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This new volume provides a collection of chapters on diverse topics
in machine learning algorithms and security analytics, AI and
machine learning, and network security applications. It presents a
variety of design algorithms that allow computers to employ machine
learning to display behavior learned from past experiences rather
than human interaction for solutions to security issues and other
challenges in data management. The book discusses a variety of
algorithms, including Convolutional Neural Network (CNN), Random
Forest Algorithm, K-Nearest Neighbor (KNN), Apriori algorithm,
MapReduce algorithm, Genetic Algorithm used in IoT applications,
and more. The volume presents a survey of speculative parallelism
techniques, overheads due to mis-speculation of parallel threads,
performance reviews, and finally efficient power consumption. It
discusses measuring perceived quality of software ecosystems based
on transactions in customer management tools and offers a study of
the background modeling and background subtraction along with
various other literature studies that justify the role of moving
object detection in computer vision. The book also discusses the
major challenging issues that occur in real-time environments,
outlines the key developments of UAV networks for disaster
management applications, and addresses open research issues and
challenges based on UAV for disaster management. It also covers the
concepts of learning with NASA datasets. Scientists, researchers,
faculty, and students involved in research in the area of AI,
machine learning, and network security will find valuable
information in this volume.
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