Classification analysis of burnout people's brain images using ontology-based speculative sense model |
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Authors: | Chandrakirishnan Balakrishnan Sivaparthipan Priyan Malarvizhi Kumar Thota Chandu BalaAnand Muthu Mohammed Hasan Ali Boris Tomaš |
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Affiliation: | 1. Department of Computer Science & Engineering, Tagore Institute of Engineering and Technology, Salem, India;2. Department of Computer and Information Science, Gannon University, Erie, USA;3. University of Nicosia, Nicosia, Cyprus;4. Computer Techniques Engineering Department, Faculty of Information Technology, Imam Ja'afar Al-Sadiq University, Baghdad, Iraq;5. Faculty of Organization and Informatics, University of Zagreb, Varazdin, Croatia |
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Abstract: | Burnout is a state of exhaustion that results from prolonged, excessive workplace stress. This can be examined with the biological explications of burnout and physical consequences and classified against prolonged vigorous activities. The research aims to classify burnout people's brain images against prolonged emotional activities using ontology analysis of treatment and prevention and intermediate layers formation based on a speculative sense model. In this segment, the Ontology analysis of Treatment and prevention and intermediate layers formation based on a hypothetical sense model is employed for burnout people's classification analysis. The methodology is performed in the platform of ontology creation and performs the classification analysis. The calculation analysis found the result, and the brain images were classified. The classification analysis of burnout people's brain images, separation of prolonged vigorous activities, and the ontology creation for treatment and prevention against burnout people's brain images were obtained. The analysis received the result, and the results of the precision, recall, storage, computation time, specificity, and classification of burnout people's brain images were obtained. Furthermore, all these Ontology analysis of Treatment and prevention and intermediate layers formation based on a hypothetical sense model had the prediction sensitivity (SN) over 50% and specificity (SP) over 90%. The Classification of Burnout People's Brain performance comparison shows that the proposed system is much more successful than existing methods, especially on a scoring accuracy of 98%. |
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Keywords: | biological explications brain images burnout ontology analysis specificity speculative sense model |
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