@article{Alkorbi2024, 
author = {Ali S. Alkorbi and Muhammad Tanveer and Humayoun Shahid and Muhammad Bilal Qadir and Fayyaz Ahmad and Zubair Khaliq and Mohammed Jalalah and Muhammad Irfan and Hassan Algadi and Farid A. Harraz},
title = {Comparative analysis of feed-forward neural network and second-order polynomial regression in textile wastewater treatment efficiency},
year = {2024},
journal = {AIMS Mathematics},
volume = {9},
number = {5},
pages = {10955-10976},
keywords = {feed-forward neural network, machine learning, multi-objective optimization, treatment of textile wastewater, decolorization, chemical oxygen demand, sustainable environment},
url = {https://www.sciopen.com/article/10.3934/math.2024536},
doi = {10.3934/math.2024536},
abstract = {This study refines a single-layer Feed-Forward Neural Network (FFNN) for the treatment of textile dye wastewater, concentrating on percentage decolorization (%DEC) and percentage chemical oxygen demand (%COD) reduction. The optimized neural network configuration comprises four input and one output neuron, fine-tuned based on the mean squared error (MSE). The training phase demonstrates a consistent MSE decline, reaching its lowest at epoch 209 for %DEC and epoch 34 for %COD, with corresponding MSEs of    1.799  ×      10          −      5       and    1.4  ×      10          −      3      , respectively. The maximum absolute errors for %DEC and %COD were found to be    4.0787 and    2.4486, while the mean absolute errors were    0.4821 and    0.7256, respectively. In contrast to second-degree polynomial regression, the FFNN model exhibits enhanced predictive accuracy, as indicated by higher        R    2   values of    0.99363 for %DEC and    0.99716 for %COD, and reduced error metrics.}
}