Colloid Transport Prediction using SVM-MLP


Authors : Prem Kumar M; Prakash Raja K; Smasheeray Allam M; Sachin S; Gokulachalam K

Volume/Issue : Volume 9 - 2024, Issue 4 - April

Google Scholar : https://tinyurl.com/bdzfessr

Scribd : https://tinyurl.com/5n7v8twm

DOI : https://doi.org/10.38124/ijisrt/IJISRT24APR2508

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Abstract : The effectiveness of the multilayer perceptron (MLP) method in conjunction with support vector machines (SVM) for the prediction of colloid transport behaviour is examined in this work. Because colloids are essential to many industrial and environmental processes, proper modelling is required for efficient management. By using SVM for feature selection and MLP for nonlinear mapping, the suggested SVM-MLP hybrid technique combines the advantages of both algorithms to improve prediction accuracy. After a great deal of testing and verification, the model shows encouraging outcomes that highlight its ability to forecast colloid transport dynamics more accurately and efficiently, providing important information for industrial and environmental applications.

Keywords : Colloid Transport, Clogging, Hydrodynamic Effect, Artificial Neural Network.

The effectiveness of the multilayer perceptron (MLP) method in conjunction with support vector machines (SVM) for the prediction of colloid transport behaviour is examined in this work. Because colloids are essential to many industrial and environmental processes, proper modelling is required for efficient management. By using SVM for feature selection and MLP for nonlinear mapping, the suggested SVM-MLP hybrid technique combines the advantages of both algorithms to improve prediction accuracy. After a great deal of testing and verification, the model shows encouraging outcomes that highlight its ability to forecast colloid transport dynamics more accurately and efficiently, providing important information for industrial and environmental applications.

Keywords : Colloid Transport, Clogging, Hydrodynamic Effect, Artificial Neural Network.

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