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

и другие.

Sustainability, Год журнала: 2024, Номер 16(24), С. 10845 - 10845

Опубликована: Дек. 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.

Язык: Английский

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

и другие.

Innovative Infrastructure Solutions, Год журнала: 2025, Номер 10(2)

Опубликована: Янв. 23, 2025

Язык: Английский

Процитировано

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

и другие.

Russian Physics Journal, Год журнала: 2025, Номер unknown

Опубликована: Фев. 12, 2025

Язык: Английский

Процитировано

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

и другие.

Journal of Radioanalytical and Nuclear Chemistry, Год журнала: 2025, Номер unknown

Опубликована: Фев. 21, 2025

Язык: Английский

Процитировано

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

и другие.

Bulletin of Engineering Geology and the Environment, Год журнала: 2025, Номер 84(3)

Опубликована: Фев. 25, 2025

Язык: Английский

Процитировано

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

и другие.

Research Square (Research Square), Год журнала: 2025, Номер unknown

Опубликована: Март 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

Язык: Английский

Процитировано

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

и другие.

Sustainability, Год журнала: 2024, Номер 16(24), С. 10845 - 10845

Опубликована: Дек. 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.

Язык: Английский

Процитировано

0