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Epidemic trend analysis, timeline progression, prediction, and
recommendation are critical for initiating effective public health
control strategies, and AI and data analytics play an important
role in epidemiology, diagnostic, and clinical fronts. The focus of
this book is data analytics for COVID-19, which includes an
overview of COVID-19 in terms of epidemic/pandemic, data processing
and knowledge extraction. Data sources, storage and platforms are
discussed along with discussions on data models, their performance,
different big data techniques, tools and technologies. This book
also addresses the challenges in applying analytics to pandemic
scenarios, case studies and control strategies. Aimed at Data
Analysts, Epidemiologists and associated researchers, this book:
discusses challenges of AI model for big data analytics in pandemic
scenarios; explains how different big data analytics techniques can
be implemented; provides a set of recommendations to minimize
infection rate of COVID-19; summarizes various techniques of data
processing and knowledge extraction; enables users to understand
big data analytics techniques required for prediction purposes.
This book presents some of the most advanced leading-edge
technology for the fourth Industrial Revolution -- known as
“Industry 4.0.” The book provides a comprehensive understanding
of the interconnections of AI, IoT, big data and cloud computing as
integral to the technologies that revolutionize the way companies
produce and distribute products and the way local governments
deliver their
services. The book emphasizes that at
every phase of the supply chain, manufactures are found to be
interweaving AI, robotics, IoT, big data/machine learning, and
cloud computing into their production facilities and throughout
their distribution networks. Equally important, the authors show
how their research can be applied to computer vision, cyber
security, database and compiler theory, natural language
processing, healthcare, education and agriculture. Presents the
fundamentals of AI, IoT, and cloud computing and how they can be
incorporated in Industry 4.0 applications Motivates readers to
address challenges in the areas of speech communication and signal
processing Provides numerous examples, case studies, technical
descriptions, and approaches of AI/ML
Covers IoT-cloud convergence with a focus on complex industrial IoT
case studies Discusses the broad background of IoT-cloud
convergence architectures and their fundamentals along with
resource provisioning mechanisms Emphasizes the use of context in
developing context aware IoT solutions Presents a novel C-model
that explains the IoT application development phases Discusses a
simplified convergence model that depicts the role of cloud in an
IoT application
This book is one-stop shop which offers essential information one
must know and can implement in real-time business expansions to
solve engineering problems in various disciplines. It will also
help us to make future predictions and decisions using AI
algorithms for engineering problems. Machine learning and
optimizing techniques provide strong insights into novice users. In
the era of big data, there is a need to deal with data science
problems in multidisciplinary perspective. In the real world, data
comes from various use cases, and there is a need of source
specific data science models. Information is drawn from various
platforms, channels, and sectors including web-based media, online
business locales, medical services studies, and Internet. To
understand the trends in the market, data science can take us
through various scenarios. It takes help of artificial intelligence
and machine learning techniques to design and optimize the
algorithms. Big data modelling and visualization techniques of
collected data play a vital role in the field of data science. This
book targets the researchers from areas of artificial intelligence,
machine learning, data science and big data analytics to look for
new techniques in business analytics and applications of artificial
intelligence in recent businesses.
This book is one-stop shop which offers essential information one
must know and can implement in real-time business expansions to
solve engineering problems in various disciplines. It will also
help us to make future predictions and decisions using AI
algorithms for engineering problems. Machine learning and
optimizing techniques provide strong insights into novice users. In
the era of big data, there is a need to deal with data science
problems in multidisciplinary perspective. In the real world, data
comes from various use cases, and there is a need of source
specific data science models. Information is drawn from various
platforms, channels, and sectors including web-based media, online
business locales, medical services studies, and Internet. To
understand the trends in the market, data science can take us
through various scenarios. It takes help of artificial intelligence
and machine learning techniques to design and optimize the
algorithms. Big data modelling and visualization techniques of
collected data play a vital role in the field of data science. This
book targets the researchers from areas of artificial intelligence,
machine learning, data science and big data analytics to look for
new techniques in business analytics and applications of artificial
intelligence in recent businesses.
This book discusses the use of converged technology, a rapidly
growing area that enhancements smart devices, communication,
Internet of things (IoT), and augmented reality (AR). The book also
explores the need for convergence of IoT and AR for various
purposes, like personalized services, context awareness, and
bridging the gap between the physical and digital world.
Furthermore, it examines the implementation of IoT and AR in use
cases to define pathways that allow application developers to
design modern solutions to satisfy requirements like scalability,
abstraction and security. Featuring an introduction, and covering
sensing techniques, and effective architecture in AR-based IoT
real-time use cases, the book also addresses the issues and
challenges in designing standard architecture and middleware to
support diverse applications. Given its scope, it is a valuable
resource for teachers and students in engineering, as well as
researchers, developers, and users working in multi-disciplinary
areas.
This book is focused on the impact of the COVID-19 pandemic on
different sectors, i.e., education, real estate, health, and
agriculture. The lockdown has been announced to control the spread
of COVID-19 infections, however people/industries/organizations
were not ready for lockdown and it has greatly affected their
growth. The front workers in the healthcare sector suffered a lot
as major responsibilities they needed to carry on. The education
sector is also hampered due to the pandemic as schools, colleges
were closed and teaching, examinations were carried out on online
platforms. These platforms were new to teachers as well as
students. The real estate sector faced tremendous loss in this
pandemic as people were scared and no one ready to invest their
money in such an uncertain time. The agriculture filed is also
suffered as raw materials required for agriculture were not
available readily due to pandemic. This book presents a survey that
is conducted to understand the impact of COVID-19 on the quality of
work-life in various sectors. The survey is focused majorly on four
sectors, i.e. education, healthcare, real estate and agriculture.
Data analysis is done based on responses of survey and mathematical
modeling is provided for each case study.
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