Machine Learning Algorithms and Applications in Engineering/ Prasenjit Chatterjee & et al eds.

Includes Index

Machine learning for smart health care -- Predictive analysis for food risk mapping utilizing machine learning approach -- Machine learning techniques enabled electric vehicle -- A comparative analysis of established techniques and their applications in the field of gesture detection -- Brain-computer interface for dream visualization using deep learning -- Machine learning and data analysis based breast cancer classification – Accurate automatic functional recognition of proteins: overview and current computational challenges -- Taxonomy of shilling attack detection techniques in recommender system – Machine learning applications in real-world time series problem -- Prediction of selective laser sintering part quality using deep learning -- CBPP: an efficient algorithm for privacy-preserving data publishing of 1:m micro data with multiple sensitive attributes -- classification of network traffic on ISP link and analysis of network bandwidth during covid-19 -- Integration of AI/MI in 5G technology toward inteligent connectivity, security and challenges -- Electrical price prediction using machine learning algorithms -- Machine learning application to predict the degradation rate of biomedical implants -- Predicting the outcomes of myocardial infraction using neural decision forest -- Image classification using contrastive learning

Machine learning, one of the top emerging sciences, has an extremely broad range of applications. However, many books on the subject provide only a theoretical approach, making it difficult for a newcomer to grasp the subject material. This book provides a more practical approach by explaining the concepts of machine learning algorithms and describing the areas of application for each algorithm, using simple practical examples to demonstrate each algorithm and showing how different issues related to these algorithms are applied.