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BibTex Citation Data :
@article{JOIV242,
author = {Abdusalam Shaltooki and Mojtaba Jamshidi},
title = {The Use of Data Mining Techniques in Predicting the Noise Emitted By the Trailing Edge of Aerodynamic Objects},
journal = {JOIV : International Journal on Informatics Visualization},
volume = {3},
number = {4},
year = {2019},
keywords = {Data mining; Classification; Hybrid model; Noise prediction; Aerodynamic objects.},
abstract = {Aerodynamic is a branch of fluid dynamics that evaluates the behavior of airflow and its interaction with moving objects. The most important application of aerodynamic is in aerospace engineering, designing and construction of flying objects. Reduction of noise emitted by aerodynamic objects is one of the most important challenges in this area and many efforts have been to reduce its negative effects. The prediction of noise emitted from these aerodynamic objects is a low-cost and fast approach that can partially replace the \"fabrication and testing\" phase. One of the most common and successful tools in prediction procedures is data mining technology. In this paper, the performance of different data mining algorithms such as Random Forest, J48, RBF Network, SVM, MLP, Logistic, and Bagging is evaluated in predicting the amount of noise emitted from aerodynamic objects. The experiments are conducted on a dataset collected by NASA, which is called \"Airfoil Self-Noise\". The obtained results illustrate that the proposed hybrid model derived from the combination of Random Forest and Bagging algorithms has better performance compared to other methods with an accuracy of 77.6% and mean absolute error of 0.2279.},
issn = {2549-9904}, pages = {388--393}, doi = {10.30630/joiv.3.4.242},
url = {https://joiv.org/index.php/joiv/article/view/242}
}
Refworks Citation Data :
@article{{JOIV}{242}, author = {Shaltooki, A., Jamshidi, M.}, title = {The Use of Data Mining Techniques in Predicting the Noise Emitted By the Trailing Edge of Aerodynamic Objects}, journal = {JOIV : International Journal on Informatics Visualization}, volume = {3}, number = {4}, year = {2019}, doi = {10.30630/joiv.3.4.242}, url = {} }Refbacks
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JOIV : International Journal on Informatics Visualization
ISSN 2549-9610 (print) | 2549-9904 (online)
Organized by Department of Information Technology - Politeknik Negeri Padang, and Institute of Visual Informatics - UKM and Soft Computing and Data Mining Centre - UTHM
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is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.