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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.
