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.