TY  - BOOK
AU  - Hussain,Walayat
AU  - Gao,Honghao
AU  - Rabhi,Fethi
AU  - Martínez,Luis
TI  - ADVANCES IN COMPLEX DECISION MAKING: using machine learning and tools for service-oriented computing 
SN  - 9781000993950
AV  - Q325.5 
PY  - 2023///
CY  - [S.l.]
PB  - CHAPMAN & HALL CRC
KW  - BUSINESS & ECONOMICS / Decision-Making & Problem Solving
KW  - bisacsh
KW  - COMPUTERS / Enterprise Applications
KW  - Machine learning
KW  - Decision making
KW  - Data processing
KW  - Service-oriented architecture (Computer science)
N2  - 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
UR  - https://www.taylorfrancis.com/books/9781003340621
UR  - http://www.oclc.org/content/dam/oclc/forms/terms/vbrl-201703.pdf
ER  -