The book focuses on a set of cutting-edge research techniques, highlighting the potential of soft computing tools in the analysis of economic and financial phenomena and in providing support for the decision-making process. In the first part the textbook presents a comprehensive and self-contained introduction to the field of self-organizing maps, elastic maps and social network analysis tools and provides necessary background material on the topic, including a discussion of more recent developments in the field. In the second part the focus is on practical applications, with particular attention paid to budgeting problems, market simulations, and decision-making processes, and on how such problems can be effectively managed by developing proper methods to automatically detect certain patterns. The book offers a valuable resource for both students and practitioners with an introductory-level college math background.

This book offers a comprehensive reference guide to operations research theory and applications in health care systems. It provides readers with all the necessary tools for solving health care problems. The respective chapters, written by prominent researchers, explain a wealth of both basic and advanced concepts of operations research for the management of operating rooms, intensive care units, supply chain, emergency medical service, human resources, lean health care, and procurement. To foster a better understanding, the chapters include relevant examples or case studies. Taken together, they form an excellent reference guide for researchers, lecturers and postgraduate students pursuing research on health care management problems. The book presents a dynamic snapshot on the field that is expected to stimulate new directions and stimulate new ideas and developments.

This book discusses the psychological traits associated with drug consumption through the statistical analysis of a new database with information on 1885 respondents and use of 18 drugs. After reviewing published works on the psychological profiles of drug users and describing the data mining and machine learning methods used, it demonstrates that the personality traits (five factor model, impulsivity, and sensation seeking) together with simple demographic data make it possible to predict the risk of consumption of individual drugs with a sensitivity and specificity above 70% for most drugs. It also analyzes the correlations of use of different substances and describes the groups of drugs with correlated use, identifying significant differences in personality profiles for users of different drugs. The book is intended for advanced undergraduates and first-year PhD students, as well as researchers and practitioners. Although no previous knowledge of machine learning, advanced data mining concepts or modern psychology of personality is assumed, familiarity with basic statistics and some experience in the use of probabilities would be helpful. For a more detailed introduction to statistical methods, the book provides recommendations for undergraduate textbooks.

Integrating Artificial Intelligence (AI) presents immense opportunities and daunting challenges in the rapidly evolving finance landscape as AI-driven algorithms and models revolutionize decision-making and enhance efficiency, concerns about bias, ethics, and security loom. Financial institutions must navigate these complexities responsibly while leveraging AI's potential to innovate and thrive. Risks and Challenges of AI-Driven Finance: Bias, Ethics, and Security guides this dynamic environment. Written for professionals, researchers, policymakers, and students, this book comprehensively explores AI's impact on finance. It delves into the intricacies of bias in algorithms, ethical frameworks, cybersecurity, and regulatory compliance, offering actionable insights to address these critical issues.

The increasing complexity of financial problems and the enormous volume of financial data often make it difficult to apply traditional modeling and algorithmic procedures. In this context, the field of computational intelligence provides an arsenal of particularly useful techniques. These techniques include new modeling tools for decision making under risk and uncertainty, data mining techniques for analyzing complex data bases, and powerful algorithms for complex optimization problems. Computational intelligence has also evolved rapidly over the past few years and it is now one of the most active fields in operations research and computer science. This volume presents the recent advances of the use of computation intelligence in financial decision making. The book covers all the major areas of computational intelligence and a wide range of problems in finance, such as portfolio optimization, credit risk analysis, asset valuation, financial forecasting, and trading.