Corrigendum: Machine learning for the detection of social anxiety disorder using effective connectivity and graph theory measures
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2023-07-01
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| Series: | Frontiers in Psychiatry |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fpsyt.2023.1257713/full |
| _version_ | 1797773371127103488 |
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| author | Abdulhakim Al-Ezzi Nidal Kamel Amal A. Al-Shargabi Fares Al-Shargie Alaa Al-Shargabi Norashikin Yahya Mohammed Isam Al-Hiyali |
| author_facet | Abdulhakim Al-Ezzi Nidal Kamel Amal A. Al-Shargabi Fares Al-Shargie Alaa Al-Shargabi Norashikin Yahya Mohammed Isam Al-Hiyali |
| author_sort | Abdulhakim Al-Ezzi |
| collection | DOAJ |
| first_indexed | 2024-03-12T22:05:32Z |
| format | Article |
| id | doaj.art-d65cef3e9f574988a3010f1bf40f468d |
| institution | Directory Open Access Journal |
| issn | 1664-0640 |
| language | English |
| last_indexed | 2024-03-12T22:05:32Z |
| publishDate | 2023-07-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Psychiatry |
| spelling | doaj.art-d65cef3e9f574988a3010f1bf40f468d2023-07-24T16:07:38ZengFrontiers Media S.A.Frontiers in Psychiatry1664-06402023-07-011410.3389/fpsyt.2023.12577131257713Corrigendum: Machine learning for the detection of social anxiety disorder using effective connectivity and graph theory measuresAbdulhakim Al-Ezzi0Nidal Kamel1Amal A. Al-Shargabi2Fares Al-Shargie3Alaa Al-Shargabi4Norashikin Yahya5Mohammed Isam Al-Hiyali6Centre for Intelligent Signal & Imaging Research (CISIR), Electrical and Electronic Engineering Department, Universiti Teknologi PETRONAS, Bandar Seri Iskandar, Perak, MalaysiaCollege of Engineering and Computer Science, VinUniversity, Hanoi, VietnamDepartment of Information Technology, College of Computer, Qassim University, Buraydah, Saudi ArabiaFaculty of Engineering, Abu Dhabi University, Abu Dhabi, United Arab EmiratesDepartment of Information Technology, Universiti Teknlogi Malaysia, Skudai, MalaysiaCentre for Intelligent Signal & Imaging Research (CISIR), Electrical and Electronic Engineering Department, Universiti Teknologi PETRONAS, Bandar Seri Iskandar, Perak, MalaysiaCentre for Intelligent Signal & Imaging Research (CISIR), Electrical and Electronic Engineering Department, Universiti Teknologi PETRONAS, Bandar Seri Iskandar, Perak, Malaysiahttps://www.frontiersin.org/articles/10.3389/fpsyt.2023.1257713/fullEEGgraph theory analysissocial anxiety disordersmachine learningeffective connectivitypartial directed coherence |
| spellingShingle | Abdulhakim Al-Ezzi Nidal Kamel Amal A. Al-Shargabi Fares Al-Shargie Alaa Al-Shargabi Norashikin Yahya Mohammed Isam Al-Hiyali Corrigendum: Machine learning for the detection of social anxiety disorder using effective connectivity and graph theory measures Frontiers in Psychiatry EEG graph theory analysis social anxiety disorders machine learning effective connectivity partial directed coherence |
| title | Corrigendum: Machine learning for the detection of social anxiety disorder using effective connectivity and graph theory measures |
| title_full | Corrigendum: Machine learning for the detection of social anxiety disorder using effective connectivity and graph theory measures |
| title_fullStr | Corrigendum: Machine learning for the detection of social anxiety disorder using effective connectivity and graph theory measures |
| title_full_unstemmed | Corrigendum: Machine learning for the detection of social anxiety disorder using effective connectivity and graph theory measures |
| title_short | Corrigendum: Machine learning for the detection of social anxiety disorder using effective connectivity and graph theory measures |
| title_sort | corrigendum machine learning for the detection of social anxiety disorder using effective connectivity and graph theory measures |
| topic | EEG graph theory analysis social anxiety disorders machine learning effective connectivity partial directed coherence |
| url | https://www.frontiersin.org/articles/10.3389/fpsyt.2023.1257713/full |
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