Paperback. 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. Discusses various applications of ML in engineering fields and the use of ML algorithms in solving challenging engineering problems ranging from biomedical 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. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. N� de ref. del art�culo 9780367612559

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T�tulo: Machine Learning Algorithms and Applications...

Editorial: Taylor & Francis Ltd, London

A�o de publicaci�n: 2025

Encuadernaci�n: Paperback

Condici�n: new

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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.

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Prasenjit Chatterjee is an Associate Professor of Mechanical Engineering Department at MCKV Institute of Engineering, India. He has published over 80 research papers in various international journals and has received numerous awards including Outstanding Researcher Award and University Gold Medal. He has been the Guest Editor of several special issues and has edited and authored several books on decision-making approaches and sustainability. He is the Lead Series Editor of International Perspectives on Decision Analysis and Operations Research, Emerald Group Publishing. Dr. Chatterjee is one of the developers of a new data-driven multiple-criteria decision-making method called Measurement of Alternatives and Ranking according to COmpromise Solution (MARCOS).

Morteza Yazdani works at Universidad Loyola Andalucia, Spain. Previously he finished his post-doctoral research at the University of Toulouse and was a lecturer the European University of Madrid. He participates in the editorial board of the International Journal of Decision Support System Technology and is a reviewer in different journals. His main research areas are decision-making modelling and fuzzy decision system in application of supply chain and energy systems and has published several journal articles.

Francisco de As�s Fern�ndez Navarro has earned his PhD in Computer Science and Artificial Intelligence from the University of Malaga, Spain. He also obtained a degree in Market Research from the Open University of Catalonia (UOC). He was awarded at the European Space Agency Noordwijk, The Netherlands with a postdoctoral fellowship in computational management and currently works as an Associate Professor at the Loyola University of Andalusia, Department of Quantitative Methods.

Javier P�rez-Rodr�guez earned his PhD in ICT from the University of Granada, Spain. In 2018 he joined the Department of Quantitative Methods at the University of Loyola, Andaluc�a as an Associate Professor. His research is focused on Computer Science and Artificial Intelligence and Bioinformatics. Within the area of machine learning, specifically, his works have been about pattern recognition and classification and has published several papers in reputable journals. His residency at the Institut f�r Mathematik und Informatik of the University of Greifswald, Germany was with Professor Stanke, who develops and maintains one of the most prestigious automatic gene recognition systems at present at an international level.

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