ArticleScientific reports2025
Optimization of interdisciplinary competence development methods for university faculty based on artificial neural networks.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Abstract
The development of interdisciplinary competence among university faculty increasingly serves as a key driver for educational innovation and knowledge integration. This study proposes a modeling framework based on Artificial Neural Networks for competence identification and pathway optimization. A Multilayer Perceptron (MLP) model is employed to conduct quantitative modeling and nonlinear prediction of 14 influencing factors. The empirical results show that the mean squared error of MLP on the test set is 0.276, which is 4.5% lower than that of One-dimensional Convolutional Neural Network (1D-CNN), and the mean absolute error is reduced by 2.11%. The goodness of fit (R
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