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Artificial Intelligence, Machine Learning, and Optimization Tools for Smart Cities
This volume offers a wealth of interdisciplinary approaches to artificial intelligence, machine learning and optimization tools, which contribute to the optimization of urban features towards forming smart, sustainable, and livable future cities. Special features include: New research on the design of city elements and smart systems with respect to new technologies and scientific thinking Discussions on the theoretical background that lead to smart cities for the future New technologies and principles of research that can promote ideas of artificial intelligence and machine learning in optimized urban environments The book engages students and researchers in the subjects of artificial intelligence, machine learning, and optimization tools in smart sustainable cities as eminent international experts contribute their research results and thinking in its chapters. Overall, its audience can benefit from a variety of disciplines including, architecture, engineering, physics, mathematics, computer science, and related fields.
Synthetic Aperture Radar (SAR) Data Applications
This carefully curated volume presents an in-depth, state-of-the-art discussion on many applications of Synthetic Aperture Radar (SAR). Integrating interdisciplinary sciences, the book features novel ideas, quantitative methods, and research results, promising to advance computational practices and technologies within the academic and industrial communities. SAR applications employ diverse and often complex computational methods rooted in machine learning, estimation, statistical learning, inversion models, and empirical models. Current and emerging applications of SAR data for earth observation, object detection and recognition, change detection, navigation, and interference mitigation are ...
Dynamics of Disasters
Based on the “Fourth International Conference on Dynamics of Disasters” (Kalamata, Greece, July 2019), this volume includes contributions from experts who share their latest discoveries on natural and unnatural disasters. Authors provide overviews of the tactical points involved in disaster relief, outlines of hurdles from mitigation and preparedness to response and recovery, and uses for mathematical models to describe natural and man-made disasters. Topics covered include economics, optimization, machine learning, government, management, business, humanities, engineering, medicine, mathematics, computer science, behavioral studies, emergency services, and environmental studies will engage readers from a wide variety of fields and backgrounds.
Analysis of Experimental Algorithms
This book constitutes the refereed post-conference proceedings of the Special Event on the Analysis of Experimental Algorithms, SEA2 2019, held in Kalamata, Greece, in June 2019. The 35 revised full papers presented were carefully reviewed and selected from 45 submissions. The papers cover a wide range of topics in both computer science and operations research/mathematical programming. They focus on the role of experimentation and engineering techniques in the design and evaluation of algorithms, data structures, and computational optimization methods.
Numerical Methods and Optimization
For students in industrial and systems engineering (ISE) and operations research (OR) to understand optimization at an advanced level, they must first grasp the analysis of algorithms, computational complexity, and other concepts and modern developments in numerical methods. Satisfying this prerequisite, Numerical Methods and Optimization: An Intro
Fragile Networks
A unified treatment of the vulnerabilities that exist in real-world network systems—with tools to identify synergies for mergers and acquisitions Fragile Networks: Identifying Vulnerabilities and Synergies in an Uncertain World presents a comprehensive study of network systems and the roles these systems play in our everyday lives. This book successfully conceptualizes, defines, and constructs mathematically rigorous, computer-based tools for the assessment of network performance and efficiency, along with robustness and vulnerability analysis. The result is a thorough exploration that promotes an understanding of the critical infrastructure of today's network systems, from congested urban...
Approximation and Optimization
This book focuses on the development of approximation-related algorithms and their relevant applications. Individual contributions are written by leading experts and reflect emerging directions and connections in data approximation and optimization. Chapters discuss state of the art topics with highly relevant applications throughout science, engineering, technology and social sciences. Academics, researchers, data science practitioners, business analysts, social sciences investigators and graduate students will find the number of illustrations, applications, and examples provided useful. This volume is based on the conference Approximation and Optimization: Algorithms, Complexity, and Applications, which was held in the National and Kapodistrian University of Athens, Greece, June 29–30, 2017. The mix of survey and research content includes topics in approximations to discrete noisy data; binary sequences; design of networks and energy systems; fuzzy control; large scale optimization; noisy data; data-dependent approximation; networked control systems; machine learning ; optimal design; no free lunch theorem; non-linearly constrained optimization; spectroscopy.
Nonlinear Combinatorial Optimization
Graduate students and researchers in applied mathematics, optimization, engineering, computer science, and management science will find this book a useful reference which provides an introduction to applications and fundamental theories in nonlinear combinatorial optimization. Nonlinear combinatorial optimization is a new research area within combinatorial optimization and includes numerous applications to technological developments, such as wireless communication, cloud computing, data science, and social networks. Theoretical developments including discrete Newton methods, primal-dual methods with convex relaxation, submodular optimization, discrete DC program, along with several applications are discussed and explored in this book through articles by leading experts.
Sustainable Logistics and Transportation
Focused on the logistics and transportation operations within a supply chain, this book brings together the latest models, algorithms, and optimization possibilities. Logistics and transportation problems are examined within a sustainability perspective to offer a comprehensive assessment of environmental, social, ethical, and economic performance measures. Featured models, techniques, and algorithms may be used to construct policies on alternative transportation modes and technologies, green logistics, and incentives by the incorporation of environmental, economic, and social measures. Researchers, professionals, and graduate students in urban regional planning, logistics, transport systems, optimization, supply chain management, business administration, information science, mathematics, and industrial and systems engineering will find the real life and interdisciplinary issues presented in this book informative and useful.
Optimal Coverage in Wireless Sensor Networks
This book will serve as a reference, presenting state-of-the-art research on theoretical aspects of optimal sensor coverage problems. Readers will find it a useful tool for furthering developments on theory and applications of optimal coverage; much of the content can serve as material for advanced topics courses at the graduate level. The book is well versed with the hottest research topics such as Lifetime of Coverage, Weighted Sensor Cover, k-Coverage, Heterogeneous Sensors, Barrier, Sweep and Partial Coverage, Mobile Sensors, Camera Sensors and Energy-Harvesting Sensors, and more. Topics are introduced in a natural order from simple covers to connected covers, to the lifetime problem. Later, the book begins revisiting earlier problems ranging from the introduction of weights to coverage by k sensors and partial coverage, and from sensor heterogeneity to novel problems such as the barrier coverage problem. The book ends with coverage of mobile sensors, camera sensors, energy-harvesting sensors, underwater sensors, and crowdsensing.
