Amitoj Singh - Böcker

This book divides edge intelligence into AI for edge (intelligence-enabled edge computing) and AI on edge (artificial intelligence on edge). It focuses on providing optimal solutions to the key concerns in edge computing through effective AI technologies, and it discusses how to build AI models, i.e., model training and inference, on edge. This book provides insights into this new inter-disciplinary field of edge computing from a broader vision and perspective. The authors discuss machine learning algorithms for edge computing as well as the future needs and potential of the technology. The authors also explain the core concepts, frameworks, patterns, and research roadmap, which offer the necessary background for potential future research programs in edge intelligence.The target audience of this book includes academics, research scholars, industrial experts, scientists, and postgraduate students who are working in the field of Internet of Things (IoT) or edge computing and would like to add machine learning to enhance the capabilities of their work. This book explores the following topics: Edge computing, hardware for edge computing AI, and edge virtualization techniques Edge intelligence and deep learning applications, training, and optimization Machine learning algorithms used for edge computing Reviews AI on IoT Discusses future edge computing needsAmitoj Singh is an Associate Professor at the School of Sciences of Emerging Technologies, Jagat Guru Nanak Dev Punjab State Open University, Punjab, India.Vinay Kukreja is a Professor at the Chitkara Institute of Engineering and Technology, Chitkara University, Punjab, India.Taghi Javdani Gandomani is an Assistant Professor at Shahrekord University, Shahrekord, Iran.

This book divides edge intelligence into AI for edge (intelligence-enabled edge computing) and AI on edge (artificial intelligence on edge). It focuses on providing optimal solutions to the key concerns in edge computing through effective AI technologies, and it discusses how to build AI models, i.e., model training and inference, on edge. This book provides insights into this new inter-disciplinary field of edge computing from a broader vision and perspective. The authors discuss machine learning algorithms for edge computing as well as the future needs and potential of the technology. The authors also explain the core concepts, frameworks, patterns, and research roadmap, which offer the necessary background for potential future research programs in edge intelligence.The target audience of this book includes academics, research scholars, industrial experts, scientists, and postgraduate students who are working in the field of Internet of Things (IoT) or edge computing and would like to add machine learning to enhance the capabilities of their work. This book explores the following topics: Edge computing, hardware for edge computing AI, and edge virtualization techniques Edge intelligence and deep learning applications, training, and optimization Machine learning algorithms used for edge computing Reviews AI on IoT Discusses future edge computing needsAmitoj Singh is an Associate Professor at the School of Sciences of Emerging Technologies, Jagat Guru Nanak Dev Punjab State Open University, Punjab, India.Vinay Kukreja is a Professor at the Chitkara Institute of Engineering and Technology, Chitkara University, Punjab, India.Taghi Javdani Gandomani is an Assistant Professor at Shahrekord University, Shahrekord, Iran.

This book explores how digital technologies are transforming cultural heritage preservation, documentation, and archiving. It delves into the technical aspects of digitalization techniques, digital preservation strategies, and the use of advanced technologies like virtual reality and augmented reality in the context of cultural heritage.Digital Cultural Heritage: Challenges, Solutions and Future Directions covers the digital documentation and archiving of cultural artifacts, which involves the use of imaging techniques, data storage, and metadata management. This aspect would resonate with engineers specializing in imaging technology, data management, and information systems. The chapters showcase the breadth of innovative ideas in delivering, communicating, interpreting, and transforming cultural heritage content and experience through multi-modal, multimedia interfaces. Aiming to offer a balanced overview of digital heritage and culture issues and technologies, the book pulls together expert views and updates on these four broad areas, namely, (a) Smart Cities and Digital Heritage, (b) Heritage and Education, (c) Culture and Society, and (d) Digital Documentation and Preservation.The book will resonate with engineers specializing in imaging technology, data management, and information systems and those exploring the intersection of digital technology and museums, such as interactive exhibits, digital displays, and virtual museum experiences. It will also be of interest to researchers, curators, and educators as well as the culture-minded public seeking to understand how the burgeoning field of digital heritage and culture may impact our social, cultural, and recreational activities.

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.

This concise book provides a survival toolkit for efficient, large-scale software development.

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.

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.