Artificial neural networks for predicting the performance of novice CAD users based on their profiled technical attributes

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dc.contributor.author Ammouri, A.H.
dc.contributor.author Hamade, R.F.
dc.contributor.author Artail, H.A.
dc.date.accessioned 2017-05-26T09:57:01Z
dc.date.available 2017-05-26T09:57:01Z
dc.date.issued 2017-05-26
dc.identifier.isbn 978-0-7918-5489-1 en_US
dc.identifier.uri http://hdl.handle.net/10725/5672
dc.description.abstract This paper utilizes Artificial Neural Networks (ANN) to forecast the mechanical CAD performance of novice trainees involved in formal training. We utilize 3 Artificial Neural Networks, ANN, techniques: Feed-Forward Backpropagation, Elman Backpropagation, and Generalized Regression. We also compare their predictive capabilities compared to those of linear regression techniques. For this purpose, two kinds of data are utilized as input vectors for the predictive techniques: performance data and trainee attributes data. Such data has been previously published by Hamade and coworkers. Performance data is based on the following four quantitative measures of performance: (1) construction speed of the CAD model, (2) sophistication of the constructed CAD model, and the rates of improvement of (3) construction speed and (4) model sophistication. Trainees’ attributes identified as related to building CAD competence include: (1) technical and (2) character attributes and (3) learning styles. Strong correlations have been found between many of the trainees’ profiled attributes and trainee’s actual performance throughout and upon the conclusion of the training. Generally, the ANN methods as well as the linear regression techniques were found to be successful in discriminating the trainees based on their profiled attributes. However, the findings suggest that, of the networks considered, the Generalized Regression Neural Network (GRNN) gave the best prediction results by yielding the least prediction error practically across all performance measures. Therefore, GRNN can be used to predict the performance of the novice CAD users. This capability may be used to pre-assess the development of CAD skills as training progresses and may serve as basis to develop custom CAD training programs and to improve the efficiency and effectiveness of CAD training. en_US
dc.language.iso en en_US
dc.publisher ASME en_US
dc.title Artificial neural networks for predicting the performance of novice CAD users based on their profiled technical attributes en_US
dc.type Conference Paper / Proceeding en_US
dc.author.school SOE en_US
dc.author.idnumber 201306463 en_US
dc.author.department Industrial And Mechanical Engineering en_US
dc.description.embargo N/A en_US
dc.keywords Computer-aided design en_US
dc.keywords Artificial neural networks en_US
dc.identifier.doi http://dx.doi.org/10.1115/IMECE2011-63409 en_US
dc.identifier.ctation Hamade, R. F., Ammouri, A. H., & Artail, H. A. (2011, January). Artificial Neural Networks for Predicting the Performance of Novice CAD Users Based on Their Profiled Technical Attributes. In ASME 2011 International Mechanical Engineering Congress and Exposition (pp. 97-103). American Society of Mechanical Engineers. en_US
dc.author.email ali.ammouri@lau.edu.lb en_US
dc.conference.date November 11–17, 2011 en_US
dc.conference.pages 97-103 en_US
dc.conference.place Denver, Colorado, USA en_US
dc.conference.title ASME 2011 International Mechanical Engineering Congress and Exposition en_US
dc.identifier.tou http://libraries.lau.edu.lb/research/laur/terms-of-use/articles.php en_US
dc.identifier.url http://proceedings.asmedigitalcollection.asme.org/proceeding.aspx?articleid=1642727 en_US
dc.volume 3: Design and Manufacturing en_US
dc.author.affiliation Lebanese American University en_US

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