This book presents the latest research in the field of machine learning, discussing the real-world application problems associated with new innovative renewable energy methodologies as well as cutting edge technologies in the transport industry. The requirements and demands of problem solving have been increasing exponentially, and new artificial intelligence and machine learning technologies have reduced the scope of data coverage worldwide. Recent advances in data technology (DT) have contributed to reducing the gaps in the coverage of domains around the globe. Attention to clean energy in recent decades has been growing exponentially. This is mainly due to a decrease in the cost of both installed capacity of converters and a decrease in the cost of generated energy. Such successes were achieved thanks to the improvement of modern technologies for the production of converters, an increase in the efficiency of using incoming energy, optimisation of the operation of converters and analysis of data obtained during the operation of systems with the possibility of planning production. The use of clean energy plays an important role in the transportation industry, where technologies are also being improved from year to year - the transportation industry is growing, and machinery and systems are becoming more autonomous and robotic, where it is no longer possible to do without complex intelligent computing, machine learning optimisation, planning and working with large amounts of data. The book is a valuable reference work for researchers in the fields of renewable energy, computer science and engineering with a particular focus on machine learning and intelligent optimization as well as for postgraduates, managers, economists and decision makers, policy makers, government officials, industrialists and practicing scientists and engineers as well compassionate global decision makers. Topics include: Machine learning, Quantum Optimization, Modern Technology in Transport Industry, Innovative Technologies in Transport Education, Systems Based on Renewable Energy Conversion, Business Process Models and Applications in Renewable Energy, Clean Energy, and Climate Change.

Table of Contents:
Preface; RES-based Multipurpose Plant for Hydrogen Production; Developing a Bayesian Network to Model Environmental, Organizational, and Human Risk Factors: A Case Study on Wind Turbines; Digital Technologies for the Implementation of Intelligent Diagnostics of the Insulation of Power Supply Systems with Insulated Neutral in Operating Mode; Irrigation System of Agricultural Fields with the Use of Solar Energy; Strategies Hybrid Simulation for Regional Market Development of Renewable Energy; RES-Based Power Plants Versus Polluting Power Plants: Pros and Cons; A Comprehensive Study of System Building Blocks for Radio Frequency Energy Harvesting; The Management of Community Participation in Rural Infrastructure Development in the Mekong River Delta, Vietnam; Warning System for Cracked Pipes in Autonomous Vehicles; Contribution of Machine Learning to Rail Transport Safety; The Power of Variable Freeing and Variable Sum Bounds in Solving the Linear Knapsack Problem; Index.