Jing Gao - Böcker

Advances in Food and Nutrition Research, Volume 99 highlights new advances in the field, with this updated volume presenting interesting chapters on a variety of topics, including Personalizing bakery products using 3D food printing, Dietary fiber in bakery products: source, processing, and function, The realm of plant proteins with focus on their application in developing new bakery products, Guiding the formulation of baked goods for the elderly population through food oral processing: challenges and opportunities, Gluten free bakery products: Ingredients and processes, Enhancing health benefits of bakery products using phytochemicals, Sugar, salt and fat reduction of bakery products, and more. Provides the authority and expertise of leading contributors from an international board of authors Presents the latest release in the Advances in Food and Nutrition Research series Includes the latest information on Functional Bakery Products

Outlier (or anomaly) detection is a very broad field which has been studied in the context of a large number of research areas like statistics, data mining, sensor networks, environmental science, distributed systems, spatio-temporal mining, etc. Initial research in outlier detection focused on time series-based outliers (in statistics). Since then, outlier detection has been studied on a large variety of data types including high-dimensional data, uncertain data, stream data, network data, time series data, spatial data, and spatio-temporal data. While there have been many tutorials and surveys for general outlier detection, we focus on outlier detection for temporal data in this book. A large number of applications generate temporal datasets. For example, in our everyday life, various kinds of records like credit, personnel, financial, judicial, medical, etc., are all temporal. This stresses the need for an organized and detailed study of outliers with respect to such temporal data.In the past decade, there has been a lot of research on various forms of temporal data including consecutive data snapshots, series of data snapshots and data streams. Besides the initial work on time series, researchers have focused on rich forms of data including multiple data streams, spatio-temporal data, network data, community distribution data, etc. Compared to general outlier detection, techniques for temporal outlier detection are very different. In this book, we will present an organized picture of both recent and past research in temporal outlier detection. We start with the basics and then ramp up the reader to the main ideas in state-of-the-art outlier detection techniques. We motivate the importance of temporal outlier detection and brief the challenges beyond usual outlier detection. Then, we list down a taxonomy of proposed techniques for temporal outlier detection. Such techniques broadly include statistical techniques (like AR models, Markov models, histograms, neuralnetworks), distance- and density-based approaches, grouping-based approaches (clustering, community detection), network-based approaches, and spatio-temporal outlier detection approaches. We summarize by presenting a wide collection of applications where temporal outlier detection techniques have been applied to discover interesting outliers. Table of Contents: Preface / Acknowledgments / Figure Credits / Introduction and Challenges / Outlier Detection for Time Series and Data Sequences / Outlier Detection for Data Streams / Outlier Detection for Distributed Data Streams / Outlier Detection for Spatio-Temporal Data / Outlier Detection for Temporal Network Data / Applications of Outlier Detection for Temporal Data / Conclusions and Research Directions / Bibliography / Authors' Biographies

This book covers network analysis and architecture for large-scale computer network planning. Networks and the Internet are essential for modern industries and societies. Building a new network, upgrading an existing network, or planning to use a public network requires integrating various network mechanisms and technologies in a cohesive fashion. This demands a deep understanding of the concepts, principles, processes, approaches, and good practices of advanced network planning. More specifically, emphasizing service-based networking, the book introduces structured processes for network planning, provides systematic approaches for network analysis and architecture, develops network planning specifications, and discusses high-level network architectural models from various perspectives. It also offers detailed discussions on component-based architecture about addressing, routing, performance, management, and security and privacy. Recent developments in data centers, virtualization,and cloud are also embedded into the network architecture. Moreover, the book includes a comprehensive introduction to building practical TCP/IP network communications via sockets with practical examples. The book is suitable for use as a textbook for senior undergraduate and postgraduate students or as a reference book for network practitioners looking to develop or enhance their skills in network planning.