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- W4295954733 abstract "Many artificial neural models and deep neural models have been utilized for the prediction of personality using handwriting images. Results received from these models are on par with graphologists with an accuracy of 84%. This research work deals with the prediction of VIA Character classification of character strengths and virtues from handwriting images using a deep learning model. Correctly giving character strength score from handwriting image is the primary objective. This is the first known study that utilizes handwriting images for the prediction of VIA character score. Volunteers’ handwriting images will be gathered. Their VIA character score will be gathered from the VIA site. These data will be fed to a neural model for training. The trained model will be evaluated for the accuracy of the character score. After training the model for 360 epochs model gives us a loss of 23.029 overall for training, the lowest mean absolute error of 0.5800 is achieved by the forgiveness character, and the lowest mean absolute percentage error of 14.5788 is achieved by the forgiveness character." @default.
- W4295954733 created "2022-09-16" @default.
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- W4295954733 date "2022-06-23" @default.
- W4295954733 modified "2023-10-03" @default.
- W4295954733 title "Analysis of Character Strengths from Handwriting Samples using Deep Learning" @default.
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- W4295954733 doi "https://doi.org/10.1109/ic3sis54991.2022.9885568" @default.
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