Personality Analysis through Handwriting Detection Using Android Based Mobile Device
waskitha wijaya,Herman Tolle,Fitri Utaminingrum +2 more
- Vol. 2, Iss: 2
TLDR
This study was conducted by taking 42 samples of handwriting from different backgrounds and showed the accurate average of the application reached 82.738%.Abstract:
Graphology is one of the psychology disciplines which aims to study the personality traits of individuals through interpretation of handwriting. We can get information of one’s personality through graphology. In addition, by using android based mobile device, graphology analysis could show one’s personality faster. This study was conducted by taking 42 samples of handwriting from different backgrounds. The feature used in this study was handwriting margin. Besides, Support Vector Machine method was employed to classify the result feature from extraction process. The result of this study showed the accurate average of the application reached 82.738%.read more
Citations
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Journal ArticleDOI
Survey on handwriting-based personality trait identification
Kinjal Chaudhari,Ankit Thakkar +1 more
TL;DR: Links between handwriting and personality psychology are presented and the use of computer-based graphology for personality prediction is encouraged and applications of graphology in various fields are discussed.
Journal ArticleDOI
A Hybrid CNN-LSTM Model for Psychopathic Class Detection from Tweeter Users
TL;DR: The proposed hybrid CNN-LSTM model was able to yield a good classification accuracy of 91.67% and a large-sized benchmark dataset was acquired for the effective classification of the given input text into psychopath vs. non-psychopath classes, thereby enabling persons with such personality traits to be identified.
Journal ArticleDOI
Detection and Classification of Psychopathic Personality Trait from Social Media Text Using Deep Learning Model
Junaid Asghar,Saima Akbar,Muhammad Zubair Asghar,Bashir Ahmad,Mabrook Al-Rakhami,Abdu Gumaei,Abdu Gumaei +6 more
TL;DR: In this article, an attention-based BILSTM was used to classify the input text into psychopath and non-psychopath traits for detecting psychopaths in text analytics domain.
Proceedings ArticleDOI
Personality Features Identification from Handwriting Using Convolutional Neural Networks
TL;DR: This research conducted using both techniques of structural and symbol analysis on handwriting structurally as a unit, using four specific letters analyzed using the Convolutional Neural Networks (CNN) classification approach.
Proceedings ArticleDOI
Applying Deep Neural Networks for Predicting Dark Triad Personality Trait of Online Users
TL;DR: This work implements a deep neural network model, namely BILSTM for the efficient prediction of dark triad (psychopath) personality traits regarding online users, and experimental results depict that the proposed model attained an improved AUC when compared to the baseline study.
References
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Journal ArticleDOI
Support-Vector Networks
Corinna Cortes,Vladimir Vapnik +1 more
TL;DR: High generalization ability of support-vector networks utilizing polynomial input transformations is demonstrated and the performance of the support- vector network is compared to various classical learning algorithms that all took part in a benchmark study of Optical Character Recognition.
A Comparison of Methods for Multi-class Support Vector Machines
Hsu Chih-Wei,Chih-Jen Lin +1 more
TL;DR: These experiments indicate that the “one-against-one” and DAG methods are more suitable for practical use than the other methods, and show that for large problems methods by considering all data at once in general need fewer support vectors.
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