Description
Taylor and Francis Ltd Machine Learning Algorithms and Applications in Engineering 1st Edition 2022 Hardbound by Chatterjee, Prasenjit
Machine Learning (ML) is a sub field of artificial intelligence that uses soft computing and algorithms to enable computers to learn on their own and identify patterns in observed data, build models that explain the world, and predict things without having explicit pre-programmed rules and models. This book discusses various applications of ML in engineering fields and the use of ML algorithms in solving challenging engineering problems ranging from biomedical, transport, supply chain and logistics, to manufacturing and industrial. Through numerous case studies, it will assist researchers and practitioners in selecting the correct options and strategies for managing organizational tasks._x000D_ _x000D_
Introduction. Fundamentals of Machine Learning. Machine Learning Algorithms: Theoretical and Mathematical Aspects. An Exhaustive Review of Applications of Machine Learning Applications in Engineering. Applications in Engineering. Design of Highway and Transportation Engineering for the Prediction of Transport Arrivals & Pedestrian Movement Analysis / Traffic Pattern / Congestion Management. Use of Machine Learning in Construction, Surveying, Geo Technical and Geo-Spatial Engineering / Seismic Data Analysis. Machine Learning for Industrial Automation / Smart Grid Management / Driver Monitoring Systems / Autonomous Vehicles. Machine Learning in Grid Integration and Power Distribution / Control and Feedback System / Power Quality / Power Usage Analysis. Machine Learning in Robotics and Intelligent Machines. Machine Learning for Predictive Maintenance and Condition Monitoring / Reliability Engineering. Use of IoT and Big Data Analytics in Manufacturing / Demand Forecasting / Process Optimization / Inventory Planning / Fault Diagnosis for Shop Floor Machinery. Machine Learning for Carbon Emission / Environmental Engineering. Machine Learning for Renewable Energy Policy. Machine Learning in Biomedical Engineering._x000D_