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This book is a compilation of peer-reviewed papers presented at the
International Conference on Machine Intelligence and Data Science
Applications, organized by the School of Computer Science,
University of Petroleum & Energy Studies, Dehradun, on
September 4 and 5, 2020. The book starts by addressing the
algorithmic aspect of machine intelligence which includes the
framework and optimization of various states of algorithms. Variety
of papers related to wide applications in various fields like image
processing, natural language processing, computer vision, sentiment
analysis, and speech and gesture analysis have been included with
upfront details. The book concludes with interdisciplinary
applications like legal, health care, smart society, cyber physical
system and smart agriculture. The book is a good reference for
computer science engineers, lecturers/researchers in machine
intelligence discipline and engineering graduates.
This book is a compilation of peer-reviewed papers presented at the
International Conference on Machine Intelligence and Data Science
Applications, organized by the School of Computer Science,
University of Petroleum & Energy Studies, Dehradun, India,
during 4-5 September 2020. The book addresses the algorithmic
aspect of machine intelligence which includes the framework and
optimization of various states of algorithms. Variety of papers
related to wide applications in various fields like data-driven
industrial IoT, bioinformatics, network and security, autonomous
computing and various other aligned areas. The book concludes with
interdisciplinary applications like legal, health care, smart
society, cyber-physical system and smart agriculture. All papers
have been carefully reviewed. The book is of interest to computer
science engineers, lecturers/researchers in machine intelligence
discipline and engineering graduates.
This book is a compilation of peer-reviewed papers presented at the
International Conference on Machine Intelligence and Data Science
Applications, organized by the School of Computer Science,
University of Petroleum & Energy Studies, Dehradun, on
September 4 and 5, 2020. The book starts by addressing the
algorithmic aspect of machine intelligence which includes the
framework and optimization of various states of algorithms. Variety
of papers related to wide applications in various fields like image
processing, natural language processing, computer vision, sentiment
analysis, and speech and gesture analysis have been included with
upfront details. The book concludes with interdisciplinary
applications like legal, health care, smart society, cyber physical
system and smart agriculture. The book is a good reference for
computer science engineers, lecturers/researchers in machine
intelligence discipline and engineering graduates.
This book is a compilation of peer-reviewed papers presented at the
International Conference on Machine Intelligence and Data Science
Applications, organized by the School of Computer Science,
University of Petroleum & Energy Studies, Dehradun, India,
during 4-5 September 2020. The book addresses the algorithmic
aspect of machine intelligence which includes the framework and
optimization of various states of algorithms. Variety of papers
related to wide applications in various fields like data-driven
industrial IoT, bioinformatics, network and security, autonomous
computing and various other aligned areas. The book concludes with
interdisciplinary applications like legal, health care, smart
society, cyber-physical system and smart agriculture. All papers
have been carefully reviewed. The book is of interest to computer
science engineers, lecturers/researchers in machine intelligence
discipline and engineering graduates.
This book provides a conceptual 'Flexibility in Resource
Management' framework supported by research/case applications in
various related areas. It links and integrates the flexibility
aspect with resource management to offer a fresh perspective, since
flexibility in different levels of resource management is emerging
as a key concern -- a business enterprise needs to have reactive
flexibility (as adaptiveness and responsiveness) to cope with the
changing and uncertain business environment. It may also endeavor
to intentionally create flexibility by way of leadership change,
re-engineering, innovation in products and processes, use of
information and communication technology, and so on. The selected
papers discussing a variety of issues concerning flexibility in
resource management, are organized into following four parts:
flexibility and innovation; flexibility in organizational
management; operations and technology management; and financial and
risk management. In addition to addressing the organizational needs
of corporate bodies spread across the globe, the book serves as a
useful reference resource for a variety of audiences including
management students, researchers, business managers, consultants
and professional institutes.
This book provides a conceptual 'Flexibility in Resource
Management' framework supported by research/case applications in
various related areas. It links and integrates the flexibility
aspect with resource management to offer a fresh perspective, since
flexibility in different levels of resource management is emerging
as a key concern -- a business enterprise needs to have reactive
flexibility (as adaptiveness and responsiveness) to cope with the
changing and uncertain business environment. It may also endeavor
to intentionally create flexibility by way of leadership change,
re-engineering, innovation in products and processes, use of
information and communication technology, and so on. The selected
papers discussing a variety of issues concerning flexibility in
resource management, are organized into following four parts:
flexibility and innovation; flexibility in organizational
management; operations and technology management; and financial and
risk management. In addition to addressing the organizational needs
of corporate bodies spread across the globe, the book serves as a
useful reference resource for a variety of audiences including
management students, researchers, business managers, consultants
and professional institutes.
This book provides insights into deep learning techniques that
impact the implementation strategies toward achieving the
Sustainable Development Goals (SDGs) laid down by the United
Nations for its 2030 agenda, elaborating on the promises, limits,
and the new challenges. It also covers the challenges, hurdles, and
opportunities in various applications of deep learning for the
SDGs. A comprehensive survey on the major applications and
research, based on deep learning techniques focused on SDGs through
speech and image processing, IoT, security, AR-VR, formal methods,
and blockchain, is a feature of this book. In particular, there is
a need to extend research into deep learning and its broader
application to many sectors and to assess its impact on achieving
the SDGs. The chapters in this book help in finding the use of deep
learning across all sections of SDGs. The rapid development of deep
learning needs to be supported by the organizational insight and
oversight necessary for AI-based technologies in general; hence,
this book presents and discusses the implications of how deep
learning enables the delivery agenda for sustainable development.
This book presents a framework for developing an analytics strategy
that includes a range of activities, from problem definition and
data collection to data warehousing, analysis, and decision making.
The authors examine best practices in team analytics strategies
such as player evaluation, game strategy, and training and
performance. They also explore the way in which organizations can
use analytics to drive additional revenue and operate more
efficiently. The authors provide keys to building and organizing a
decision intelligence analytics that delivers insights into all
parts of an organization. The book examines the criteria and tools
for evaluating and selecting decision intelligence analytics
technologies and the applicability of strategies for fostering a
culture that prioritizes data-driven decision making. Each chapter
is carefully segmented to enable the reader to gain knowledge in
business intelligence, decision making and artificial intelligence
in a strategic management context.
Globally, concerns for the environment and human well-being have
increased as results of threats imposed by climate change and
disasters, environmental degradation, pollution of natural
resources, water scarcity and proliferation of slums. Finding
appropriate solutions to these threats and challenges is not
simple, as these are generally complex and require state-of-the-art
technology to collect, measure, handle and analyse large volumes of
varying data sets. However, the recent advances in sensor
technology, coupled with the rapid development of computational
power, have greatly enhanced our abilities to capture, store and
analyse the surrounding physical environment. This book explores
diverse dimensions of geo-intelligence (GI) technology in
developing a computing framework for location-based,
data-integrating earth observation and predictive modelling to
address these issues at all levels and scales. The book provides
insight into the applications of GI technology in several fields of
spatial and social sciences and attempts to bridge the gap between
them.
This book provides an understanding of the evolution of
digitization in our day to day life and how it has become a part of
our social system. The obvious challenges faced during this process
and how these challenges were overcome have been discussed. The
discussions revolve around the solutions to these challenges by
leveraging the use of various advanced technologies. The book
mainly covers the use of these technologies in variety of areas
such as smart cities, healthcare informatics, transportation
automation, digital transformation of education. The book intends
to be treated as a source to provide the systematic discussion to
the bouquet of areas that are essential part of digitized
societies. In light of this, the book accommodates theoretical,
methodological, well-established, and validated empirical work
dealing with various related topics.
This book is a compilation of peer reviewed papers presented at
International Conference on Machine Intelligence and Data Science
Applications (MIDAS 2021), held in Comilla University, Cumilla,
Bangladesh during 26 - 27 December 2021. The book covers
applications in various fields like image processing, natural
language processing, computer vision, sentiment analysis, speech
and gesture analysis, etc. It also includes interdisciplinary
applications like legal, healthcare, smart society, cyber physical
system and smart agriculture, etc. The book is a good reference for
computer science engineers, lecturers/researchers in machine
intelligence discipline and engineering graduates.
This book presents a framework for developing an analytics strategy
that includes a range of activities, from problem definition and
data collection to data warehousing, analysis, and decision making.
The authors examine best practices in team analytics strategies
such as player evaluation, game strategy, and training and
performance. They also explore the way in which organizations can
use analytics to drive additional revenue and operate more
efficiently. The authors provide keys to building and organizing a
decision intelligence analytics that delivers insights into all
parts of an organization. The book examines the criteria and tools
for evaluating and selecting decision intelligence analytics
technologies and the applicability of strategies for fostering a
culture that prioritizes data-driven decision making. Each chapter
is carefully segmented to enable the reader to gain knowledge in
business intelligence, decision making and artificial intelligence
in a strategic management context.
A fundamental requirement of Agenda 21 of UNCED is to support
sustainable development while safeguarding the Earth's environment.
This requires optimal management of natural resources which depends
on the availability of reliable and timely information at the
global, national, regional and local scales. One such technology,
"Geoinformatics", consisting of Remote Sensing (RS), Geographical
Information System (GIS), and Global Positioning System (GPS) is
source of reliable and timely information needed for natural
resource management, environmental protection and addressing issues
related to sustainable development. It offers a powerful tool for
resource assessment, mapping, monitoring, modelling, management
etc. It is also capable to make use of recent developments in the
digital integration of human reasoning, data and dynamic models.
These tools have been available for past three decades. Many
institutions and organisations are carrying out various research
and operational applications of direct relevance particular to
natural resource management. However, there are still limitations
in understanding the underlying science and research elements, as
there are larger questions of capacity building to use
geoinformatics in natural resource management and associated
sustainable development applications. These programs also find gaps
between the theoretical concepts and the operational utilisation of
these tools. This could be solved by providing wide range of
applications and prospective potential of this technology to the
students and research community in this area. "Geoinformatics for
Natural Resource Management" contains chapters written by noted
researchers and experts. The focus emerged with filling a gap in
the available literature on the subject by bringing together the
concepts, theories and experiences of the experts in this field.
Globally, concerns for the environment and human well-being have
increased as results of threats imposed by climate change and
disasters, environmental degradation, pollution of natural
resources, water scarcity and proliferation of slums. Finding
appropriate solutions to these threats and challenges is not
simple, as these are generally complex and require state-of-the-art
technology to collect, measure, handle and analyse large volumes of
varying data sets. However, the recent advances in sensor
technology, coupled with the rapid development of computational
power, have greatly enhanced our abilities to capture, store and
analyse the surrounding physical environment. This book explores
diverse dimensions of geo-intelligence (GI) technology in
developing a computing framework for location-based,
data-integrating earth observation and predictive modelling to
address these issues at all levels and scales. The book provides
insight into the applications of GI technology in several fields of
spatial and social sciences and attempts to bridge the gap between
them.
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