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9798369372500: Expert Artificial Neural Network Applications for Science and Engineering

Synopsis

Advancements in Artificial Neural Networks (ANN), machine learning, and deep learning are transforming the way complex science and engineering problems are addressed, offering solutions where traditional methods fall short. These technologies enable accurate modeling and analysis in areas such as heat transfer, desalination processes, pollutant biodegradability, and material science, contributing to sustainable development and innovative engineering practices. By applying these methods, researchers can enhance efficiency, optimize resource use, and tackle pressing environmental challenges. This integration of advanced computational tools into real-world applications represents a significant leap forward in addressing multidisciplinary engineering and scientific challenges. Expert Artificial Neural Network Applications for Science and Engineering provides a complete understanding of the ANNs for engineering practices. It discusses current developments in solving complicated engineering problems that cannot be solved using traditional methods. Covering topics such as industrial equipment reliability, manufacturing processes, and air quality forecasting, this book is an excellent resource for mechanical engineers, chemical engineers, civil engineers, electrical engineers, biomedical engineers, computer scientists, professionals, researchers, scholars, academicians, and more.

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À propos des auteurs

Dr. Lingala Syam Sundar is currently working at Prince Mohammed Bin Fahd University, Kingdom of Saudi Arabia. Dr. Syam Sundar obtained his doctorate in the field of Energy Systems from Jawaharlal Nehru Technological University, Hyderabad, India in 2010 and Post Doc Research Fellow from University of Aveiro, 2016 and M. Tech in Thermal Engineering from Jawaharlal Nehru Technological University, Hyderabad, India in 2003 and B. Tech in Mechanical Engineering from Andhra University, India in 1998. Dr. Syam Sundar has an expertise in Thermal process engineering, power plant design and Nanofluids heat transfer, Synthesis of hybrid nanoparticles, Thermophysical properties analysis, CFD analysis and Finite Element Method analysis. He has more than 110 publications to his credit in journals and conferences of international repute and supervised 2 PhD research works with another 2 on-going. He is the top 100 thousand or in the top 2 per cent of the most cited researchers in the world throughout their career and in their scientific field, according to a Stanford University study signed by the team led by John P.A. Ioannidis.

Deepanraj Balakrishnan is currently working as Research Faculty in the Department of Mechanical Engineering, College of Engineering at Prince Mohammad Bin Fahd University, Saudi Arabia. Dr. Deepanraj received his Bachelor's degree in Mechanical Engineering and Master's Degree in Thermal Engineering from Anna University, Chennai and Ph.D degree in Mechanical Engineering from National Institute of Technology, Calicut. He also completed his MBA in Energy Management from Jaipur National University. He has more than10 years of working experience, which include both teaching and research. Dr. Deepanraj has published more than 100 research articles in peer reviewed international journals and conferences and holds 2 Indian patent. He is serving as guest editor, editorial board member and reviewer for many international peer reviewed journals including well known publishers like Elsevier, Springer and Inderscience. His area of interest includes Renewable Energy Utilization; Energy Conservation and Waste Management with special focus on Bio-Energy.

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Sundar, Lingala Syam (EDT); Balakrishnan, Deepanraj (EDT); Sousa, Antonio C. M. (EDT)
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Hardcover. Etat : new. Hardcover. Advancements in Artificial Neural Networks (ANN), machine learning, and deep learning are transforming the way complex science and engineering problems are addressed, offering solutions where traditional methods fall short. These technologies enable accurate modeling and analysis in areas such as heat transfer, desalination processes, pollutant biodegradability, and material science, contributing to sustainable development and innovative engineering practices. By applying these methods, researchers can enhance efficiency, optimize resource use, and tackle pressing environmental challenges. This integration of advanced computational tools into real-world applications represents a significant leap forward in addressing multidisciplinary engineering and scientific challenges. Expert Artificial Neural Network Applications for Science and Engineering provides a complete understanding of the ANNs for engineering practices. It discusses current developments in solving complicated engineering problems that cannot be solved using traditional methods. Covering topics such as industrial equipment reliability, manufacturing processes, and air quality forecasting, this book is an excellent resource for mechanical engineers, chemical engineers, civil engineers, electrical engineers, biomedical engineers, computer scientists, professionals, researchers, scholars, academicians, and more. "The goal of this book is to use an expert Artificial Neural Network (ANN) to solve various science and engineering application problems and to discuss current developments in solving complicated engineering problems that cannot be solved using traditional methods"-- Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. N° de réf. du vendeur 9798369372500

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Sundar, Lingala Syam (EDT); Balakrishnan, Deepanraj (EDT); Sousa, Antonio C. M. (EDT)
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Sundar, Lingala Syam (EDT); Balakrishnan, Deepanraj (EDT); Sousa, Antonio C. M. (EDT)
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Hardcover. Etat : new. Hardcover. Advancements in Artificial Neural Networks (ANN), machine learning, and deep learning are transforming the way complex science and engineering problems are addressed, offering solutions where traditional methods fall short. These technologies enable accurate modeling and analysis in areas such as heat transfer, desalination processes, pollutant biodegradability, and material science, contributing to sustainable development and innovative engineering practices. By applying these methods, researchers can enhance efficiency, optimize resource use, and tackle pressing environmental challenges. This integration of advanced computational tools into real-world applications represents a significant leap forward in addressing multidisciplinary engineering and scientific challenges. Expert Artificial Neural Network Applications for Science and Engineering provides a complete understanding of the ANNs for engineering practices. It discusses current developments in solving complicated engineering problems that cannot be solved using traditional methods. Covering topics such as industrial equipment reliability, manufacturing processes, and air quality forecasting, this book is an excellent resource for mechanical engineers, chemical engineers, civil engineers, electrical engineers, biomedical engineers, computer scientists, professionals, researchers, scholars, academicians, and more. "The goal of this book is to use an expert Artificial Neural Network (ANN) to solve various science and engineering application problems and to discuss current developments in solving complicated engineering problems that cannot be solved using traditional methods"-- Shipping may be from multiple locations in the US or from the UK, depending on stock availability. N° de réf. du vendeur 9798369372500

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Hardcover. Etat : new. Hardcover. Advancements in Artificial Neural Networks (ANN), machine learning, and deep learning are transforming the way complex science and engineering problems are addressed, offering solutions where traditional methods fall short. These technologies enable accurate modeling and analysis in areas such as heat transfer, desalination processes, pollutant biodegradability, and material science, contributing to sustainable development and innovative engineering practices. By applying these methods, researchers can enhance efficiency, optimize resource use, and tackle pressing environmental challenges. This integration of advanced computational tools into real-world applications represents a significant leap forward in addressing multidisciplinary engineering and scientific challenges. Expert Artificial Neural Network Applications for Science and Engineering provides a complete understanding of the ANNs for engineering practices. It discusses current developments in solving complicated engineering problems that cannot be solved using traditional methods. Covering topics such as industrial equipment reliability, manufacturing processes, and air quality forecasting, this book is an excellent resource for mechanical engineers, chemical engineers, civil engineers, electrical engineers, biomedical engineers, computer scientists, professionals, researchers, scholars, academicians, and more. "The goal of this book is to use an expert Artificial Neural Network (ANN) to solve various science and engineering application problems and to discuss current developments in solving complicated engineering problems that cannot be solved using traditional methods"-- Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability. N° de réf. du vendeur 9798369372500

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