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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.
750 kr
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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.
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26th International Conference, Jaén, Spain, November 13–15, 2025, Proceedings, Part I
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26th International Conference, Jaén, Spain, November 13–15, 2025, Proceedings, Part II
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This book examines one of the more common and wide-spread methodologies to deal with uncertainty in real-world decision making problems, the computing with words paradigm, and the fuzzy linguistic approach. The 2-tuple linguistic model is the most popular methodology for computing with words (CWW), because it improves the accuracy of the linguistic computations and keeps the interpretability of the results.
The authors provide a thorough review of the specialized literature in CWW and highlight the rapid growth and applicability of the 2-tuple linguistic model. They explore the foundations and methodologies for CWW in complex frameworks and extensions. The book introduces the software FLINTSTONES that provides tools for solving linguistic decision problems based on the 2-tuple linguistic model.
Professionals and researchers working in the field of classification or fuzzy sets and systems will find The 2-tuple Linguistic Model: Computing with Words in Decision Making a valuable resource. Undergraduate and postdoctoral students studying computer science and statistics will also find this book a useful study guide.
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