
Deep Learning in Visual Computing and Signal Processing
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The volume first lays out the fundamentals of deep learning as well as deep learning architectures and frameworks. It goes on to discuss deep learning in neural networks and deep learning for object recognition and detection models. It looks at the various specific applications of deep learning in visual and signal processing, such as in biorobotics, for automated brain tumor segmentation in MRI images, in neural networks for use in seizure classification, for digital forensic investigation based on deep learning, and more.
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Vibhav Kumar Sachan, PhD, is Professor and Additional Head of the Department of Electronics and Communication Engineering Department at the KIET Group of Institutions, India. During his academic career of 18 years, he has taught at undergraduate and postgraduate levels and has authored books, edited several conference proceedings, and written book chapters. He has published many papers in reputed national and international journals and conferences and is an editorial board member of several journals.
Akansha Singh, PhD, is Associate Professor in the School of Computer Science Engineering and Technology, Bennett University, India. She has to her credit more than 70 research papers, 20 books, and numerous conference papers. Dr. Singh has served as a reviewer and technical committee member for multiple conferences and journals and is also an associate editor and guest editor for several journals in her field.
Sanjeevikumar Padmanaban, PhD, is a senior member of IEEE and a faculty member with the Department of Energy Technology, Aalborg University, Esbjerg, Denmark. He is also affiliated with CTIF Global Capsule, Department of Business Development and Technology Aarhus University, Denmark. He was formerly affiliated with VIT University, India; the National Institute of Technology, India; Qatar University, Doha, Qatar; Dublin Institute of Technology, Ireland; and the University of Johannesburg, South Africa.
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