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Global inequality has been a burning issue for years now. As the
economies progress, it is expected that the benefits of growth will
percolate to the lower sections of society. However, this
percolation takes place in a discriminating manner. Inequality can
be observed in terms of health, income, education, wealth, gender,
availability of opportunities, and other socio-economic parameters.
The governing authorities and international agencies have been
taking various corrective measures to reduce the widening levels of
inequality. However, certain external factors like the pandemic can
wash away the efforts taken and deteriorate the progress made on
the inequality levels in economies. Emerging Trends and Insights on
Economic Inequality in the Wake of Global Crises discusses the
impact of global disasters and crises on economic inequality. It
provides an overview of the evolution of global inequality over the
years, increasing different forms of inequalities amidst crises,
the corrective measures taken by the national and international
agencies, and the way forward for economies with worsening
inequalities. Covering topics such as crisis management, digital
agriculture, and economic welfare, this premier reference source is
an essential resource for economists, business leaders and
executives, government officials, students and educators of higher
education, sociologists, researchers, and academicians.
Processing information and analyzing data efficiently and
effectively is crucial for any company that wishes to stay
competitive in its respective market. Nonlinear data presents new
challenges to organizations, however, due to its complexity and
unpredictability. The only technology that can properly handle this
form of data is artificial neural networks. These modeling systems
present a high level of benefits in analyzing complex data in a
proficient manner, yet considerable research on the specific
applications of these intelligent components is significantly
deficient. Applications of Artificial Neural Networks for Nonlinear
Data is a collection of innovative research on the contemporary
nature of artificial neural networks and their specific
implementations within data analysis. While highlighting topics
including propagation functions, optimization techniques, and
learning methodologies, this book is ideally designed for
researchers, statisticians, academicians, developers, scientists,
practitioners, students, and educators seeking current research on
the use of artificial neural networks in diagnosing and solving
nonparametric problems.
Decision-making is a frequent problem in today's financial,
business, and industrial world. Thus, fuzzy expert systems are
increasingly being used to solve decision-making problems by
attempting to solve a part or whole of a practical problem. These
expert systems have proven that they can solve problems in various
domains where human expertise is required, including the field of
agriculture. Fuzzy Expert Systems and Applications in Agricultural
Diagnosis is a crucial source that examines the use of fuzzy expert
systems for prediction and problem solving in the agricultural
industry. Featuring research on topics such as nutrition
management, sustainable agriculture, and defuzzification, this book
is ideally designed for farmers, researchers, scientists,
academics, students, policymakers, and development practitioners
seeking the latest research in technological tools that support
crop disease diagnosis.
Processing information and analyzing data efficiently and
effectively is crucial for any company that wishes to stay
competitive in its respective market. Nonlinear data presents new
challenges to organizations, however, due to its complexity and
unpredictability. The only technology that can properly handle this
form of data is artificial neural networks. These modeling systems
present a high level of benefits in analyzing complex data in a
proficient manner, yet considerable research on the specific
applications of these intelligent components is significantly
deficient. Applications of Artificial Neural Networks for Nonlinear
Data is a collection of innovative research on the contemporary
nature of artificial neural networks and their specific
implementations within data analysis. While highlighting topics
including propagation functions, optimization techniques, and
learning methodologies, this book is ideally designed for
researchers, statisticians, academicians, developers, scientists,
practitioners, students, and educators seeking current research on
the use of artificial neural networks in diagnosing and solving
nonparametric problems.
Student assessment in online learning is submitted remotely without
any face-to-face interaction, and therefore, student authentication
is widely seen as one of the major challenges in online
examination. Authentication is the process of determining whether
someone or something is, in fact, who or what it is declared to be.
As the dependence upon computers and computer networks grows,
especially within education, the need for authentication has
increased. Biometric Authentication in Online Learning Environments
provides innovative insights into biometrics as a strategy to
mitigate risk and provide authentication, while introducing a
framework that provides security to improve e-learning and on-line
examination by utilizing biometric-based authentication techniques.
This book examines e-learning, security, threats in online exams,
security considerations, and biometric technologies, and is
designed for IT professionals, higher education administrators,
professors, researchers, business professionals, academicians, and
libraries seeking topics centered on biometrics as an
authentication strategy within educational environments.
Machine learning allows for non-conventional and productive answers
for issues within various fields, including problems related to
visually perceptive computers. Applying these strategies and
algorithms to the area of computer vision allows for higher
achievement in tasks such as spatial recognition, big data
collection, and image processing. There is a need for research that
seeks to understand the development and efficiency of current
methods that enable machines to see. Challenges and Applications
for Implementing Machine Learning in Computer Vision is a
collection of innovative research that combines theory and practice
on adopting the latest deep learning advancements for machines
capable of visual processing. Highlighting a wide range of topics
such as video segmentation, object recognition, and 3D modelling,
this publication is ideally designed for computer scientists,
medical professionals, computer engineers, information technology
practitioners, industry experts, scholars, researchers, and
students seeking current research on the utilization of evolving
computer vision techniques.
Decision-making is a frequent problem in today's financial,
business, and industrial world. Thus, fuzzy expert systems are
increasingly being used to solve decision-making problems by
attempting to solve a part or whole of a practical problem. These
expert systems have proven that they can solve problems in various
domains where human expertise is required, including the field of
agriculture. Fuzzy Expert Systems and Applications in Agricultural
Diagnosis is a crucial source that examines the use of fuzzy expert
systems for prediction and problem solving in the agricultural
industry. Featuring research on topics such as nutrition
management, sustainable agriculture, and defuzzification, this book
is ideally designed for farmers, researchers, scientists,
academics, students, policymakers, and development practitioners
seeking the latest research in technological tools that support
crop disease diagnosis.
Machine learning allows for non-conventional and productive answers
for issues within various fields, including problems related to
visually perceptive computers. Applying these strategies and
algorithms to the area of computer vision allows for higher
achievement in tasks such as spatial recognition, big data
collection, and image processing. There is a need for research that
seeks to understand the development and efficiency of current
methods that enable machines to see. Challenges and Applications
for Implementing Machine Learning in Computer Vision is a
collection of innovative research that combines theory and practice
on adopting the latest deep learning advancements for machines
capable of visual processing. Highlighting a wide range of topics
such as video segmentation, object recognition, and 3D modelling,
this publication is ideally designed for computer scientists,
medical professionals, computer engineers, information technology
practitioners, industry experts, scholars, researchers, and
students seeking current research on the utilization of evolving
computer vision techniques.
Digital classrooms have become a common addition to curriculums in
higher education; however, such learning systems are only
successful if students are properly motivated to learn. Optimizing
Student Engagement in Online Learning Environments is a critical
scholarly resource that examines the importance of motivation in
digital classrooms and outlines methods to reengage learners.
Featuring coverage on a broad range of topics such as motivational
strategies, learning assessment, and student involvement, this book
is geared toward academicians, researchers, and students seeking
current research on the importance of maintaining ambition among
learners in digital classrooms.
Student assessment in online learning is submitted remotely without
any face-to-face interaction, and therefore, student authentication
is widely seen as one of the major challenges in online
examination. Authentication is the process of determining whether
someone or something is, in fact, who or what it is declared to be.
As the dependence upon computers and computer networks grows,
especially within education, the need for authentication has
increased. Biometric Authentication in Online Learning Environments
provides innovative insights into biometrics as a strategy to
mitigate risk and provide authentication, while introducing a
framework that provides security to improve e-learning and on-line
examination by utilizing biometric-based authentication techniques.
This book examines e-learning, security, threats in online exams,
security considerations, and biometric technologies, and is
designed for IT professionals, higher education administrators,
professors, researchers, business professionals, academicians, and
libraries seeking topics centered on biometrics as an
authentication strategy within educational environments.
Web usage mining is defined as the application of data mining
technologies to online usage patterns as a way to better understand
and serve the needs of web-based applications. Because the internet
has become a central component in information sharing and commerce,
having the ability to analyze user behavior on the web has become a
critical component to a variety of industries. Web Usage Mining
Techniques and Applications Across Industries addresses the systems
and methodologies that enable organizations to predict web user
behavior as a way to support website design and personalization of
web-based services and commerce. Featuring perspectives from a
variety of sectors, this publication is designed for use by IT
specialists, business professionals, researchers, and
graduate-level students interested in learning more about the
latest concepts related to web-based information retrieval and
mining.
The development of fuzzy expert systems has provided new
opportunities for problem solving amidst uncertainties. The medical
field, in particular, has benefitted tremendously from advancing
fuzzy system technologies. Fuzzy Expert Systems for Disease
Diagnosis highlights the latest research and developments in fuzzy
rule-based methods used in the detection of medical complications
and illness. Offering emerging solutions and practical
applications, this timely publication is designed for use by
researchers, academicians, and students, as well as practitioners
in the medical field.
Mobile computing facilitates data transmission without needing to
be connected to a fixed physical link. Mobile voice communication
is widely established throughout the world and the number of
subscribers to various cellular networks has increased considerably
over the last few years. An extension of this technology is the
ability to send and receive data across these cellular networks.
Mobile data communication has become a very important and rapidly
evolving technology as it allows users to transmit data from remote
locations to other remote or fixed locations. This proves to be the
solution to the biggest problem for business people on the move.
Mobile Computing Techniques in Emerging Markets: Systems,
Applications and Services provides the latest research and best
practices in the field of mobile computing. Theoretical and
pragmatic viewpoints on mobile computing offer guidance for
professionals using this book to inform their practices. A solid
foundation on mobile computing and an expansive vision of its
possibilities combine to promote understanding and the successful
implementation of mobile computing techniques in emerging markets.
Knowledge Discovery Practices and Emerging Applications of Data
Mining: Trends and New Domains introduces the reader to recent
research activities in the field of data mining. This book covers
association mining, classification, mobile marketing, opinion
mining, microarray data mining, internet mining and applications of
data mining on biological data, telecommunication and distributed
databases, among others, while promoting understanding and
implementation of data mining techniques in emerging domains.
Global inequality has been a burning issue for years now. As the
economies progress, it is expected that the benefits of growth will
percolate to the lower sections of society. However, this
percolation takes place in a discriminating manner. Inequality can
be observed in terms of health, income, education, wealth, gender,
availability of opportunities, and other socio-economic parameters.
The governing authorities and international agencies have been
taking various corrective measures to reduce the widening levels of
inequality. However, certain external factors like the pandemic can
wash away the efforts taken and deteriorate the progress made on
the inequality levels in economies. Emerging Trends and Insights on
Economic Inequality in the Wake of Global Crises discusses the
impact of global disasters and crises on economic inequality. It
provides an overview of the evolution of global inequality over the
years, increasing different forms of inequalities amidst crises,
the corrective measures taken by the national and international
agencies, and the way forward for economies with worsening
inequalities. Covering topics such as crisis management, digital
agriculture, and economic welfare, this premier reference source is
an essential resource for economists, business leaders and
executives, government officials, students and educators of higher
education, sociologists, researchers, and academicians.
Use of big data has proven to be beneficial within many different
industries, especially in the field of engineering; however,
infiltration of this type of technology into more traditional heavy
industries, such as the railways, has been limited. Innovative
Applications of Big Data in the Railway Industry is a pivotal
reference source for the latest research findings on the
utilization of data sets in the railway industry. Featuring
extensive coverage on relevant areas such as driver support
systems, railway safety management, and obstacle detection, this
publication is an ideal resource for transportation planners,
engineers, policymakers, and graduate-level engineering students
seeking current research on a specific application of big data and
its effects on transportation.
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