DEEP LEARNING MACHINE LEARNING AN Techniques and Applications

Biomedical and Health Informatics is an important field that brings tremendous opportunities and helps address challenges due to an abundance of available biomedical data. This book examines and demonstrates state-of-the-art approaches for IOT and Machine Learning based biomedical and health related applications.

About the author

Sujata Dash is an Associate Professor at P.G. Department of Computer Science & Application, North Orissa University, at Baripada, India.
Subhendu Kumar Pani is a Professor in the Department of Computer Science Engineering and also Research coordinator at Orissa Engineering College (OEC) Bhubaneswar.
Joel J. P. C. Rodrigues is a Professor at the Federal University of Piauí, Brazil; and senior researcher at the Instituto de Telecomunicações, Portugal.
Babita Majhi is an Assistant Professor in the department of Computer Science and Information Technology, Guru Ghasidas Vishwavidyalaya, Central University, Bilaspur, India.

Summary

Biomedical and Health Informatics is an important field that brings tremendous opportunities and helps address challenges due to an abundance of available biomedical data. This book examines and demonstrates state-of-the-art approaches for IOT and Machine Learning based biomedical and health related applications.

Product details

• Assisted by: Sujata Dash (Editor), Subhendu Kumar Pani (Editor), Joel J. P. C. Rodrigues (Editor), Babita Majhi (Editor), Dash Sujata (Editor), Rodrigues Joel J. P. C. (Editor)
• Authors: Sujata (North Orissa University Dash
• Publisher: Taylor & Francis Ltd.
• Content: Book
• Product form: Paperback / Softback
• Publication date: 29.07.2024
• Subject: Natural sciences, medicine, IT, technology > Biology > Ecology; Guides
• EAN: 9780367548469
• ISBN: 978-0-367-54846-9
• Pages: 362
• Series: Biomedical Engineering
• Subjects: machine learning, MATHEMATICS / Probability & Statistics / General, MEDICAL / Health Care Delivery, Edge Computing, SCIENCE / Biotechnology, TECHNOLOGY & ENGINEERING / Biomedical, COMPUTERS / Machine Theory, Support Vector Machines, biotechnology, Health Informatics, Probability & statistics, Mathematical theory of computation, Biomedical engineering, Health systems & services, Probability and statistics, COMPUTERS / Data Science / Machine Learning, Primary care medicine, primary health care, MEDICAL / Nursing / Care Plans, polynomial kernel, medical image analysis, Clinical decision support, Data Set, EEG signal, smart phones, disease prediction algorithms, UCI Machine Learn Repository, DBN, neural network models, SVM Classifier, CNN Model, SVM Model, computational biomedicine, advanced biomedical signal analysis, healthcare data processing, Ml Algorithm, SPECT Image, KNN Model, Deep Boltzmann Machine, Sparse Auto-encoders, Deep RL, RF., Adaptive Median Filter, Alzheimer’s Disease Neuroimaging Initiative, Classification Technique Support Vector Machine, Chinese Longitudinal Healthy Longevity Survey, Tear Film Breakup Time