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Currently, machine learning is playing a pivotal role in the
progress of genomics. The applications of machine learning are
helping all to understand the emerging trends and the future scope
of genomics. This book provides comprehensive coverage of machine
learning applications such as DNN, CNN, and RNN, for
predicting the sequence of DNA and RNA binding proteins, expression
of the gene, and splicing control. In addition, the book addresses
the effect of multiomics data analysis of cancers using tensor
decomposition, machine learning techniques for protein engineering,
CNN applications on genomics, challenges of long noncoding RNAs in
human disease diagnosis, and how machine learning can be used as
a tool to shape the future of medicine. More importantly, it
gives a comparative analysis and validates the outcomes of machine
learning methods on genomic data to the functional laboratory tests
or by formal clinical assessment. The topics of this book will
cater interest to academicians, practitioners working in the
field of functional genomics, and machine learning. Also, this book
shall guide comprehensively the graduate, postgraduates, and Ph.D.
scholars working in these fields.
This book presents the current trends, technologies, and challenges
in Big Data in the diversified field of engineering and sciences.
It covers the applications of Big Data ranging from conventional
fields of mechanical engineering, civil engineering to electronics,
electrical, and computer science to areas in pharmaceutical and
biological sciences. This book consists of contributions from
various authors from all sectors of academia and industries,
demonstrating the imperative application of Big Data for the
decision-making process in sectors where the volume, variety, and
velocity of information keep increasing. The book is a useful
reference for graduate students, researchers and scientists
interested in exploring the potential of Big Data in the
application of engineering areas.
This book presents the current trends, technologies, and challenges
in Big Data in the diversified field of engineering and sciences.
It covers the applications of Big Data ranging from conventional
fields of mechanical engineering, civil engineering to electronics,
electrical, and computer science to areas in pharmaceutical and
biological sciences. This book consists of contributions from
various authors from all sectors of academia and industries,
demonstrating the imperative application of Big Data for the
decision-making process in sectors where the volume, variety, and
velocity of information keep increasing. The book is a useful
reference for graduate students, researchers and scientists
interested in exploring the potential of Big Data in the
application of engineering areas.
Currently, machine learning is playing a pivotal role in the
progress of genomics. The applications of machine learning are
helping all to understand the emerging trends and the future scope
of genomics. This book provides comprehensive coverage of machine
learning applications such as DNN, CNN, and RNN, for predicting the
sequence of DNA and RNA binding proteins, expression of the gene,
and splicing control. In addition, the book addresses the effect of
multiomics data analysis of cancers using tensor decomposition,
machine learning techniques for protein engineering, CNN
applications on genomics, challenges of long noncoding RNAs in
human disease diagnosis, and how machine learning can be used as a
tool to shape the future of medicine. More importantly, it gives a
comparative analysis and validates the outcomes of machine learning
methods on genomic data to the functional laboratory tests or by
formal clinical assessment. The topics of this book will cater
interest to academicians, practitioners working in the field of
functional genomics, and machine learning. Also, this book shall
guide comprehensively the graduate, postgraduates, and Ph.D.
scholars working in these fields.
Data Analytics in Biomedical Engineering and Healthcare explores
key applications using data analytics, machine learning, and deep
learning in health sciences and biomedical data. The book is useful
for those working with big data analytics in biomedical research,
medical industries, and medical research scientists. The book
covers health analytics, data science, and machine and deep
learning applications for biomedical data, covering areas such as
predictive health analysis, electronic health records, medical
image analysis, computational drug discovery, and genome structure
prediction using predictive modeling. Case studies demonstrate big
data applications in healthcare using the MapReduce and Hadoop
frameworks.
Predictive Modeling for Energy Management and Power Systems
Engineering introduces readers to the cutting-edge use of big data
and large computational infrastructures in energy demand estimation
and power management systems. The book supports engineers and
scientists who seek to become familiar with advanced optimization
techniques for power systems designs, optimization techniques and
algorithms for consumer power management, and potential
applications of machine learning and artificial intelligence in
this field. The book provides modeling theory in an easy-to-read
format, verified with on-site models and case studies for specific
geographic regions and complex consumer markets.
This book presents a broad range of deep-learning applications
related to vision, natural language processing, gene expression,
arbitrary object recognition, driverless cars, semantic image
segmentation, deep visual residual abstraction, brain-computer
interfaces, big data processing, hierarchical deep learning
networks as game-playing artefacts using regret matching, and
building GPU-accelerated deep learning frameworks. Deep learning,
an advanced level of machine learning technique that combines class
of learning algorithms with the use of many layers of nonlinear
units, has gained considerable attention in recent times. Unlike
other books on the market, this volume addresses the challenges of
deep learning implementation, computation time, and the complexity
of reasoning and modeling different type of data. As such, it is a
valuable and comprehensive resource for engineers, researchers,
graduate students and Ph.D. scholars.
Handbook of Neural Computation explores neural computation
applications, ranging from conventional fields of mechanical and
civil engineering, to electronics, electrical engineering and
computer science. This book covers the numerous applications of
artificial and deep neural networks and their uses in learning
machines, including image and speech recognition, natural language
processing and risk analysis. Edited by renowned authorities in
this field, this work is comprised of articles from reputable
industry and academic scholars and experts from around the world.
Each contributor presents a specific research issue with its recent
and future trends. As the demand rises in the engineering and
medical industries for neural networks and other machine learning
methods to solve different types of operations, such as data
prediction, classification of images, analysis of big data, and
intelligent decision-making, this book provides readers with the
latest, cutting-edge research in one comprehensive text.
The disciplines of science and engineering rely heavily on the
forecasting of prospective constraints for concepts that have not
yet been proven to exist, especially in areas such as artificial
intelligence. Obtaining quality solutions to the problems presented
becomes increasingly difficult due to the number of steps required
to sift through the possible solutions, and the ability to solve
such problems relies on the recognition of patterns and the
categorization of data into specific sets. Predictive modeling and
optimization methods allow unknown events to be categorized based
on statistics and classifiers input by researchers. The Handbook of
Research on Predictive Modeling and Optimization Methods in Science
and Engineering is a critical reference source that provides
comprehensive information on the use of optimization techniques and
predictive models to solve real-life engineering and science
problems. Through discussions on techniques such as robust design
optimization, water level prediction, and the prediction of human
actions, this publication identifies solutions to developing
problems and new solutions for existing problems, making this
publication a valuable resource for engineers, researchers,
graduate students, and other professionals.
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