The rapidly evolving business and technology landscape demands sophisticated decision-making tools to stay ahead of the curve. Advances in Complex Decision Making: Using Machine Learning and 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 applications 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. This 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"-- Provided by publisher Chapter 1 Application of Choquet–OWA Aggregation Operator to Fuse ELICIT Information Wen He, Wei Liang, Álvaro Labella and Rosa M. Rodríguez Chapter 2 GPipe: Using Adaptive Directed Acyclic Graphs to Run Data and Feature Pipelines with on-the-fly Transformations José Hélio de Brum Müller, Fethi Rabhi and Zoran Milosevic Chapter 3 Building an ESG Decision-Making System: Challenges and Research Directions Fethi Rabhi, Mingqin Yu, Alan Ng, Eric Lim, Felix Tan and Alan Hsiao Chapter 4 Analysing Trust, Security and Cost of Cloud Consumer’s Reviews using RNN, LSTM and GRU Muhammad Raheel Raza, Walayat Hussain and Mehdi Rajaeian Chapter 5 Interval Type-2 Fuzzy Decision Analysis Framework Based on Online Textual Reviews Xiao-Hong Pan, Shi-Fan He, Diego García-Zamora and Luis Martínez Chapter 6 Robust Comprehensive Minimum Cost Consensus Model for Multi-criteria Group Decision Making: Application in IoT Platform Selection Yefan Han, Bapi Dutta, Diego García-Zamora and Luis Martínez Index
