Machine Learning And Mechanics Based Soft Computing Applications
Download Machine Learning And Mechanics Based Soft Computing Applications PDF/ePub or read online books in Mobi eBooks. Click Download or Read Online button to get Machine Learning And Mechanics Based Soft Computing Applications book now. This website allows unlimited access to, at the time of writing, more than 1.5 million titles, including hundreds of thousands of titles in various foreign languages.
Machine Learning and Mechanics Based Soft Computing Applications
Author: Thi Dieu Linh Nguyen
language: en
Publisher: Springer Nature
Release Date: 2023-03-01
This book highlights recent advances in the area of machine learning and robotics-based soft computing applications. The book covers various artificial intelligence, machine learning, and mechanics, a mix of mechanical computational engineering work. The current computing era has a huge market/potential for machine learning, robotics, and soft computing techniques and their applications. With this in view, the book shares latest research and cutting-edge applications useful for professionals and researchers in these areas.
Soft Computing and Its Engineering Applications
The two-volume proceedings set CCIS 2430-2431 constitutes the revised selected papers of the 6th International Conference on Soft Computing and its Engineering Applications, icSoftComp 2024, held in Bangkok, Thailand, during December 10–12, 2024. The 58 full papers and 3 short papers included in this book were carefully reviewed and selected from 501 submissions. They were organized in topical sections as follows: Part I : Theory and Methods. Part II : Theory and Methods; Systems and Applications; Hybrid Techniques; Soft Computing for Smart World.
Soft Computing in Water Resources Engineering
Engineers have attempted to solve water resources engineering problems with the help of empirical, regression-based and numerical models. Empirical models are not universal, nor are regression-based models. The numerical models are, on the other hand, physics-based but require substantial data measurement and parameter estimation. Hence, there is a need to employ models that are robust, user-friendly, and practical and that do not have the shortcomings of the existing methods. Artificial intelligence methods meet this need. Soft Computing in Water Resources Engineering introduces the basics of artificial neural networks (ANN), fuzzy logic (FL) and genetic algorithms (GA). It gives details on the feed forward back propagation algorithm and also introduces neuro-fuzzy modelling to readers. Artificial intelligence method applications covered in the book include predicting and forecasting floods, predicting suspended sediment, predicting event-based flow hydrographs and sedimentographs, locating seepage path in an earth-fill dam body, and the predicting dispersion coefficient in natural channels. The author also provides an analysis comparing the artificial intelligence models and contemporary non-artificial intelligence methods (empirical, numerical, regression, etc.). The ANN, FL, and GA are fairly new methods in water resources engineering. The first publications appeared in the early 1990s and quite a few studies followed in the early 2000s. Although these methods are currently widely known in journal publications, they are still very new for many scientific readers and they are totally new for students, especially undergraduates. Numerical methods were first taught at the graduate level but are now taught at the undergraduate level. There are already a few graduate courses developed on AI methods in engineering and included in the graduate curriculum of some universities. It is expected that these courses, too, will soon be taught at the undergraduate levels.