The rapidly evolving business and technology landscape demands sophisticated decision-making tools to stay ahead of the curve. The book Advances in Complex Decision Making: Using Machine Learning Tools for Service-Oriented Computing is a cutting-edge technical guide exploring the latest decision-making technology advancements. This book provides a comprehensive overview of machine learning algorithms and examines their application in complex decision-making systems in a service-oriented framework. The authors also delve into service-oriented computing and how it can be used to build complex systems that support decision-making. Many real-world examples are discussed in this book to provide a practical insight into how discussed techniques can be applied in various domains, including distributed computing, cloud computing, IoT and other online platforms. For researchers, students, data scientists and technical practitioners, this book offers a deep dive into the current developments of machine learning algorithms and their applications in service-oriented computing. The book discusses various topics, including - Fuzzy Decisions, ELICIT, OWA aggregation, Directed Acyclic Graph, RNN, LSTM, GRU, Type-2 Fuzzy Decision, Evidential Reasoning algorithm, and robust optimisation algorithms. This book is essential for anyone interested in the intersection of machine learning and service computing in complex decision-making systems.

The rapidly evolving business and technology landscape demands sophisticated decision-making tools to stay ahead of the curve. The book Advances in Complex Decision Making: Using Machine Learning Tools for Service-Oriented Computing is a cutting-edge technical guide exploring the latest decision-making technology advancements. This book provides a comprehensive overview of machine learning algorithms and examines their application in complex decision-making systems in a service-oriented framework. The authors also delve into service-oriented computing and how it can be used to build complex systems that support decision-making. Many real-world examples are discussed in this book to provide a practical insight into how discussed techniques can be applied in various domains, including distributed computing, cloud computing, IoT and other online platforms. For researchers, students, data scientists and technical practitioners, this book offers a deep dive into the current developments of machine learning algorithms and their applications in service-oriented computing. The book discusses various topics, including - Fuzzy Decisions, ELICIT, OWA aggregation, Directed Acyclic Graph, RNN, LSTM, GRU, Type-2 Fuzzy Decision, Evidential Reasoning algorithm, and robust optimisation algorithms. This book is essential for anyone interested in the intersection of machine learning and service computing in complex decision-making systems.