Algorithms In Decision Support Systems

This book aims to provide a new vision of how algorithms are the core of decision support systems (DSSs), which are increasingly important information systems that help to make decisions related to unstructured and semi-unstructured decision problems that do not have a simple solution from a human point of view. It begins with a discussion of how DSSs will be vital to improving the health of the population. The following article deals with how DSSs can be applied to improve the performance of people doing a specific task, like playing tennis. It continues with a work in which authors apply DSSs to insect pest management, together with an interactive platform for fitting data and carrying out spatial visualization. The next article improves how to reschedule trains whenever disturbances occur, together with an evaluation framework. The final works focus on different relevant areas of DSSs: 1) a comparison of ensemble and dimensionality reduction models based on an entropy criterion; 2) a radar emitter identification method based on semi-supervised and transfer learning; 3) design limitations, errors, and hazards in creating very large-scale DSSs; and 4) efficient rule generation for associative classification. We hope you enjoy all the contents in the book.

Intelligent prediction and decision support systems are based on signal processing, computer vision (CV), machine learning (ML), software engineering (SE), knowledge based systems (KBS), data mining, artificial intelligence (AI) and include several systems developed from the study of expert systems (ES), genetic algorithms (GA), artificial neural networks (ANN) and fuzzy-logic systems The use of automatic decision support systems in design and manufacturing industry, healthcare and commercial software development systems has the following benifits: Cost savings in companies, due to employment of expert system technology. Fast decision making, completion of projects in time and development of new products. Improvement in decision making capability and quality. Usage of Knowledge database and Preservation of expertise of individuals Eases complex decision problems. Ex: Diagnosis in Healthcare To address the issues and challenges related to development, implementation and application of automatic and intelligent prediction and decision support systems in domains such as manufacturing, healthcare and software product design, development and optimization, this book aims to collect and publish wide ranges of quality articles such as original research contributions, methodological reviews, survey papers, case studies and/or reports covering intelligent systems, expert prediction systems, evaluation models, decision support systems and Computer Aided Diagnosis (CAD).

A broad introduction to algorithms for decision making under uncertainty, introducing the underlying mathematical problem formulations and the algorithms for solving them. Automated decision-making systems or decision-support systems—used in applications that range from aircraft collision avoidance to breast cancer screening—must be designed to account for various sources of uncertainty while carefully balancing multiple objectives. This textbook provides a broad introduction to algorithms for decision making under uncertainty, covering the underlying mathematical problem formulations and the algorithms for solving them. The book first addresses the problem of reasoning about uncertainty and objectives in simple decisions at a single point in time, and then turns to sequential decision problems in stochastic environments where the outcomes of our actions are uncertain. It goes on to address model uncertainty, when we do not start with a known model and must learn how to act through interaction with the environment; state uncertainty, in which we do not know the current state of the environment due to imperfect perceptual information; and decision contexts involving multiple agents. The book focuses primarily on planning and reinforcement learning, although some of the techniques presented draw on elements of supervised learning and optimization. Algorithms are implemented in the Julia programming language. Figures, examples, and exercises convey the intuition behind the various approaches presented.

Leading technology scholars examine how networks powered by algorithms are transforming humanity, posing deep questions about power, freedom, and fairness. This title is also available as Open Access on Cambridge Core.

SMART SYSTEMS FOR INDUSTRIAL APPLICATIONS The prime objective of this book is to provide an insight into the role and advancements of artificial intelligence in electrical systems and future challenges. The book covers a broad range of topics about AI from a multidisciplinary point of view, starting with its history and continuing on to theories about artificial vs. human intelligence, concepts, and regulations concerning AI, human-machine distribution of power and control, delegation of decisions, the social and economic impact of AI, etc. The prominent role that AI plays in society by connecting people through technologies is highlighted in this book. It also covers key aspects of various AI applications in electrical systems in order to enable growth in electrical engineering. The impact that AI has on social and economic factors is also examined from various perspectives. Moreover, many intriguing aspects of AI techniques in different domains are covered such as e-learning, healthcare, smart grid, virtual assistance, etc. Audience The book will be of interest to researchers and postgraduate students in artificial intelligence, electrical and electronic engineering, as well as those engineers working in the application areas such as healthcare, energy systems, education, and others.

This book is a token of appreciation for Professor Gregory E. Kersten (1949–2020), one of the most prominent and active researchers and scholars in the broadly perceived field of collective decisions, notably negotiations, the author of numerous influential papers, books, and edited volumes, a great scientist, mentor, and a loyal friend and colleague. This book contains some papers in the fields of group and collective decisions, voting, social choice, negotiations, and related topics, with examples of real applications. The authors are top researchers and scholars from all over the world whose life and academic career has been inspired and influenced by Professor Kersten.

Decision support systems (DSS) have evolved over the past four decades from theoretical concepts into real world computerized applications. DSS architecture contains three key components: knowledge base, computerized model, and user interface. DSS simulate cognitive decision-making functions of humans based on artificial intelligence methodologies (including expert systems, data mining, machine learning, connectionism, logistical reasoning, etc.) in order to perform decision support functions. The applications of DSS cover many domains, ranging from aviation monitoring, transportation safety, clinical diagnosis, weather forecast, business management to internet search strategy. By combining knowledge bases with inference rules, DSS are able to provide suggestions to end users to improve decisions and outcomes. This book is written as a textbook so that it can be used in formal courses examining decision support systems. It may be used by both undergraduate and graduate students from diverse computer-related fields. It will also be of value to established professionals as a text for self-study or for reference.

This book aims to provide a new vision of how algorithms are the core of decision support systems (DSSs), which are increasingly important information systems that help to make decisions related to unstructured and semi-unstructured decision problems that do not have a simple solution from a human point of view. It begins with a discussion of how DSSs will be vital to improving the health of the population. The following article deals with how DSSs can be applied to improve the performance of people doing a specific task, like playing tennis. It continues with a work in which authors apply DSSs to insect pest management, together with an interactive platform for fitting data and carrying out spatial visualization. The next article improves how to reschedule trains whenever disturbances occur, together with an evaluation framework. The final works focus on different relevant areas of DSSs: 1) a comparison of ensemble and dimensionality reduction models based on an entropy criterion; 2) a radar emitter identification method based on semi-supervised and transfer learning; 3) design limitations, errors, and hazards in creating very large-scale DSSs; and 4) efficient rule generation for associative classification. We hope you enjoy all the contents in the book.