Artificial Intelligence for Information Management: A Healthcare Perspective

This book discusses the advancements in artificial intelligent techniques used in the well-being of human healthcare. It details the techniques used in collection, storage and analysis of data and their usage in different healthcare solutions. It also discusses the techniques of predictive analysis in early diagnosis of critical diseases. The edited book is divided into four parts – part A discusses introduction to artificial intelligence and machine learning in healthcare; part B highlights different analytical techniques used in healthcare; part C provides various security and privacy mechanisms used in healthcare; and finally, part D exemplifies different tools used in visualization and data analytics.

• Healthcare
• Artificial Intelligence in Healthcare
• Machine Learning in Healthcare
• Data Analytics in Healthcare
• Data Visualization
• Health Informatics

• Department of CSE, National Institute of Technical Teachers Training and Research, Chandigarh, India

  K. G. Srinivasa

• Department of ISE, Ramaiah Institute of Technology (MSRIT), Bangalore, India

  Siddesh G. M., S. R. Mani Sekhar

Dr. Srinivasa K G was awarded a Ph.D. in Computer Science and Engineering from Bangalore University in 2007. He has received various awards, including the All India Council for Technical Education – Career Award for Young Teachers; Indian Society of Technical Education – ISGITS National Award for Best Research Work Done by Young Teachers; Institution of Engineers (India) – IEI Young Engineer Award in Computer Engineering; the ISTE’s Rajarambapu Patil National Award for Promising Engineering Teachers in 2012; and a Visiting Scientist Fellowship Award from IMS Singapore. He has published more than 100 research papers in international journals and conferences and has authored three textbooks: File Structures using C++, Soft Computing for Data Mining Applications and Guide to High Performance Computing. He has also edited research books in the area of cyber-physical systems and energy-aware computing. He has been awarded a BOYSCAST Fellowship by the DST to conduct collaborative researchwith the Clouds Laboratory at the University of Melbourne. He is Principal Investigator for several AICTE, UGC, DRDO and DST funded projects. He is a senior member of IEEE and ACM. His research areas include data mining, machine learning and cloud computing. 

Dr. Siddesh G M is currently working as Associate Professor in the Department of Information Science & Engineering, M S Ramaiah Institute of Technology, Bangalore. He is the recipient of Seed Money to Young Scientist for Research (SMYSR) for FY 2014-15, from Government of Karnataka, Vision Group on Science and Technology (VGST). He has published a good number of research papers in reputed international conferences and journals. He is a member of ISTE, IETE, etc. He has authored books on Network Data Analytics, Statistical Modelling and Machine Learning Principles for Bioinformatics Techniques, Tools, and Applications, Statistical Programming in R, Internet of Things with Springer, Oxford University Press and Cengagepublishers, respectively. He has edited research monographs in the area of cyber-physical systems, fog computing and energy aware computing, bioinformatics with CRC Press, IGI Global and Springer publishers, respectively. His research interests include Internet of Things, distributed computing and data analytics.

Dr. S. R. Mani Sekhar is currently working as an Assistant Professor at the Department of Information Science & Engineering, Ramaiah Institute of Technology, Bangalore. He is a member of ISTE. He has published a good number of research papers and book chapters. He has authored a book titled “Programming with R”, Cengage publisher. He has also edited a book titled “Statistical Modelling and Machine Learning Principles for Bioinformatics Techniques, Tools, and Applications”, Springer. He is also an Associate Editor for International Journal of End-User Computing and Development. His research interests include bioinformatics, data science, data analytics and software engineering.

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