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dc.contributor.authorAl-Ezzi, Abdulhakim
dc.contributor.authorKamel, Nidal
dc.contributor.authorAl-Shargabi, Amal A.
dc.contributor.authorAl-Shargie, Fares
dc.contributor.authorAl-Shargabi, Alaa
dc.contributor.authorYahya, Norashikin
dc.contributor.authorAl-Hiyali, Mohammed Isam
dc.date.accessioned2024-10-24T18:18:18Z
dc.date.available2024-10-24T18:18:18Z
dc.date.issued2023-07-24
dc.identifier.urihttps://vinspace.edu.vn/handle/VIN/407
dc.description.abstractHere’s the corrected funding and acknowledgment statements as requested: Funding This research was supported by the Ministry of Education, Malaysia under the Higher Institute Center of Excellence (HiCOE) scheme awarded to the Centre for Intelligent Signal and Imaging Research (CISIR), Universiti Teknologi PETRONAS. Acknowledgments Researchers would like to thank the Deanship of Scientific Research, Qassim University for funding the publication of this project. Researchers also would like to thank the Centre for Intelligent Signal and Imaging Research (CISIR), Universiti Teknologi PETRONAS for their support by providing EEG data. The authors apologize for this error and state that this does not change the scientific conclusions of the article in any way. The original article has been updated.en_US
dc.language.isoenen_US
dc.subjectEEGen_US
dc.subjectgraph theory analysisen_US
dc.subjectsocial anxiety disordersen_US
dc.subjectmachine learningen_US
dc.subjecteffective connectivityen_US
dc.subjectpartial directed coherenceen_US
dc.subjectsupport vector machineen_US
dc.subjectevent related potentialen_US
dc.titleCorrigendum: Machine learning for the detection of social anxiety disorder using e ective connectivity and graph theory measuresen_US
dc.typeArticleen_US


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  • Nidal Kamel, PhD. [3]
    College of Engineering and Computer Science; Associate Professor, Electrical Engineering program

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