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Handbook of Research on Machine Learning 1st Edition 2022 Hardbound at Meripustak

Handbook of Research on Machine Learning 1st Edition 2022 Hardbound by Mangla, Monika, Taylor and Francis Ltd

Books from same Author: Mangla, Monika

Books from same Publisher: Taylor and Francis Ltd

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  • General Information  
    Author(s)Mangla, Monika
    PublisherTaylor and Francis Ltd
    Edition1st Edition
    ISBN9781774638682
    Pages564
    BindingHardbound
    LanguageEnglish
    Publish YearAugust 2022

    Description

    Taylor and Francis Ltd Handbook of Research on Machine Learning 1st Edition 2022 Hardbound by Mangla, Monika

    This volume takes the reader on a technological voyage of machine learning advancements, highlighting the systematic changes in algorithms, challenges, and constraints. The technological advancements in the ML arena have transformed and revolutionized several fields, including transportation, agriculture, finance, weather monitoring, and others. This book brings together researchers, authors, industrialists, and academicians to cover a vast selection of topics in ML, starting with the rudiments of machine learning approaches and going on to specific applications in healthcare and industrial automation.The book begins with an overview of the ethics, security and privacy issues, future directions, and challenges in machine learning as well as a systematic review of deep learning techniques and provides an understanding of building generative adversarial networks. Chapters explore predictive data analytics for health issues. The book also adds a macro dimension by highlighting the industrial applications of machine learning, such as in the steel industry, for urban information retrieval, in garbage detection, in measuring air pollution, for stock market predictions, for underwater fish detection, as a fake news predictor, and more. PART 1: RUDIMENTS OF MACHINE LEARNING APPROACHES, 1. Ethics in AI in Machine Learning, 2. Advances in Artificial Intelligence Models for Providing Security and Privacy Using Machine Learning Techniques, 3. A Systematic Review of Deep Learning Techniques for Semantic Image Segmentation: Methods, Future Directions, and Challenges, 4. Covariate Shift in Machine Learning, 5. Understanding and Building Generative Adversarial Networks, PART 2: APPLICATION OF MACHINE LEARNING IN HEALTHCARE, 6. Machine Learning in Healthcare: Applications, Current Status, and Future Prospectus, 7. Employing Machine Learning for Predictive Data Analytics in Healthcare, 8. Prediction of Heart Disease Using Machine Learning, 9. Detection of Infectious Diseases in Human Bodies by Using Machine Learning Algorithms, 10. Medical Review Analytics Using Social Media, 11. Time Series Forecasting Techniques for Infectious Disease Prediction, PART 3: TOWARDS INDUSTRIAL AUTOMATION THROUGH MACHINE LEARNING, 12. Machine Learning in the Steel Industry, 13. Experiments Synergizing Machine Learning Approaches with Geospatial Big Data for Improved Urban Information Retrieval, 14. Garbage Detection Using SURF Algorithm Based on Merchandise Marker, 15. Evolution of Long Short-Term Memory (LSTM) in Air Pollution Forecasting, 16. Application of Machine Learning in Stock Market Prediction, 17. Deep Learning Model for Stochastic Analysis and Time-Series Forecasting of Indian Stock Market, 18. Enhanced Fish Detection in Underwater Video Using Wavelet-Based Color Correction and Machine Learning, 19. Fake News Predictor Model-Based on Machine Learning and Natural Language Processing, 20. Machine Learning on Simulation Tools for Underwater Sensor Network, 21. Prediction and Analysis of Heritage Monuments Images Using Machine Learning Techniques



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