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.

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.

This book discusses recent research and applications about intelligent processing practices and tools for e-commerce data, information and knowledge. The authors first explain how advances in intelligent processing of data, information and knowledge that has wildly been used in e-commerce applications. They then show how this brings new opportunities and challenges for processing e-commerce data, information and knowledge. The book, made up of contributions from both academia and industry, aims to present advances in artificial intelligence to collect, process, and mining Data, information and knowledge, such as new algorithms and techniques in the field, foundational theory and systems, as well as practical e-commerce applications. Some of the topics discussed include AI for e-commerce, such as machine learning, deep learning; personalized service recommendation to e-commerce; modeling, description, and verification for data, information and knowledge; and task scheduling and performance optimization for large-scale concurrency.

This book constitutes the refereed post-conference proceedings of the 7th International Conference on IoT as a Service, IoTaaS 2021, which took place in Sydney, Australia, in December 2021. Due to COVID-19 pandemic the conference was held virtually. The 17 revised full papers were carefully reviewed and selected from 129 submissions. The papers are divided in content-related tracks: Intelligent IoT communication solutions; Social Internet of Things: security, management; Machine learning prediction and recommendation in IoT.

This book discusses recent research and applications about intelligent processing practices and tools for e-commerce data, information and knowledge. The authors first explain how advances in intelligent processing of data, information and knowledge that has wildly been used in e-commerce applications. They then show how this brings new opportunities and challenges for processing e-commerce data, information and knowledge. The book, made up of contributions from both academia and industry, aims to present advances in artificial intelligence to collect, process, and mining Data, information and knowledge, such as new algorithms and techniques in the field, foundational theory and systems, as well as practical e-commerce applications. Some of the topics discussed include AI for e-commerce, such as machine learning, deep learning; personalized service recommendation to e-commerce; modeling, description, and verification for data, information and knowledge; and task scheduling and performance optimization for large-scale concurrency.