A Proposed Artificial Intelligence Algorithm for Development of Higher Education

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Názov: A Proposed Artificial Intelligence Algorithm for Development of Higher Education
Autori: Amin Al Ka’Bi
Zdroj: WSEAS TRANSACTIONS ON COMPUTERS. 22:7-12
Informácie o vydavateľovi: World Scientific and Engineering Academy and Society (WSEAS), 2023.
Rok vydania: 2023
Predmety: 4. Education, 05 social sciences, 0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology, 0503 education
Popis: Higher education has delved into a new stage of rapid development focusing on quality improvement, while encountering new challenges and obstacles. In this research work, an artificial intelligence algorithm for education improvement is proposed. Firstly, deep feature abstraction in temporal and special dimensions is performed using Long Short-Term Memory (LSTM) artificial neural network and convolutional networks. Consequently, multiscale attention fusion techniques are used to improve the articulateness of the characteristics and come up with better recommendations with the assistance of multilayer perceptron. Moreover, the proposed model helps in improving the cognitive capability of students and enhances their overall quality of perception. Moreover, it has been proven that the performance of the proposed model provides better recommendation outcomes and better robustness compared to existing models through conducting extensive experiments based on real data.
Druh dokumentu: Article
Jazyk: English
ISSN: 2224-2872
1109-2750
DOI: 10.37394/23205.2023.22.2
Rights: URL: https://wseas.com/journals/computers/2023/a045105-002(2023).pdf
Prístupové číslo: edsair.doi...........761d95f574f0ec05f96ffdbe6658045c
Databáza: OpenAIRE
Popis
Abstrakt:Higher education has delved into a new stage of rapid development focusing on quality improvement, while encountering new challenges and obstacles. In this research work, an artificial intelligence algorithm for education improvement is proposed. Firstly, deep feature abstraction in temporal and special dimensions is performed using Long Short-Term Memory (LSTM) artificial neural network and convolutional networks. Consequently, multiscale attention fusion techniques are used to improve the articulateness of the characteristics and come up with better recommendations with the assistance of multilayer perceptron. Moreover, the proposed model helps in improving the cognitive capability of students and enhances their overall quality of perception. Moreover, it has been proven that the performance of the proposed model provides better recommendation outcomes and better robustness compared to existing models through conducting extensive experiments based on real data.
ISSN:22242872
11092750
DOI:10.37394/23205.2023.22.2