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 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.
Chapter 1 Application of ChoquetOWA Aggregation Operator to Fuse ELICIT
Information
Wen He, Wei Liang, Įlvaro Labella and Rosa M. Rodrķguez
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 A. 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 Consumers 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 Garcķa-Zamora and Luis Martķnez
Chapter 6 Robust Comprehensive Minimum Cost Consensus Model for
Multi-criteria Group Decision Making: Application in IoT Platform Selection
Yefan Han, Bapi Dutta, Diego Garcķa-Zamora and Luis Martķnez
Walayat Hussain received his PhD in Computer Science from the University of Technology Sydney, Australia. Currently, he is the Head of the Information Technology Discipline at the PFBS, Australian Catholic University, Australia. His current research interests are cloud/edge computing, business intelligence, decision-support systems, AI and machine learning.
Honghao Gao is currently with the School of Computer Engineering and Science, Shanghai University, China. He is also a professor at the College of Future Industry, Gachon University, Korea. His research interests include software security, cloud/edge computing, intelligent data processing and AI4Healthcare. Prof. Gao is a fellow of the Institution of Engineering and Technology (IET) and a fellow of the British Computer Society (BCS).
Fethi Rabhi received a PhD in Computer Science at the University of Sheffield in 1990. He is a professor at the School of Computer Science and Engineering at the University of New South Wales in Australia, specialising in software engineering applied to Business Applications.
Luis Martķnez received MSc and PhD degrees in Computer Sciences, both from the University of Granada, Spain. He is a full professor in the Department of Computer Science at the University of Jaén. His current research interests are fuzzy decision making, fuzzy systems, decision-support systems, computing with words and recommender systems.
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