This new volume, Deep Learning in Visual Computing and Signal Processing, covers the fundamentals and advanced topics in designing and deploying techniques using deep architectures and their application in visual computing and signal processing. 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. Key features: Covers both the fundamentals and the latest concepts in deep learning Presents some of the diverse applications of deep learning in visual computing and signal processing Includes over 90 figures and tables to elucidate the text An enlightening amalgamation of deep learning concepts with visual computing and signal processing applications, this valuable resource will serve as a guide for researchers, engineers, and students who want to have a quick start on learning and/or building deep learning systems. It provides a good theoretical and practical understanding and complete information and knowledge required to understand and build deep learning models from scratch.

This new volume discusses the applications and challenges of deep learning and the internet of things for applications in healthcare. It describes deep learning techniques in conjunction with IoT used by practitioners and researchers worldwide. The authors explore the convergence of IoT and deep learning to enable things to communicate, share information, and coordinate decisions. The book includes deep feedforward networks, regularization, optimization algorithms, convolutional networks, sequence modeling, and practical methodology. Chapters look at assistive devices in healthcare, alerting and detection devices, energy efficiency in using IoT, data mining for gathering health information for individuals with autism, IoT for mobile applications, and more. The text also offers mathematical and conceptual background that presents the latest technology as well as a selection of case studies.

Machine Learning and the Internet of Medical Things in Healthcare discusses the applications and challenges of machine learning for healthcare applications. The book provides a platform for presenting machine learning-enabled healthcare techniques and offers a mathematical and conceptual background of the latest technology. It describes machine learning techniques along with the emerging platform of the Internet of Medical Things used by practitioners and researchers worldwide. The book includes deep feed forward networks, regularization, optimization algorithms, convolutional networks, sequence modeling, and practical methodology. It also presents the concepts of the Internet of Things, the set of technologies that develops traditional devices into smart devices. Finally, the book offers research perspectives, covering the convergence of machine learning and IoT. It also presents the application of these technologies in the development of healthcare frameworks.

Field pea (Pisum sativum L. var. arvense.) is amongst the most important legume crop of India, belongs to family Leguminoseae or Fabaceae. Based on triennium ending 2010-11, the contribution of Fieldpeas to total pulses area in India was 3%.In India, productivity of this crop has remained stagnant and area is shrinking due to competition from irrigated wheat, and more remunerative pulses. To increase the production of this crop there is need to operate field pea breeding programme in efficient manner. Selection of parents with the combination of desired genes is the key to success in a hybridization breeding programme. By selection of desirable parents and through hybridization between selected parents, there are chances to break the already existing plateau, and to obtain transgressive segregants in the future generations. Thus the present book deals to identify transgressive segregants, which would mainly depend upon the genetic parameters of various quantitative traits, their mutual associationship and their direct and indirect effects on yield. This book is a useful study material for the researchers in the field of Genetics and Plant Breeding.

Cloud Computing, a technology that uses the internet and central remote servers to maintain data and applications. Cloud computing permits consumers and businesses to use applications without installation and access their personal files at any computer with internet access. This technology permits for much more efficient computing by centralizing storage, memory, processing and bandwidth. The focuses will be on the emerging field of Cloud computing and especially on the middle layer called PaaS . It aims to understand the layer PaaS and implement it, exploiting features such as scalability and accessibility to name a few.