Decision Support Systems for Sustainable Computing investigates recent technological advances in decision support systems models designed to solve real world applications. The book provides a broad overview of digital technology transformation as applied to the circular economy which is seeking to drive improvements in scientific research, communication, logistics, automation, production, and the improved sustainability of these processes and products. The book explores applications of decision support for sustainable development across supply chain management, business intelligence, agriculture, aviation, communications, and finance. - Provides a broad overview of emerging trends and technologies in decision support systems applications - Investigates recent trends and core concepts in digital technology transformation as applied to the circular economy and sustainable development - Analyzes the application of decision support systems models across a range of case studies and processes which rely on multi-criteria decision-making and have been designed specifically to improve overall sustainability

In recent years, much work has been done in formulating and clarifying the concept of sustainable development and related theoretical and research issues. Now, the challenge has shifted to designing and stimulating processes of effective planning and decision-making, at all levels of human activity, in such a way as to achieve local and global sustainable development. Information technology can help a great deal in achieving sustainable development by providing well-designed and useful tools for decision makers. One such tool is the decision support system, or DSS. This book explores the area of DSS in the context of sustainable development. As DSS is a very new technique, especially in the developing world, this book will serve as a reference text, primarily for managers, government officials, and information professionals in developing countries. It covers the concept of sustainable development, defines DSS and how it can be used in the planning and management of sustainable development, and examines the state of the art in DSS use. Other interested readers will include students, teachers, and analysts in information sciences; DSS designers, developers, and implementors; and international development agencies.

This unique book dicusses the latest research, innovative ideas, challenges and computational intelligence (CI) solutions in sustainable computing. It presents novel, in-depth fundamental research on achieving a sustainable lifestyle for society, either from a methodological or from an application perspective. Sustainable computing has expanded to become a significant research area covering the fields of computer science and engineering, electrical engineering and other engineering disciplines, and there has been an increase in the amount of literature on aspects sustainable computing such as energy efficiency and natural resources conservation that emphasizes the role of ICT (information and communications technology) in achieving system design and operation objectives. The energy impact/design of more efficient IT infrastructures is a key challenge in realizing new computing paradigms. The book explores the uses of computational intelligence (CI) techniques for intelligent decision support that can be exploited to create effectual computing systems, and addresses sustainability problems in computing and information processing environments and technologies at the different levels of CI paradigms. An excellent guide to surveying the state of the art in computational intelligence applied to challenging real-world problems in sustainable computing, it is intended for scientists, practitioners, researchers and academicians dealing with the new challenges and advances in area.

"This book focuses on information technology using sustainable green computing to reduce energy and resources used"--Provided by publisher.

INTELLIGENT DECISION SUPPORT SYSTEMS FOR SMART CITY APPLICATIONS This book provides smart city frameworks to address new difficulties by adding new features and allowing the city environment to react to collected data and information to increase the efficiency and sustainability of services for inhabitants. Making a smart city is an emerging strategy to mitigate the problems generated by urban population growth and rapid urbanization. This book aims to provide a better understanding of the concept of smart cities and the application of an intelligent decision support system. Based on the analysis of existing information there are eight critical factors of smart city initiatives: management and organization, technology, governance, policy context, people and communities, economy, built infrastructure, and natural environment. This book will focus on the application of the decision support system in managing these eight crucial aspects of smart cities. The intent in writing this book was also to provide a source that covers the stage-by-stage integration of the four key areas involving planning, physical infrastructure, ICT infrastructure, and deploying the smart solutions necessary for city transformation. With this as the motivation, “Decision Support Systems for Smart City Applications” provides the application of an intelligent decision support system for effectively and efficiently managing the transformation process, which can aid various supply chain stakeholders, academic researchers, and related professionals in building smart cities. Various chapters of this book are expected to support practicing managers during the implementation of smart solutions for city transformation. Audience This book is aimed at both academics and practitioners alike in the fields of intelligent computing, decision support systems, the manufacturing industry, supply chain managers, stakeholders, policymakers, and other technical and administrative personnel.

HANDBOOK OF INTELLIGENT COMPUTING AND OPTIMIZATION FOR SUSTAINABLE DEVELOPMENT This book provides a comprehensive overview of the latest breakthroughs and recent progress in sustainable intelligent computing technologies, applications, and optimization techniques across various industries. Optimization has received enormous attention along with the rapidly increasing use of communication technology and the development of user-friendly software and artificial intelligence. In almost all human activities, there is a desire to deliver the highest possible results with the least amount of effort. Moreover, optimization is a very well-known area with a vast number of applications, from route finding problems to medical treatment, construction, finance, accounting, engineering, and maintenance schedules in plants. As far as optimization of real-world problems is concerned, understanding the nature of the problem and grouping it in a proper class may help the designer employ proper techniques which can solve the problem efficiently. Many intelligent optimization techniques can find optimal solutions without the use of objective function and are less prone to local conditions. The 41 chapters comprising the Handbook of Intelligent Computing and Optimization for Sustainable Development by subject specialists, represent diverse disciplines such as mathematics and computer science, electrical and electronics engineering, neuroscience and cognitive sciences, medicine, and social sciences, and provide the reader with an integrated understanding of the importance that intelligent computing has in the sustainable development of current societies. It discusses the emerging research exploring the theoretical and practical aspects of successfully implementing new and innovative intelligent techniques in a variety of sectors, including IoT, manufacturing, optimization, and healthcare. Audience It is a pivotal reference source for IT specialists, industry professionals, managers, executives, researchers, scientists, and engineers seeking current research in emerging perspectives in the field of artificial intelligence in the areas of Internet of Things, renewable energy, optimization, and smart cities.

Praise for the First Edition "This is the most usable decision support systems text. [i]t is far better than any other text in the field" —Computing Reviews Computer-based systems known as decision support systems (DSS) play a vital role in helping professionals across various fields of practice understand what information is needed, when it is needed, and in what form in order to make smart and valuable business decisions. Providing a unique combination of theory, applications, and technology, Decision Support Systems for Business Intelligence, Second Edition supplies readers with the hands-on approach that is needed to understand the implications of theory to DSS design as well as the skills needed to construct a DSS. This new edition reflects numerous advances in the field as well as the latest related technological developments. By addressing all topics on three levels—general theory, implications for DSS design, and code development—the author presents an integrated analysis of what every DSS designer needs to know. This Second Edition features: Expanded coverage of data mining with new examples Newly added discussion of business intelligence and transnational corporations Discussion of the increased capabilities of databases and the significant growth of user interfaces and models Emphasis on analytics to encourage DSS builders to utilize sufficient modeling support in their systems A thoroughly updated section on data warehousing including architecture, data adjustment, and data scrubbing Explanations and implications of DSS differences across cultures and the challenges associated with transnational systems Each chapter discusses various aspects of DSS that exist in real-world applications, and one main example of a DSS to facilitate car purchases is used throughout the entire book. Screenshots from JavaScript® and Adobe® ColdFusion are presented to demonstrate the use of popular software packages that carry out the discussed techniques, and a related Web site houses all of the book's figures along with demo versions of decision support packages, additional examples, and links to developments in the field. Decision Support Systems for Business Intelligence, Second Edition is an excellent book for courses on information systems, decision support systems, and data mining at the advanced undergraduate and graduate levels. It also serves as a practical reference for professionals working in the fields of business, statistics, engineering, and computer technology.

This book is a collection of best selected research papers presented at Second International Conference on Intelligent Systems and Sustainable Computing (ICISSC 2022), held in School of Engineering, Malla Reddy University, Hyderabad, India, during December 16–17, 2022. The book covers recent research in intelligent systems, intelligent business systems, soft computing, swarm intelligence, artificial intelligence and neural networks, data mining and data warehousing, cloud computing, distributed computing, big data analytics, Internet of things (IoT), machine learning, speech processing, sustainable high-performance systems, VLSI and embedded systems, image and video processing and signal processing and communication.

This book addresses major challenges faced by farmers and the technological solutions based on Internet of Things (IoT). A major challenge in agriculture is cultivating and supplying high-quality produce at the best. Currently, around 50% of global farm produce never reaches the end consumer due to wastage and suboptimal prices. The book presents solutions that reduce the transport costs, improve the predictability of prices based on data analytics and the current market conditions, and reduce the number of middle steps and agents between the farmer and the end consumer. It discusses the design of an IoT-based monitoring system to analyze crop environments and a method to improve the efficiency of decision-making by analyzing harvest statistics. Further, it explores climate-smart methods, known as smart agriculture, that have been adopted by a number of Indian farmers.