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    A novel model for mood changes on a critical event using emotional state and trait analysis and handwriting features
    (Computers and Industrial Engineering, 2014) Ugurlu, Bora; Güçlü Kandemir, Rembiye; Carus, Aydin; Abay, Ercan
    Our brain and sub consciousness actually form our character as a result of our habits. Handwriting which is the significant part of our personality is unique. It contains so much knowledge about us. It is possible to get a comprehension about an individual mood by examining handwriting. In the industry, some real-world applications are frequently used to determine personal identifications, to support employment in business, and to perform document transaction management and e-signature, which is a type of handwriting. The researches done to explore the relation between anxiety and handwriting are rarely computerized. Visual assessments of handwriting analysis for anxiety are common in the literature. According to visual assessments, there are so many debates. In this study, we investigated the relationship between exam anxiety of university students and handwriting in a computerized way. These students often have anxiety based on emotional state and trait. We recommend a novel model for mood changes, which is able to determine whether a student is anxious or not. Collaborative work of two distinct disciplines (i.e., computer science and medical science) makes results much more valuable. Our study is an important preliminary step of a complex mood change assessment system for industrial applications when a critical event occurs. The experimental results show that the proposed model is consistent and statistically significant, and also, is applicable to different fields of business area.
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    Öğe
    An Expert System for Determining the Emotional Change on a Critical Event Using Handwriting Features
    (Uikten - Assoc Information Communication Technology Education & Science, 2016) Ugurlu, Bora; Kandemir, Rembiye; Carus, Aydin; Abay, Ercan
    An individual may sometimes feel anxious when a critical event happens. Job interview, wedding, moving in a new city/country can result this occurrence. Examinations taken in school are also that kind of events. Since our handwriting is controlled by brain, it is possible to see clear changes in handwriting style during examinations. In our study, an expert system is developed which considers handwriting features to predict student's exam anxiety state. 210 handwriting samples are collected and classification is made by using J48 decision tree algorithm. The average of Precision, Recall and F-Measure metrics are 71%, 66% and 67%, respectively.