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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.
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