Please use this identifier to cite or link to this item: http://148.72.244.84/xmlui/handle/xmlui/3771
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dc.contributor.authorReem A. K. Aljorani-
dc.contributor.authorBoshra F. Zopon Al_bayaty-
dc.date.accessioned2023-10-16T19:23:16Z-
dc.date.available2023-10-16T19:23:16Z-
dc.date.issued2021-
dc.identifier.citationhttps://dx.doi.org/10.24237/djps.17.03.558Ben_US
dc.identifier.issn2222-8373-
dc.identifier.urihttp://148.72.244.84:8080/xmlui/handle/xmlui/3771-
dc.description.abstractSince Arabic video classification is not a popular field and there isn’t a lot of researches in this area especially in the educational field. A system was proposed to solve this problem and to make the educational Arabic videos more available to the students. A survey was fulfilled to study several papers in order to design and implement a system that classifies videos operative in the Arabic language by extracting its audio features using azure cognitive services which produce text transcripts. Several preprocessing operations are then applied to process the text transcript. A stochastic gradient descent SGD algorithm was used to classify transcripts and give a suitable label for each video. In addition, a search technique was applied to enable students to retrieve the videos they need. The results showed that SGD algorithm recorded the highest classification accuracy with 89.3 % when compared to other learning models. In the section below, a survey was introduced consisting of the most relevant and recent papers to this work.en_US
dc.description.sponsorshiphttps://djps.uodiyala.edu.iqen_US
dc.language.isoenen_US
dc.subjectVideo Mining, Video Retrieval, Text Classification, Machine learning.en_US
dc.titleEncapsulation Video Classification and Retrieval Based on Arabic Texten_US
dc.typeArticleen_US
Appears in Collections:مجلة ديالى للعلوم الاكاديمية / Academic Science Journal (Acad. Sci. J.)

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