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    <name>Article</name>
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          <name>Title</name>
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              <text>Classification of Psychological Disorders by Feature Ranking and Fusion using Gradient Boosting Classification of Psychological Disorders</text>
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          <name>Subject</name>
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              <text>Electroencephalograph (EEG); gradient boosting classifier; gridSearchCV; negative state emotions; psychological disorders</text>
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              <text>Negative emotional regulation is a defining element of psychological disorders. Our goal was to create a machine-learning model to classify psychological disorders based on negative emotions. EEG brainwave dataset displaying positive, negative, and neutral emotions. However, negative emotions are responsible for psychological health. In this paper, research focused solely on negative emotional state characteristics for which the divide-and-conquer approach has been applied to the feature extraction process. Features are grouped into four equal subsets and feature selection has been done for each subset by feature ranking approach based on their feature importance determined by the Random Forest-Recursive Feature Elimination with Cross-validation (RF-RFECV) method. After feature ranking, the fusion of the feature subset is employed to obtain a new potential dataset. 10-fold cross-validation is performed with a grid search created using a set of predetermined model parameters that are important to achieving the greatest possible accuracy. Experimental results demonstrated that the proposed model has achieved 97.71% accuracy in predicting psychological disorders  2023, International Journal of Advanced Computer Science and Applications.All Rights Reserved.</text>
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              <text>Tahseen S.; Danti A.</text>
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              <text>International Journal of Advanced Computer Science and Applications, Vol-14, No. 2, pp. 287-294.</text>
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              <text>2023-01-01</text>
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              <text>&lt;a href="https://doi.org/10.14569/IJACSA.2023.0140235" target="_blank" rel="noreferrer noopener"&gt;https://doi.org/10.14569/IJACSA.2023.0140235&lt;/a&gt;
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              <text>All Open Access; Gold Open Access</text>
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              <text>ISSN: 2158107X</text>
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              <text>Tahseen S.; Danti A., Department of Computer Science and Engineering, Christ University, Bangalore, India</text>
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