Optimized SVR with nature-inspired algorithms for environmental modelling of mycotoxins in food virtual-water samples DOI Creative Commons
A. G. Usman, Sagiru Mati, Hanita Daud

и другие.

Scientific Reports, Год журнала: 2025, Номер 15(1)

Опубликована: Май 13, 2025

The accurate determination of mycotoxins in food samples is crucial to guarantee safety and minimize their toxic effects on human animal health. This study proposed the use a support vector regression (SVR) predictive model improved by two metaheuristic algorithms used for optimization namely, Harris Hawks Optimization (HHO) Particle Swarm (PSO) predict chromatographic retention time various mycotoxin groups. dataset was collected from secondary sources train validate SVR-HHO SVR-PSO models. performance models assessed via mean square error, correlation coefficient, Nash-Sutcliffe efficiency. outperformed existing methods 4-7% both learning (training testing) phases respectively. By using optimization, parameter adjustment became more effective, avoiding trapping local minima improving generalization. These results demonstrate how machine metaheuristics may be combined accurately forecast levels, providing useful tool regulatory compliance monitoring. framework perfect commercial quality assurance, testing, extensive programs because it provides exceptional accuracy resilience predicting times. In contrast conventional models, effectively manages intricate nonlinear interactions, guaranteeing identification while lowering hazards

Язык: Английский

Second-order based ensemble machine learning technique for modelling river water biological oxygen demand (BOD): Insights into improved learning DOI
A. G. Usman,

May Almousa,

Hanita Daud

и другие.

Journal of Radiation Research and Applied Sciences, Год журнала: 2025, Номер 18(2), С. 101439 - 101439

Опубликована: Март 22, 2025

Язык: Английский

Процитировано

0

Trend in global ocean heat content into different depth layers from 1940 to 2050 DOI Creative Commons
Mehmet Bilgili

Natural Hazards, Год журнала: 2025, Номер unknown

Опубликована: Апрель 17, 2025

Язык: Английский

Процитировано

0

Optimized SVR with nature-inspired algorithms for environmental modelling of mycotoxins in food virtual-water samples DOI Creative Commons
A. G. Usman, Sagiru Mati, Hanita Daud

и другие.

Scientific Reports, Год журнала: 2025, Номер 15(1)

Опубликована: Май 13, 2025

The accurate determination of mycotoxins in food samples is crucial to guarantee safety and minimize their toxic effects on human animal health. This study proposed the use a support vector regression (SVR) predictive model improved by two metaheuristic algorithms used for optimization namely, Harris Hawks Optimization (HHO) Particle Swarm (PSO) predict chromatographic retention time various mycotoxin groups. dataset was collected from secondary sources train validate SVR-HHO SVR-PSO models. performance models assessed via mean square error, correlation coefficient, Nash-Sutcliffe efficiency. outperformed existing methods 4-7% both learning (training testing) phases respectively. By using optimization, parameter adjustment became more effective, avoiding trapping local minima improving generalization. These results demonstrate how machine metaheuristics may be combined accurately forecast levels, providing useful tool regulatory compliance monitoring. framework perfect commercial quality assurance, testing, extensive programs because it provides exceptional accuracy resilience predicting times. In contrast conventional models, effectively manages intricate nonlinear interactions, guaranteeing identification while lowering hazards

Язык: Английский

Процитировано

0