University of Bahrain
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Hybrid Intelligent Technique with Deep Learning to Classify Personality Traits

Show simple item record Ibrahim, Ruba Talal Ramo, Fawziya Mahmood 2023-01-29T18:38:56Z 2023-01-29T18:38:56Z 2023-01-29
dc.identifier.issn 2210-142X
dc.description.abstract The importance of personality traits in education, employment, and disease detection has prompted several studies to construct intelligent systems that can identify any individual’s personality traits based on their signature, face, handwriting, etc. As is well known, signatures play a significant legal role in document authentication. Thus, graphology shows that the study of the signature features aids in the prediction of personality traits.This paper examines two models to categorize an individual’s personality traits into five groups using the big five-factor.Much preprocessing is applied to data training and testing.The analysis of images is based on 6646 images in total and then split into 5315 for training and 1331 for testing.The first model is a designed convolution neural network(CNN) with five main layers and initializing hyperparameters in a good manner. The second method involves combining deep learning with fuzzy learning (FD5NN) to overcome data determinism and ambiguity.The results showed that both models produced good results. The designed CNN and hybrid FD5NN had accuracy rates of 0.93% and 0.97%, respectively. We conclude that whether deep learning is used alone or in hybridization with other representations, we will get the best results in extracting and classifying features from image signatures. en_US
dc.language.iso en en_US
dc.publisher University of Bahrain en_US
dc.subject Personality Traits, Deep Learning, Fuzzy Learning, CNN, Signature. en_US
dc.title Hybrid Intelligent Technique with Deep Learning to Classify Personality Traits en_US
dc.type Article en_US
dc.volume 13 en_US
dc.issue 1 en_US
dc.pagestart 231 en_US
dc.pageend 244 en_US
dc.contributor.authorcountry Iraq en_US
dc.contributor.authoraffiliation Department of Computer Science, College of Computer Science and Mathematics, University of Mosul, Iraq en_US
dc.source.title International Journal of Computing and Digital Systems en_US
dc.abbreviatedsourcetitle IJCDS en_US

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