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

A machine learning approach to predicting pervious concrete properties: a review DOI
Navaratnarajah Sathiparan, Pratheeba Jeyananthan, Daniel Niruban Subramaniam

et al.

Innovative Infrastructure Solutions, Journal Year: 2025, Volume and Issue: 10(2)

Published: Jan. 23, 2025

Language: Английский

Citations

0
The influence of geometric parameters of reinforcement on the destruction of reinforced concrete structures under impact DOI

S. P. Batuev,

П. А. Радченко,

Andriy Radchenko

et al.

Russian Physics Journal, Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 12, 2025

Language: Английский

Citations

0
Optimized mix proportion design for radiation-shielding concrete using particle swarm optimization: a case study on fast neutron and gamma shielding DOI

Haoxuan Li,

Fengdi Qin,

Zhongkai Fan

et al.

Journal of Radioanalytical and Nuclear Chemistry, Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 21, 2025

Language: Английский

Citations

0
Hybrid catboost models optimized with metaheuristics for predicting shear strength in rock joints DOI
Xiaohua Ding, Mahdi Hasanipanah, Mohammad Matin Rouhani

et al.

Bulletin of Engineering Geology and the Environment, Journal Year: 2025, Volume and Issue: 84(3)

Published: Feb. 25, 2025

Language: Английский

Citations

0
Modeling the Destruction of Heavy Reinforced Concrete Screens Under Impact DOI Creative Commons
С. П. Батуев, П. А. Радченко, А. В. Радченко

et al.

Research Square (Research Square), Journal Year: 2025, Volume and Issue: unknown

Published: March 3, 2025

Abstract This paper presents the results of numerical modeling interaction between a cylindrical titanium impactor and barriers made heavy reinforced concrete at various initial velocities with different reinforcement configurations involving steel bars plates varying geometric characteristics. The was carried out using finite element method in three-dimensional formulation EFES software package developed by authors. Johnson-Holmquist model, which accounts for plasticity, crack development, damage accumulation material, employed to describe destruction processes concrete. study investigated influence geometry configuration on pattern barriers, extent damage, residual velocity impactor. obtained can be used optimize design protective structures

Language: Английский

Citations

0
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