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Technical Advancements of Machine Learning in Healthcare de Hrudaya Kumar Tripathy

Technical Advancements of Machine Learning in Healthcare: Studies in Computational Intelligence, cartea 936

Editat de Hrudaya Kumar Tripathy, Sushruta Mishra, Pradeep Kumar Mallick, Amiya Ranjan Panda
en Limba Engleză Paperback – mar 2022
This book focuses on various advanced technologies which integrate with machine learning to assist one of the most leading industries, healthcare. It presents recent research works based on machine learning approaches supported by medical and information communication technologies with the use of data and image analysis. The book presents insight about techniques which broadly deals in delivery of quality, accurate and affordable healthcare solutions by predictive, proactive and preventative methods. The book also explores the possible use of machine learning in enterprises, such as enhanced medical imaging/diagnostics, understanding medical data, drug discovery and development, robotic surgery and automation, radiation treatments, creating electronic smart records and outbreak prediction.
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Specificații

ISBN-13: 9789813347007
ISBN-10: 9813347007
Ilustrații: XIII, 388 p. 196 illus., 138 illus. in color.
Dimensiuni: 155 x 235 mm
Greutate: 0.56 kg
Ediția:1st ed. 2021
Editura: Springer Nature Singapore
Colecția Springer
Seria Studies in Computational Intelligence

Locul publicării:Singapore, Singapore

Cuprins

Innovation on Machine Learning in Healthcare Services - An Introduction.- Big Data Application in Health Care: A Study.- Empirical Study on Different Feature Selection and Classification Algorithms for Prediction of Hepatitis Disease.- Meta Cognitive Neural Network for Emphysema Classification.- Analysis of Gaussian & Cauchy Mutations in  K-Means Particle Swarm Optimization Algorithm for Data Clustering.- Emergence of Drug Discovery in Machine Learning.- Deep Learning Frameworks in Healthcare systems.- Deep Learning Neural Network and CNN based Diagnosis of Heart Diseases.- Deep learning model for efficient mammogram analysis.

Notă biografică

Dr. Hrudaya Kumar Tripathy is an Associate Professor of the School of Computer Engineering, KIIT University, Bhubaneswar, India. He has completed a Doctorate in Computer Science from Berhampur University, a Master in Computer Science Engineering from the Indian Institute of Technology, Guwahati, and received Post-Doctoral Fellowship from the Ministry of Higher Education Malaysia. He has 20 years of teaching experience with post doctorate research experience. He had been Visiting Faculty for a couple of years in Asia Pacific University, Kuala Lumpur, Malaysia, and the University of Utara Malaysia, Sintok, Malaysia. Dr.Tripathy awarded a Young IT professional award in 2013 on a regional level from the Computer Society of India (CSI). He has more than 100 research publications in different national/international journals and conferences to his credit.
Dr. Sushruta Mishra is an Indian by birth. He completed his B.Tech. from ITER, Bhubaneswar, in 2009. He pursued his M.Tech. degree from IIIT, Bhubaneswar, in 2012. He completed his Ph.D. from KIIT University, Bhubaneswar, in 2017. Currently, he is working as Assistant Professor in KIIT University, Bhubaneswar, in the School of Computer Engineering. He has a good 7 years of academic teaching experience. Her prime research area of interest includes machine learning, sentiment analysis, cognitive computing and robotics. Dr. Mishra has published more than 60 research and development articles in various reputed journals, conferences and book chapters.  
Dr. Pradeep Kumar Mallick is currently working as Senior Associate Professor  in the School of Computer Engineering , KIIT Deemed to be University, India .He has also served as Professor and Head Department of Computer Science and Engineering,Vignana Bharathi Institute of Technology, Hyderabad. He has completed his Post Doctoral Fellowship from Kongju National University South Korea,PhD from Siksha Ó’ Anusandhan University, India and Master degree from Bijupatnaik Unversity ,Odisha . Besides academics, he is also involved in various administrative activities, Member of Board of Studies , Member of Doctoral Research Evaluation Committee, Admission Committee etc.Now he is the editorial member of Korean Convergence Society for SMB.He has published 12 edited  books,1 text Book and more than 100 research papers in National and international journals and conference proceedings to his credit.
Dr. Amiya Ranjan Panda has seven years of research experience in DRDO Chandipur and more than three years of teaching experience. He received B.Tech. degree in Information Technology from Biju Patnaik University of Technology, Odisha, India, in 2009, M.Tech. degree in Computer Science and Engineering from the KIIT University, India  in 2012. and Ph.D. degree from Siksha ‘O’ Anusandhan University, Odisha, in the year 2017 . Currently, he is working as Assistant Professor in KIIT Deemed to be University, Bhubaneswar. His research interest is machine learning, IoT, data acquisition system and software-defined radio. He has published more than 25 articles in international journals. Currently he is working with a real-time project of DRDO. 

Textul de pe ultima copertă

This book focuses on various advanced technologies which integrate with machine learning to assist one of the most leading industries, healthcare. It presents recent research works based on machine learning approaches supported by medical and information communication technologies with the use of data and image analysis. The book presents insight about techniques which broadly deals in delivery of quality, accurate and affordable healthcare solutions by predictive, proactive and preventative methods. The book also explores the possible use of machine learning in enterprises, such as enhanced medical imaging/diagnostics, understanding medical data, drug discovery and development, robotic surgery and automation, radiation treatments, creating electronic smart records and outbreak prediction.

Caracteristici

Focuses on various advanced technologies of machine learning Presents insight about techniques which predictive, proactive, and preventative Serves as a future guide for researchers and practitioners in academia and industry