This book is devoted to the issues faced by universities in the field of distance learning during and after covid as well as in digitalization times. The book devotes a lot of space to the issues of Web 3.0 in University e-learning, industry 4.0, artificial intelligence and digital equity. The aim and scope of this book is to draw a holistic picture of education before and after covid-19, the psychological effects of covid in education, using modern technologies application in education with taking into consideration sustainability development, industry 4.0 and society 5.0 aspects. The authors also raised the issue of artifical intelligence investigation in learner-instructor interaction. Highlights: To elaborate the functions of online education and numerous pedagogical strategies based on electronic learning to aid teachers and students with the tools required to succeed in the 21st century via engaging virtual experiences; To analyse tools provided by Ed-Tech firms and the effect of digital tools on maintaining the educational process in times of crisis and after pandemic; To create a roadmap for higher education institutions and provide tips regarding how to improve the effectiveness of the hybrid learning system; To understand e-learning characteristic in the era of industry 4.0 and society 5.0 and characteristics of the different web generations; And, using AI applications to improve connections and relationships between students and teachers and in education in the future. The book is both scientific and educational. It can be used at the university and by anyone interested in the topics it covers.

Electronic device usage has increased considerably in the past two decades. System configurations are continuously requiring upgrades; existing systems often become obsolete in a matter of 2–3 years. Green computing is the complete effective management of design, manufacture, use, and disposal, involving as little environmental impact as possible. This book intends to explore new and innovative ways of conserving energy, effective e-waste management, and renewable energy sources to harness and nurture a sustainable eco-friendly environment. This book: • Highlights innovative principles and practices using effective e-waste management and disposal • Explores artificial intelligence based sustainable models • Discovers alternative sources and mechanisms for minimizing environmental hazards • Highlights successful case studies in alternative sources of energy • Presents solid illustrations, mathematical equations, as well as practical in-the-field applications • Serves as a one-stop reference guide to stakeholders in the domain of green computing, e-waste management, renewable energy alternatives, green transformational leadership including theory concepts, practice and case studies • Explores cutting-edge technologies like internet of energy and artificial intelligence, especially the role of machine learning and deep learning in renewable energy and creating a sustainable ecosystem • Explores futuristic trends in renewable energy This book aims to address the increasing interest in reducing the environmental impact of energy as well as its further development and will act as a useful reference for engineers, architects, and technicians interested in and working with energy systems; scientists and engineers in developing countries; industries, manufacturers, inventors, universities, researchers, and interested consultants to explain the foundation to advanced concepts and research trends in the domain of renewable energy and sustainable computing. The content coverage of the book is organized in the form of 11 clear and thorough chapters providing a comprehensive view of the global renewable energy scenario, as well as how science and technology can play a vital role in renewable energy.

The book Advances in Distance Learning in Times of Pandemic is devoted to the issues and challenges faced by universities in the field of distance learning in COVID-19 times. It covers both the theoretical and practical aspects connected to distance education. It elaborates on issues regarding distance learning, its challenges, assessment by students and their expectations, the use of tools to improve distance learning, and the functioning of e-learning in the industry 4.0 and society 5.0 eras. The book also devotes a lot of space to the issues of Web 3.0 in university e-learning, quality assurance, and knowledge management. The aim and scope of this book is to draw a holistic picture of ongoing online teaching-activities before and during the lockdown period and present the meaning and future of e-learning from students’ points of view, taking into consideration their attitudes and expectations as well as industry 4.0 and society 5.0 aspects. The book presents the approach to distance learning and how it has changed, especially during a pandemic that revolutionized education. It highlights • the function of online education and how that has changed before and during the pandemic. • how e-learning is beneficial in promoting digital citizenship. • distance learning characteristic in the era of industry 4.0 and society 5.0. • how the era of industry 4.0 treats distance learning as a desirable form of education. The book covers both scientific and educational aspects and can be useful for university-level undergraduate, postgraduate and research-grade courses and can be referred to by anyone interested in exploring the diverse aspects of distance learning.

Convolutional neural networks (CNNs), a type of deep neural network that has become dominant in a variety of computer vision tasks, in recent few years has attracted interest across a variety of domains due to their high efficiency at extracting meaningful information from visual imagery. Convolutional neural networks (CNNs) excel at a wide range of machine learning and deep learning tasks. As sensor-enabled internet of things (IoT) devices pervade every aspect of modern life, it is becoming increasingly critical to run CNN inference, a computationally intensive application, on resource-constrained devices. Through this edited volume we aim to provide a structured presentation of CNN enabled IoT applications in vision, speech, and natural language processing. This book discusses a variety of CNN techniques and applications, including but not limited to, IoT enabled CNN for speech de-noising, a smart app for visually impaired people, disease detection, ECG signal analysis, weather monitoring, texture analysis, etc. Unlike other books on the market, this book covers the tools, techniques, and challenges associated with the implementation of CNN algorithms, computation time, and the complexity associated with reasoning and modelling various types of data. We have included CNN's current research trends and future directions.

- The book will provide a foundation for researcher, scientists and educationists towards the application of advanced technologies such as IoT, AI and block chain in the area of Supply chain Management to track assets accurately and for upgrading supply chain business operations. - It will provide a multidimensional view for the masses ranging from computer science, food industry, hotel management, apparel, medical, inventory management and agriculture domain. It can be used as a reference book for higher education UG and PG students, as well as research scholars.

XAI Based Intelligent Systems for Society 5.0 focuses on the development and analysis of Explainable Artificial Intelligence (XAI)-based models and intelligent systems that can be utilized for Society 5.0—characterized by a knowledge intensive, data driven, and non-monetary society. The book delves into the issues of transparency, explainability, data fusion, and interpretability, which are significant for the development of a super smart society and are addressed through XAI-based models and techniques. XAI-based deep learning models, fuzzy and hybrid intelligent systems, expert systems, and intrinsic explainable models in the context of Society 5.0 are presented in detail.The book also addresses—using XAI-based intelligent techniques—the privacy issues intrinsic in storing huge amounts of data or information in virtual space. The concept of Responsible AI, which is at the core of the future direction of XAI for Society 5.0, is also explored in this book. Finally, the application areas of XAI, including relevant case studies, are presented in the concluding chapter. This book serves as a valuable resource for graduate/post graduate students, academicians, analysts, computer scientists, engineers, researchers, professionals, and other personnel working in the area of artificial intelligence, machine learning, and intelligent systems, who are interested in creating a people-centric smart society.

This book discovers what it will take to reindustrialize the previous industrial powerhouses in order to offset the advantages of cheap labor suppliers dominating the industrial sector by exploring the current situation of the production, processing, and manufacturing industries. The Internet of Things (IoT), Big Data, Cyber-Physical Systems (CPS), and Cloud Computing, Cyber Security, Cobotics, Automation, AI, 3D Printing and Additive Manufacturing, SDN, Blockchain technologies are outlined in this unique and comprehensive book, which has true potential for professionals, researchers, policymakers, and book users. New Horizons for Industry 4.0 in Modern Business encompass trends in business and technology globally that may completely alter how manufacturing and production are conducted. What you will discover: Learn about the Industrial Internet of Things and the Industrial Internet. Learn about the technologies that must develop to support Industry 4.0 and what is being done right now to make that happen. In this book, the topic of Industry 4.0 is covered in detail, and it even moves on to concepts of Digital Twins to boost output and create Industrial Internet of Things. With the development of new digital industrial technology, or "Industry 4.0," it is now feasible to collect and analyze data from many machines, resulting in processes that are quicker, more adaptable, and more efficient, producing things of higher quality while spending less money. The manufacturing revolution will boost productivity, alter economics, promote industrial development, and alter workforce demographics, ultimately altering the competitiveness of businesses and areas. Although advanced digital technology is being employed in manufacturing, Industry 4.0 will completely change how things are done. Greater production efficiencies will result, and conventional connections between suppliers, manufacturers, and consumers-as well as between people and machines-will shift. Industry 4.0 is changing the business process. This disruptive technology is radically changing the way businesses/manufacturing is conducted. It will give machines that little bit of intuition with the help of robotics, 3D printing, artificial intelligence, augmented reality, and virtual reality-that will help them do mindless and repetitive jobs without human intervention, allowing humans to focus more on their core competencies.

Generative Adversarial Networks (GAN) have started a revolution in Deep Learning, and today GAN is one of the most researched topics in Artificial Intelligence. Generative Adversarial Networks for Image-to-Image Translation provides a comprehensive overview of the GAN (Generative Adversarial Network) concept starting from the original GAN network to various GAN-based systems such as Deep Convolutional GANs (DCGANs), Conditional GANs (cGANs), StackGAN, Wasserstein GANs (WGAN), cyclical GANs, and many more. The book also provides readers with detailed real-world applications and common projects built using the GAN system with respective Python code. A typical GAN system consists of two neural networks, i.e., generator and discriminator. Both of these networks contest with each other, similar to game theory. The generator is responsible for generating quality images that should resemble ground truth, and the discriminator is accountable for identifying whether the generated image is a real image or a fake image generated by the generator. Being one of the unsupervised learning-based architectures, GAN is a preferred method in cases where labeled data is not available. GAN can generate high-quality images, images of human faces developed from several sketches, convert images from one domain to another, enhance images, combine an image with the style of another image, change the appearance of a human face image to show the effects in the progression of aging, generate images from text, and many more applications. GAN is helpful in generating output very close to the output generated by humans in a fraction of second, and it can efficiently produce high-quality music, speech, and images.