Sustainable Smart Education Based on AI Models Incorporating Firefly Algorithm to Evaluate Further Education DOI Open Access
En-Hui Li, Zixi Wang, Jin Liu

et al.

Sustainability, Journal Year: 2024, Volume and Issue: 16(24), P. 10845 - 10845

Published: Dec. 11, 2024

With the popularity of higher education and evolution workplace environment, graduate has become a key choice for students planning their future career paths. Therefore, this study proposes to use data processing ability pattern recognition machine learning models analyze relevant information applicants. This explores three different models—backpropagation neural networks (BPNN), random forests (RF), logistic regression (LR)—and combines them with firefly algorithm (FA). Through selection, model was constructed verified. By comparing verification results composite models, whose evaluation were closest actual selected as research result. The experimental show that result BPNN-FA is best, an R value 0.8842 highest prediction accuracy. At same time, influence each characteristic parameter on analyzed. CGPA greatest results, which provides direction evaluators level students’ scientific ability, well providing impetus continue promote combination artificial intelligence.

Language: Английский

Sustainable Smart Education Based on AI Models Incorporating Firefly Algorithm to Evaluate Further Education DOI Open Access
En-Hui Li, Zixi Wang, Jin Liu

et al.

Sustainability, Journal Year: 2024, Volume and Issue: 16(24), P. 10845 - 10845

Published: Dec. 11, 2024

With the popularity of higher education and evolution workplace environment, graduate has become a key choice for students planning their future career paths. Therefore, this study proposes to use data processing ability pattern recognition machine learning models analyze relevant information applicants. This explores three different models—backpropagation neural networks (BPNN), random forests (RF), logistic regression (LR)—and combines them with firefly algorithm (FA). Through selection, model was constructed verified. By comparing verification results composite models, whose evaluation were closest actual selected as research result. The experimental show that result BPNN-FA is best, an R value 0.8842 highest prediction accuracy. At same time, influence each characteristic parameter on analyzed. CGPA greatest results, which provides direction evaluators level students’ scientific ability, well providing impetus continue promote combination artificial intelligence.

Language: Английский

Citations

0