Data-Centric Systems, please visit this link: https://www.elsevier.com/books-and-journals/book-series/intelligent-data-centric-systems-sensor-collected-intelligence Review Provides a thorough accounting of semantic information and structure of data with simple and straightforward examples. From the Back Cover Machine Learning, Big Data, and IoT for Medical Informatics focuses on the latest techniques adopted in the field of medical informatics. In medical informatics, machine learning, big data and IOT based techniques play a significant role for disease diagnosis and their prediction. In medical field, the structure of data is equally important for accurate predictive analytic due to heterogeneity of data like ECG data, X-Ray data, image data etc. Thus, this book focuses on the usability of machine learning; big data and IOT based techniques to handle structured and unstructured data. This book also emphasis on the privacy preservation techniques of medical data. This book can be used as a reference book for both undergraduate and graduate courses like medical informatics, machine learning, big data and IoT. About the Author Dr. Pardeep Kumar is currently working as an Associate Professor in the Department of Computer Science and Engineering at Jaypee University of Information Technology, Waknaghat, Solan, India, and has more than 14 years of experience in academia. Previously, he was associated with Mody University of Technology and Science, Laxmangarh, Sikar, Rajasthan. He completed his PhD in Computer Science and Engineering at Uttarakhand Technical University, Dehradun. He obtained his MTech in Computer Science and Engineering from Guru Jambheshwar University of Science and Technology, Hisar, Haryana, and his BTech in Information Technology from Kurukshetra University, Kurukshetra, Haryana. Dr. Kumar has been appointed as Associate Editor of the IEEE Access journal, as well as Guest Editor of several SCI and Scopus Index journals of international repute. Dr. Yugal Kumar is currently working as an Assistant Professor (senior grade) in the Department of Computer Science and Engineering at Jaypee University of Information Technology, Waknaghat, Solan, India. He completed his PhD in Computer Science and Engineering at the Birla institute of Technology, Mesra, Ranchi, and has now more than 14 years of teaching and research experience. He has presented and published more than 80 research papers, as well as participated in various national and international conferences. He is serving as a member of the editorial review board of various journals including PLOS ONE, Journal of Advanced Computational Intelligence and Intelligent Informatics, and Journal of Information Processing System. Mohamed A. Tawhid earned his PhD in Applied Mathematics from the University of Maryland Baltimore County, Maryland, United States. From 2000 to 2002, he was