Machine Intelligence and Signal Analysis, 1st ed. 2019 Advances in Intelligent Systems and Computing Series, Vol. 748

The book covers the most recent developments in machine learning, signal analysis, and their applications. It covers the topics of machine intelligence such as: deep learning, soft computing approaches, support vector machines (SVMs), least square SVMs (LSSVMs) and their variants; and covers the topics of signal analysis such as: biomedical signals including electroencephalogram (EEG), magnetoencephalography (MEG), electrocardiogram (ECG) and electromyogram (EMG) as well as other signals such as speech signals, communication signals, vibration signals, image, and video. Further, it analyzes normal and abnormal categories of real-world signals, for example normal and epileptic EEG signals using numerous classification techniques. The book is envisioned for researchers and graduate students in Computer Science and Engineering, Electrical Engineering, Applied Mathematics, and Biomedical Signal Processing.

M. Tanveer is working as Assistant Professor and Ramanujan Fellow at Discipline of Mathematics, Indian Institute of Technology Indore, India. Prior to that, he worked as Postdoctoral Research Fellow at Rolls-Royce@NTU Corporate Lab, Nanyang Technological University (NTU), Singapore. He served as Assistant Professor at Department of Computer Science and Engineering, LNM Institute of Information Technology (LNMIIT), Jaipur, India. He received his Ph.D. degree in Computer Science from the Jawaharlal Nehru University, New Delhi, India, and his M.Phil. degree in Mathematics from Aligarh Muslim University, Aligarh, India. His research interests include support vector machines, optimization, applications to Alzheimer’s disease and dementias, biomedical signal processing, and fixed-point theory and applications. He has been awarded competitive research funding by various prestigious agencies such as Department of Science & Technology (DST), Council of Scientific and Industrial Research (CSIR) and Science & Engineering Research Board (SERB). He is the recipient of 2017 SERB Early Career Research Award in Engineering Sciences and the only recipient of 2016 prestigious DST-SERB Ramanujan Fellowship in Mathematical Sciences. He is a member of the editorial review board of Applied Intelligence, Springer (International Journal of Artificial Intelligence, Neural Networks, and Complex Problem-Solving Technologies). He has published over 24 papers in reputed international journals.

Dr. Ram Bilas Pachori received B.E. degree with honors in Electronics and Communication Engineering from Rajiv Gandhi Proudyogiki Vishwavidyalaya, Bhopal, India in 2001, M.Tech. and Ph.D. degrees in Electrical Engineering from Indian Institute of Technology (IIT) Kanpur, India in 2003 and 2008 respectively. He worked as Postdoctoral Fellow at Charles Delaunay Institute, University of Technology of Troyes, France during 2007-2008. He served as Assistant Professor at Communication Research

Presents the latest advances in the area of data mining, artificial intelligence, optimization, machine learning methods and algorithms

Discusses applications for studying human brain and heart disorders like epilepsy, Alzheimer’s, and coronary artery disease

Serves as a valuable reference resource for future work