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Books > Computing & IT > Applications of computing > Artificial intelligence
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Bio-inspired Algorithms for Data Streaming and Visualization, Big Data Management, and Fog Computing (Hardcover, 1st ed. 2021)
Loot Price: R4,808
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Bio-inspired Algorithms for Data Streaming and Visualization, Big Data Management, and Fog Computing (Hardcover, 1st ed. 2021)
Series: Springer Tracts in Nature-Inspired Computing
Expected to ship within 12 - 17 working days
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This book aims to provide some insights into recently developed
bio-inspired algorithms within recent emerging trends of fog
computing, sentiment analysis, and data streaming as well as to
provide a more comprehensive approach to the big data management
from pre-processing to analytics to visualization phases. The
subject area of this book is within the realm of computer science,
notably algorithms (meta-heuristic and, more particularly,
bio-inspired algorithms). Although application domains of these new
algorithms may be mentioned, the scope of this book is not on the
application of algorithms to specific or general domains but to
provide an update on recent research trends for bio-inspired
algorithms within a specific application domain or emerging area.
These areas include data streaming, fog computing, and phases of
big data management. One of the reasons for writing this book is
that the bio-inspired approach does not receive much attention but
shows considerable promise and diversity in terms of approach of
many issues in big data and streaming. Some novel approaches of
this book are the use of these algorithms to all phases of data
management (not just a particular phase such as data mining or
business intelligence as many books focus on); effective
demonstration of the effectiveness of a selected algorithm within a
chapter against comparative algorithms using the experimental
method. Another novel approach is a brief overview and evaluation
of traditional algorithms, both sequential and parallel, for use in
data mining, in order to provide an overview of existing algorithms
in use. This overview complements a further chapter on bio-inspired
algorithms for data mining to enable readers to make a more
suitable choice of algorithm for data mining within a particular
context. In all chapters, references for further reading are
provided, and in selected chapters, the author also include ideas
for future research.
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