Advances in Complex Decision Making : Using Machine Learning and Tools for Service-Oriented Computing

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. Cover Half Title Title Page Copyright Page Table of Contents Preface Acknowledgements About the Editors List of Contributors Chapter 1: Application of Choquet–OWA Aggregation Operator to Fuse ELICIT Information 1.1 Introduction 1.2 Preliminaries 1.2.1 2-Tuple Linguistic Model 1.2.2 ELICIT Information 1.2.3 Choquet Integral 1.2.4 OWA Operator 1.3 A New Aggregation Operator for ELICIT Information 1.4 Properties of the ELICIT-Choquet-OWA Operator 1.5 A Novel MCDM Model 1.6 An Illustrative Example 1.7 Conclusions References Chapter 2: GPipe: Using Adaptive Directed Acyclic Graphs to Run Data and Feature Pipelines with on-the-fly Transformations 2.1 Introduction 2.2 Background and Related Work 2.3 Proposed Solution 2.3.1 Motivating Example 2.3.2 Representing Data and Feature Transformations using a DAG 2.3.3 Processing DAGs 2.4 Evaluation 2.4.1 Prototype Implementation 2.4.1.1 Transformers Module 2.4.1.2 Publishers Module 2.4.1.3 Pipeline Module 2.4.1.4 GPipe Module 2.4.2 Experimental Results 2.5 Conclusions and Future Work Notes References Chapter 3: Building an ESG Decision-Making System: Challenges and Research Directions 3.1 Introduction 3.2 ESG Background 3.3 ESG Research Challenges 3.3.1 ESG Strategic Direction Determination 3.3.2 Modelling and Architecting ESG Systems 3.3.3 ESG System DevOps Processes 3.4 ESG Decision-Making Systems 3.4.1 Data Collection Layer 3.4.2 Data Processing Layer 3.4.3 Application Layer 3.4.4 Example 1: Air Quality Monitoring 3.4.5 Example 2: Climate Change Financial Risk Management 3.5 Conclusions Acknowledgments References Chapter 4: Analysing Trust, Security and Cost of Cloud Consumer’s Reviews using RNN, LSTM, and GRU 4.1 Introduction 4.2 Related Works 4.2.1 Aspect-Based Sentiment Classification 4.2.2 Cloud Computing Service Selection 4.3 Deep Learning Approaches 4.3.1 Recurrent Neural Network (RNN) 4.3.2 Long Short-Term Memory (LSTM) 4.3.3 Gated Recurrent Unit (GRU) 4.4 Experimentation 4.4.1 Dataset 4.4.2 Aspect Extraction 4.4.3 Hyper-parameters Configuration 4.5 Results 4.6 Conclusion References Chapter 5: Interval Type-2 Fuzzy Decision Analysis Framework Based on Online Textual Reviews 5.1 Introduction 5.2 Preliminaries 5.2.1 Interval Type-2 Fuzzy Sets 5.2.2 Entropy-Based Weight Determination Model 5.2.3 Evidential Reasoning Algorithm 5.2.4 Minimax Regret Approach 5.3 An Online Review-Based Interval Type-2 Fuzzy decision-making Method 5.3.1 Distributed Structure-Based Online Review Processing Mechanism 5.3.2 An Entropy-Based Interval Type-2 Fuzzy Weights Determination Model 5.3.3 An Evidential Reasoning-Based Information Fusion Approach 5.3.4 An Improved Minimax Regret Approach 5.4 Case Study 5.4.1 Problem Description 5.4.2 Implementation Process 5.5 Conclusions References Chapter 6: Robust Comprehensive Minimum Cost Consensus Model for Multi-Criteria Group Decision Making: Application in IoT Platform Selection 6.1 Introduction 6.2 Preliminaries 6.2.1 Multi-Criteria Group Decision Making 6.2.2 Consensus Reaching Process 6.2.3 Minimum Cost Consensus Models 6.2.4 Robust Optimization 6.3 Robust Comprehensive Minimum Cost Consensus for Multi-Criteria Decision Making 6.4 Case Study 6.4.1 Numerical Experiment 6.4.2 Sensitivity Analysis 6.5 Conclusions Acknowledgments References Index "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