Introduces edge computing, hardware for edge computing AI, edge virtualization techniques Explores edge intelligence and deep learning applications, training and optimization Explains machine learning algorithms for edge Reviews AI on IoT Discusses future edge computing needs

This book presents an emotion centered research framework titled "emoha" for design innovation. It defines emoha and underlines the importance of the developed framework in culturalization of technology and thereby design innovation. The book explains the detailed research on product styling which leads to the creation of "Emoha" and how to use it in product design.

This concise book provides a survival toolkit for efficient, large-scale software development. Discussing a multi-contextual research framework that aims to harness human-related factors in order to improve flexibility, it includes a carefully selected blend of models, methods, practices, and case studies. To investigate mission-critical communication aspects in system engineering, it also examines diverse, i.e. cross-cultural and multinational, environments. This book helps students better organize their knowledge bases, and presents conceptual frameworks, handy practices and case-based examples of agile development in diverse environments. Together with the authors' previous books, "Crisis Management for Software Development and Knowledge Transfer" (2016) and "Managing Software Crisis: A Smart Way to Enterprise Agility" (2018), it constitutes a comprehensive reference resource adds value to this book.

This concise book provides a survival toolkit for efficient, large-scale software development. Discussing a multi-contextual research framework that aims to harness human-related factors in order to improve flexibility, it includes a carefully selected blend of models, methods, practices, and case studies. To investigate mission-critical communication aspects in system engineering, it also examines diverse, i.e. cross-cultural and multinational, environments. This book helps students better organize their knowledge bases, and presents conceptual frameworks, handy practices and case-based examples of agile development in diverse environments. Together with the authors' previous books, "Crisis Management for Software Development and Knowledge Transfer" (2016) and "Managing Software Crisis: A Smart Way to Enterprise Agility" (2018), it constitutes a comprehensive reference resource adds value to this book.

Business practices are rapidly changing due to technological advances in the workplace. Organizations are challenged to implement new programs for more efficient business while maintaining their standards of excellence and achievement. Achieving Enterprise Agility through Innovative Software Development brings together the necessary methodologies and resources for organizations to understand the challenges and discover the solutions that will enhance their businesses. Including chapters on recent advances in software engineering, this publication will be an essential reference source for researchers, practitioners, students, and professionals in the areas of agile software methodologies, lean development, knowledge engineering, artificial intelligence, cloud computing, software project management, and component-based software engineering.

This book provides insights into how deep learning techniques impact language and speech processing applications. The authors discuss the promise, limits and the new challenges in deep learning. The book covers the major differences between the various applications of deep learning and the classical machine learning techniques. The main objective of the book is to present a comprehensive survey of the major applications and research oriented articles based on deep learning techniques that are focused on natural language and speech signal processing. The book is relevant to academicians, research scholars, industrial experts, scientists and post graduate students working in the field of speech signal and natural language processing and would like to add deep learning to enhance capabilities of their work. Discusses current research challenges and future perspective about how deep learning techniques can be applied to improve NLP and speech processing applications; Presents and escalates the research trends and future direction of language and speech processing; Includes theoretical research, experimental results, and applications of deep learning.