Ying Wang and Yang Li
Attentive dual residual generative adversarial network, colour Wiener filtering method, gazelle optimisation algorithm, vocational skills education, quality assessment
Vocational training holds significant importance in the Chinese educational system, with a deliberate emphasis on reform and innovation. Enhancing teaching quality is a priority, and conducting a comprehensive assessment of it is essential. Artificial intelligence, particularly deep learning technology, emerges as a promising solution due to its ability to effectively handle the complex and diverse aspects involved in evaluating teaching quality in vocational education. In this manuscript Chinese vocational skills education quality assessment using attentive dual residual generative adversarial network optimised with gazelle optimisation algorithm (CVSE-ADRGAN-GOA) is proposed. Initially, the input data is amassed from Chinese vocational skills education real time data. The acquired data is preprocessed using the colour Wiener filtering method for normalising the input data. Then, the preprocessed data is given to attentive dual residual generative adversarial network (ADRGAN) for the quality assessment of Chinese vocational skills education. The gazelle optimisation algorithm (GOA) is used to optimise the input weight parameters of the ADRGAN. The proposed CVSE-ADRGAN-GOA technique is activated in MATLAB and its efficacy is evaluated utilising some performance metrics, like accuracy, precision, sensitivity, F1-score, specificity, error rate, receiver operating characteristic (ROC), computational time. The proposed CVSE-ADRGAN-GOA method provides 22.43%, 21.76%, 25.65% higher accuracy, 25.67%, 22.66%, 27.92% higher precision while compared to the existing models, like Internet environment and machine learning espoused innovation way of secretarial education ∗ International Exchanges and Cooperation Office, Beijing Polytechnic College, China; e-mail: [email protected] ∗∗ Surveying and Mapping Branch, Beijing Jingneng Geological Engineering Co., Ltd., China; e-mail: [email protected] Corresponding author: Ying Wang Recommended by Howard Li
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