<> "The repository administrator has not yet configured an RDF license."^^ . <> . . . "Hybrid of PSO-ANN and PCA-SVR Models for the Prediction of External Corrosion in Pipeline Infrastructure: A Comparative Study"^^ . "This paper compares two models, PSO-ANN and PCASVR, for predicting pipeline corrosion. PSO-ANN uses Particle Swarm Optimization, PCA-SVR uses Principal Component Analysis and Support Vector Regression. Both models were tested on a dataset with corrosion-related features. PSO-ANN performed exceptionally well due to its global optimization and neural network's ability to handle complexity. PCA-SVR was competitive, especially with high-dimensional data, but slightly less accurate for complex issues. This study helps engineers and researchers choose models for better corrosion prediction and pipeline management. © 2023 IEEE."^^ . "2023" . . . "Institute of Electrical and Electronics Engineers Inc."^^ . . "Institute of Electrical and Electronics Engineers Inc."^^ . . . "2023 IEEE International Conference on Sensors and Nanotechnology, SENNANO 2023"^^ . . . . . . . . . . . . . . "H."^^ . "Amzar"^^ . "H. Amzar"^^ . . "Z."^^ . "May"^^ . "Z. May"^^ . . "M.I."^^ . "Haziq"^^ . "M.I. Haziq"^^ . . . . . "HTML Summary of #18992 \n\nHybrid of PSO-ANN and PCA-SVR Models for the Prediction of External Corrosion in Pipeline Infrastructure: A Comparative Study\n\n" . "text/html" . .