Advanced Analytics and Deep Learning Models The book provides
readers with an in-depth understanding of concepts and technologies
related to the importance of analytics and deep learning in many
useful real-world applications such as e-healthcare,
transportation, agriculture, stock market, etc. Advanced analytics
is a mixture of machine learning, artificial intelligence, graphs,
text mining, data mining, semantic analysis. It is an approach to
data analysis. Beyond the traditional business intelligence, it is
a semi and autonomous analysis of data by using different
techniques and tools. However, deep learning and data analysis both
are high centers of data science. Almost all the private and public
organizations collect heavy amounts of data, i.e., domain-specific
data. Many small/large companies are exploring large amounts of
data for existing and future technology. Deep learning is also
exploring large amounts of unsupervised data making it beneficial
and effective for big data. Deep learning can be used to deal with
all kinds of problems and challenges that include collecting
unlabeled and uncategorized raw data, extracting complex patterns
from a large amount of data, retrieving fast information, tagging
data, etc. This book contains 16 chapters on artificial
intelligence, machine learning, deep learning, and their uses in
many useful sectors like stock market prediction, a recommendation
system for better service selection, e-healthcare, telemedicine,
transportation. There are also chapters on innovations and future
opportunities with fog computing/cloud computing and artificial
intelligence. Audience Researchers in artificial intelligence, big
data, computer science, and electronic engineering, as well as
industry engineers in healthcare, telemedicine, transportation, and
the financial sector. The book will also be a great source for
software engineers and advanced students who are beginners in the
field of advanced analytics in deep learning.
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