Prediction of Geometrical Characteristics of an Inclined Negatively Buoyant Jet Using Group Method of Data Handling (GMDH) Neural Network

Autor: Hassan Alfaifi, Hossein Bonakdari

Publikováno v: Fluids, Vol 9, Iss 9, p 198 (2024)

A new approach to predicting the geometrical characteristics of the mixing behavior of an inclined dense jet for angles ranging from 15° to 85° is proposed in this study. This approach is called the group method of data handling (GMDH) and is based

Externí odkaz: https://doaj.org/article/09073bcc80304cbab921e2cb2d484d8b

Exploring Predictive Variables Affecting the Sales of Companies Listed With Korean Stock Indices Through Machine Learning Analysis

Autor: Gwangsu Lee

Publikováno v: IEEE Access, Vol 11, Pp 63534-63549 (2023)

This study uses machine learning algorithms to explore predictor variables that determine whether the national statistical indices managed and announced by the Korean government influence the sales of companies listed on the Korea Composite Stock Pri

Externí odkaz: https://doaj.org/article/1ae9c1e5916741a08a5da9cc930c0941

Generalized regression neural network application for fault type detection in distribution transformer windings considering statistical indices

Autor: Behkam, Reza, Karami, Hossein, Salay Naderi, Mehdi, B. Gharehpetian, Gevork

Publikováno v: COMPEL -The international journal for computation and mathematics in electrical and electronic engineering, 2021, Vol. 41, Issue 1, pp. 381-409.

Externí odkaz: http://www.emeraldinsight.com/doi/10.1108/COMPEL-06-2021-0199

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