This book explores the applications and advancements of federated learning across diverse sectors, focusing on its integration with cutting- edge technologies like Internet of Things (IoT), artificial intelligence (AI), blockchain, and digital twins.
This book introduces an auto design based optimization for building frames using an artificial neural network (ANN) based Lagrange method and novel genetic algorithm (GA).
In einer Welt, die von Daten und Geschwindigkeit dominiert wird, wird künstliche Intelligenz (KI) zur treibenden Kraft hinter bahnbrechenden Veränderungen.
Departing from traditional methodologies of teaching data analysis, this book presents a dual-track learning experience, with both Executive and Technical Tracks, designed to accommodate readers with various learning goals or skill levels.
This contributed volume offers practical implementation strategies for statistical learning and data science techniques, with fully peer-reviewed papers that embody insights and experiences gathered within the LISA 2020 Global Network.
A comprehensive guide for IoT engineers, this book integrates data science, machine learning, and systems analytics to provide a robust understanding of modern techniques.
This book introduces an auto design based optimization for building frames using an artificial neural network (ANN) based Lagrange method and novel genetic algorithm (GA).
Collecting the latest developments in the field, Multimedia Data Mining: A Systematic Introduction to Concepts and Theory defines multimedia data mining, its theory, and its applications.
Although there are already some books published on Big Data, most of them only cover basic concepts and society impacts and ignore the internal implementation details-making them unsuitable to R&D people.
Drawing on the authors' two decades of experience in applied modeling and data mining, Foundations of Predictive Analytics presents the fundamental background required for analyzing data and building models for many practical applications, such as consumer behavior modeling, risk and marketing analytics, and other areas.
A ready reference for the next-generation discovery techniques, this book presents advanced research findings on resource discovery, network navigability, and trust management on the Internet of Things (IoT) and Social Internet of Things (SIoT) ecosystems.
The Definitive Resource on Text Mining Theory and Applications from Foremost Researchers in the FieldGiving a broad perspective of the field from numerous vantage points, Text Mining: Classification, Clustering, and Applications focuses on statistical methods for text mining and analysis.
A culmination of the authors' years of extensive research on this topic, Relational Data Clustering: Models, Algorithms, and Applications addresses the fundamentals and applications of relational data clustering.
Thoroughly covering basic introductions and intuitions, technical details, and formal foundations, this text focuses on the established foundations in this area that have become relatively stable over time.
Providing key information on how to work with research data, Introduction to Data Technologies presents ideas and techniques for performing critical, behind-the-scenes tasks that take up so much time and effort yet typically receive little attention in formal education.
Regularization, Optimization, Kernels, and Support Vector Machines offers a snapshot of the current state of the art of large-scale machine learning, providing a single multidisciplinary source for the latest research and advances in regularization, sparsity, compressed sensing, convex and large-scale optimization, kernel methods, and support vecto
