Alexander Gegov – författare

This book highlights the latest research in computational intelligence and its applications. It covers both conventional and trending approaches in individual chapters on Fuzzy Systems, Intelligence in Robotics, Deep Learning Approaches, Optimization and Classification, Detection, Inference and Prediction, Hybrid Methods, Emerging Intelligence, Intelligent Health Care, and Engineering Data- and Model-Driven Applications. All chapters are based on peer-reviewed contributions presented at the 19th Annual UK Workshop on Computational Intelligence, held in Portsmouth, UK, on 4–6 September 2019.

The book offers a valuable reference guide for readers with expertise in computational intelligence or who are seeking a comprehensive and timely review of the latest trends in computational intelligence. Special emphasis is placed on novel methods and their use in a wide range of application areas, updating both academics and professionals on the state of the art.

This book introduces novel methods for leak and blockage detection in pipelines. The leak happens as a result of ageing pipelines or extreme pressure forced by operational error or valve rapid variation. Many factors influence blockage formation in pipes like wax deposition that leads to the formation and eventual growth of solid layers and deposition of suspended solid particles in the fluids. In this book, initially, different categories of leak detection are overviewed. Afterwards, the observability and controllability of pipeline systems are analysed. Control variables can be usually presented by pressure and flow rates at the start and end points of the pipe. Different cases are considered based on the selection of control variables to model the system. Several theorems are presented to test the observability and controllability of the system. In this book, the leakage flow in the pipelines is studied numerically to find the relationship between leakage flow and pressure difference. Removing leakage completely is almost impossible; hence, the development of a formal systematic leakage control policy is the most reliable approach to reducing leakage rates.

The ideas introduced in this book explore the relationships among rule based systems, machine learning and big data. Rule based systems are seen as a special type of expert systems, which can be built by using expert knowledge or learning from real data.

The book focuses on the development and evaluation of rule based systems in terms of accuracy, efficiency and interpretability. In particular, a unified framework for building rule based systems, which consists of the operations of rule generation, rule simplification and rule representation, is presented. Each of these operations is detailed using specific methods or techniques. In addition, this book also presents some ensemble learning frameworks for building ensemble rule based systems.

The book is a timely report on advanced methods and applications of computational intelligence systems. It covers a long list of interconnected research areas, such as fuzzy systems, neural networks, evolutionary computation, evolving systems and machine learning. The individual chapters are based on peer-reviewed contributions presented at the 16th Annual UK Workshop on Computational Intelligence, held on September 7-9, 2016, in Lancaster, UK. The book puts a special emphasis on novels methods and reports on their use in a wide range of applications areas, thus providing both academics and professionals with a comprehensive and timely overview of new trends in computational intelligence.