Metaheuristic Optimization of Agricultural Machinery for the Colombian Carnation Industry DOI Creative Commons

Nixon Cuenca Orozco,

Federico Gutiérrez Madrid, Héctor Fabio Quintero Riaza

et al.

Agronomy, Journal Year: 2024, Volume and Issue: 14(11), P. 2589 - 2589

Published: Nov. 3, 2024

The flower-growing sector in Latin America presents significant health risks for workers, which highlights the need technological updates their production processes. Likewise, outdated machinery leads to losses that be avoided. method of productive innovation developed this document involves optimizing a mechanism agricultural used carnation classification. optimization is achieved by minimizing jerk mechanism’s movement using metaheuristic methods. results three methods are compared against brute force methodology. Optimization these allows achieving satisfactory with up 98% time reduction process. This gives longer useful life machinery, reduces stops needed maintenance from once an hour every hours, and damage done machine stems.

Language: Английский

Optimizing Landslide Susceptibility Mapping in Oued Guebli Watershed: A Comparative Study of Deep Learning, Support Vector Machines, Logistic Regression with Spatial Validation and AUC-ROC Analysis DOI
Nadjib Mebirouk, Moussa Amrane, Salah Messast

et al.

Iranian Journal of Science and Technology Transactions of Civil Engineering, Journal Year: 2025, Volume and Issue: unknown

Published: March 29, 2025

Language: Английский

Citations

0
Landslide Susceptibility Assessment Using Hybrid Method of Best-first Decision Tree and Machine Learning Ensembles DOI Creative Commons

Weipeng Li,

Jianguo Wang, Linhai Li

et al.

KSCE Journal of Civil Engineering, Journal Year: 2025, Volume and Issue: unknown, P. 100199 - 100199

Published: April 1, 2025

Language: Английский

Citations

0
Optimizing Landslide Susceptibility Mapping in Oued Guebli Watershed: A Comparative Study of Deep Learning, Support Vector Machines, Logistic Regression with Spatial Validation and AUC- ROC Analysis DOI
Nadjib Mebirouk, Moussa Amrane, Salah Messast

et al.

Research Square (Research Square), Journal Year: 2024, Volume and Issue: unknown

Published: Sept. 5, 2024

Abstract methods Logistic Regression (LR), Support Vector Machines (SVM), and Deep Learning (DL) to identify areas most susceptible landslides. The selection of causative factors was based on a detailed statistical study examining the relationship between landslide occurrence specific characteristics such as slope, lithology, Normalized Difference Vegetation Index (NDVI), Topographic Wetness (TWI), land use, proximity roads, watercourses, geological faults. These were essential in generating accurate reliable susceptibility maps using Geographic Information Systems (GIS) technology. Metrics performance, including accuracy, precision, F1-score, specificity, sensitivity, RMSE, used evaluate performance models, which verified, validated, compared area under curve (AUC) value Receiver Operating Characteristics Curves (ROC) method spatial validation technique. This evaluated percentage active high very classes. DL SVM models demonstrated concentration points these classes, with 99% 98% respectively, whereas LR model showed 89%. In terms AUC validation, achieved highest 0.9894, followed by an 0.9873, while lower 0.9093. precise results help high-risk more effectively, thereby safeguarding residents preserving infrastructure Oued Guebli watershed. choice effective underscores its capability deliver maps, are important for informed decision-making risk management.

Language: Английский

Citations

0
Metaheuristic Optimization of Agricultural Machinery for the Colombian Carnation Industry DOI Creative Commons

Nixon Cuenca Orozco,

Federico Gutiérrez Madrid, Héctor Fabio Quintero Riaza

et al.

Agronomy, Journal Year: 2024, Volume and Issue: 14(11), P. 2589 - 2589

Published: Nov. 3, 2024

The flower-growing sector in Latin America presents significant health risks for workers, which highlights the need technological updates their production processes. Likewise, outdated machinery leads to losses that be avoided. method of productive innovation developed this document involves optimizing a mechanism agricultural used carnation classification. optimization is achieved by minimizing jerk mechanism’s movement using metaheuristic methods. results three methods are compared against brute force methodology. Optimization these allows achieving satisfactory with up 98% time reduction process. This gives longer useful life machinery, reduces stops needed maintenance from once an hour every hours, and damage done machine stems.

Language: Английский

Citations

0