Research on the application of neural network modeling in the assessment of traditional sports training and coaches’ quality in colleges and universities DOI Creative Commons
Dan Chen, Lin Li

Applied Mathematics and Nonlinear Sciences, Год журнала: 2024, Номер 9(1)

Опубликована: Янв. 1, 2024

Abstract Scientific monitoring in sports training colleges and universities is particularly important, which an important symbol of the scientific level training, has a role promoting ability athletes coaches. In this paper, we collect data based on evaluation index coach quality then preprocess collected athlete performance into BP neural network model. The firefly algorithm used to optimize prediction network, model, visualization system for constructed display predicted assessment real-time. It been found that average error model 0.73%, can be training. test scores all aspects assisted by were significantly better than those traditional group, coaches higher group (P<0.05). This paper forms systematic method college enhance help improve

Язык: Английский

Multi-modal multi-objective wolf pack algorithm with circumferential scouting and intra-niche interactions DOI
Jia Zhao, Fujun Chen, Renbin Xiao

и другие.

Swarm and Evolutionary Computation, Год журнала: 2025, Номер 93, С. 101842 - 101842

Опубликована: Янв. 8, 2025

Язык: Английский

Процитировано

0

A Decision Support System for Wheat Powdery Mildew Risk Prediction Using Weather Monitoring, Machine Learning and Explainable Artificial Intelligence DOI
Grygorii Diachenko, Іvan Laktionov,

Oleksandr Vinyukov

и другие.

Computers and Electronics in Agriculture, Год журнала: 2025, Номер 230, С. 109905 - 109905

Опубликована: Янв. 10, 2025

Язык: Английский

Процитировано

0

Contextual Information Aggregation and Multi‐Scale Feature Fusion for Single Image De‐Raining in Generative Adversarial Networks DOI
Jia Zhao, Ming Chen, Jeng‐Shyang Pan

и другие.

Concurrency and Computation Practice and Experience, Год журнала: 2025, Номер 37(3)

Опубликована: Янв. 16, 2025

ABSTRACT Aiming to address issues such as non‐uniform rain density and misjudgment caused by noise in image de‐raining, we propose a single‐image de‐raining method based on generative adversarial network with contextual information aggregation multi‐scale feature fusion. First, design generator composed of encoding, context aggregation, decoding stages. Features are extracted using convolution, while expansion convolution effectively aggregates information. Transposition is then used restore the image, enhancing model's ability perceive details achieve accurate judgment content reconstruction. Second, fusion discriminator structure capture different kernels scales connect maps from scales. This improves understand differentiate between authentic fake images. Finally, new refinement loss function reduce grid artifact generation add Lipschitz constraints further minimize imaging gap. In this paper, peak signal‐to‐noise ratio structural similarity evaluation criteria, experiments conducted real synthesized demonstrate superior removal performance proposed method.

Язык: Английский

Процитировано

0

Multi-label feature selection via label relaxation DOI
Yuling Fan, Peizhong Liu, Jinghua Liu

и другие.

Applied Soft Computing, Год журнала: 2025, Номер unknown, С. 113047 - 113047

Опубликована: Март 1, 2025

Процитировано

0

Firefly Algorithm-based Optimization of Control Parameters in DC Conversion Systems DOI Open Access
Thanh‐Lam Le

Engineering Technology & Applied Science Research, Год журнала: 2025, Номер 15(2), С. 20588 - 20594

Опубликована: Апрель 3, 2025

Sustainable energy and electric vehicles require DC-DC converters in renewable systems, EV charging, smart grids. In this context, buck are crucial, providing efficient voltage regulation reliable performance these advanced systems. While Proportional-Integral (PI) controllers widely adopted for their simplicity dependability, they often rely on manual parameter tuning, limiting adaptability responsiveness. To address limitation, research introduces a digital control strategy that optimizes the PI parameters using Firefly Algorithm (FA). This optimization significantly enhances stability reduces oscillations converter. A MATLAB/Simulink simulation model is utilized to validate proposed approach, results demonstrate FA-optimized substantially improve converter's performance, making it highly suitable high-demand applications

Язык: Английский

Процитировано

0

Optimized feature selection in high-dimensional gene expression data using weighted differential gene expression analysis DOI
Amjad Ali, Zardad Khan, Saeed Aldahmani

и другие.

Applied Soft Computing, Год журнала: 2025, Номер unknown, С. 113329 - 113329

Опубликована: Июнь 1, 2025

Язык: Английский

Процитировано

0

Multi-Objective Feature Selection Algorithm using Beluga Whale Optimization DOI

Kiana Kouhpah Esfahani,

Behnam Mohammad Hasani Zade,

N. Mansouri

и другие.

Chemometrics and Intelligent Laboratory Systems, Год журнала: 2024, Номер 257, С. 105295 - 105295

Опубликована: Дек. 4, 2024

Язык: Английский

Процитировано

1

Research on the application of neural network modeling in the assessment of traditional sports training and coaches’ quality in colleges and universities DOI Creative Commons
Dan Chen, Lin Li

Applied Mathematics and Nonlinear Sciences, Год журнала: 2024, Номер 9(1)

Опубликована: Янв. 1, 2024

Abstract Scientific monitoring in sports training colleges and universities is particularly important, which an important symbol of the scientific level training, has a role promoting ability athletes coaches. In this paper, we collect data based on evaluation index coach quality then preprocess collected athlete performance into BP neural network model. The firefly algorithm used to optimize prediction network, model, visualization system for constructed display predicted assessment real-time. It been found that average error model 0.73%, can be training. test scores all aspects assisted by were significantly better than those traditional group, coaches higher group (P<0.05). This paper forms systematic method college enhance help improve

Язык: Английский

Процитировано

0