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DC Field | Value | Language |
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dc.contributor.author | MOULEBHAR, Samia | - |
dc.contributor.author | BENDIMERAD.M, BENDIMERAD.M | - |
dc.contributor.author | TAYBI, NAIMA | - |
dc.contributor.author | YAHLA, YAHLA | - |
dc.date.accessioned | 2024-02-13T10:31:40Z | - |
dc.date.available | 2024-02-13T10:31:40Z | - |
dc.date.issued | 2022 | - |
dc.identifier.uri | http://dspace.univ-temouchent.edu.dz/handle/123456789/2182 | - |
dc.description.abstract | Depression is a psychological illness that occurs in many people at different ages. As part of our work, we proposed two intelligent methods based on AI to automatically diagnose depression where we automated two international tests (Beck, resilience test) and global diagnosis by artificial neural network We also performed neural classifieds of MRI images for the detection of depression. Then we used fuzzy inference systems to conclude the difference in severity of patients in the same class Finally, we programmed interfaces summarizing our proposed diagnosis The results obtained are satisfactory and demonstrate the effectiveness of our methods | en_US |
dc.language.iso | fr | en_US |
dc.subject | Depression, Diagnostic Automation, Beck, Resilience, RNA, SIF, MRI v | en_US |
dc.subject | Dépression, automatisation de diagnostic, Beck, Résilience, RNA, SIF, IRM | en_US |
dc.title | AUTOMATISATION DU DIAGNOSTIC DE LA DEPRESSION PAR LES METHODES DE L’INTELLIGENCE ARTIFICIELLE | en_US |
dc.type | Thesis | en_US |
Appears in Collections: | Electronique |
Files in This Item:
File | Description | Size | Format | |
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mémoire finale.pdf | 2,49 MB | Adobe PDF | View/Open |
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