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Artificial intelligence is at the forefront of research and
implementation in many industries including healthcare and
agriculture. Whether it's detecting disease or generating
algorithms, deep learning techniques are advancing exponentially.
Researchers and professionals need a platform in which they can
keep up with machine learning trends and their developments in the
real world. The Handbook of Research on Applications and
Implementations of Machine Learning Techniques provides innovative
insights into the multi-disciplinary applications of machine
learning algorithms for data analytics. The content within this
publication examines disease identification, neural networks, and
language support. It is designed for IT professionals, developers,
data analysts, technology specialists, R&D professionals,
industrialists, practitioners, researchers, academicians, and
students seeking research on deep learning procedures and their
enactments in the fields of medicine, engineering, and computer
science.
In today's market, emerging technologies are continually assisting
in common workplace practices as companies and organizations search
for innovative ways to solve modern issues that arise. Prevalent
applications including internet of things, big data, and cloud
computing all have noteworthy benefits, but issues remain when
separately integrating them into the professional practices.
Significant research is needed on converging these systems and
leveraging each of their advantages in order to find solutions to
real-time problems that still exist. Challenges and Opportunities
for the Convergence of IoT, Big Data, and Cloud Computing is a
pivotal reference source that provides vital research on the
relation between these technologies and the impact they
collectively have in solving real-world challenges. While
highlighting topics such as cloud-based analytics, intelligent
algorithms, and information security, this publication explores
current issues that remain when attempting to implement these
systems as well as the specific applications IoT, big data, and
cloud computing have in various professional sectors. This book is
ideally designed for academicians, researchers, developers,
computer scientists, IT professionals, practitioners, scholars,
students, and engineers seeking research on the integration of
emerging technologies to solve modern societal issues.
In today's market, emerging technologies are continually assisting
in common workplace practices as companies and organizations search
for innovative ways to solve modern issues that arise. Prevalent
applications including internet of things, big data, and cloud
computing all have noteworthy benefits, but issues remain when
separately integrating them into the professional practices.
Significant research is needed on converging these systems and
leveraging each of their advantages in order to find solutions to
real-time problems that still exist. Challenges and Opportunities
for the Convergence of IoT, Big Data, and Cloud Computing is a
pivotal reference source that provides vital research on the
relation between these technologies and the impact they
collectively have in solving real-world challenges. While
highlighting topics such as cloud-based analytics, intelligent
algorithms, and information security, this publication explores
current issues that remain when attempting to implement these
systems as well as the specific applications IoT, big data, and
cloud computing have in various professional sectors. This book is
ideally designed for academicians, researchers, developers,
computer scientists, IT professionals, practitioners, scholars,
students, and engineers seeking research on the integration of
emerging technologies to solve modern societal issues.
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