@article{Karimian_Falahat_Bakhsh_Rad_Barkhordari_2024, title={A Comparative Study on Predicting the Characteristics of Plasma Activated Water: Artificial Neural Network (ANN) & Support Vector Regression (SVR)}, volume={18}, url={https://oiccpress.com/journal-of-theoretical-and-applied-physics/article/a-comparative-study-on-predicting-the-characteristics-of-plasma-activated-water-artificial-neural-network-ann-support-vector-regression-svr/}, DOI={10.57647/j.jtap.2024.1804.48}, abstractNote={In this paper, the applicability of the Machine Learning (ML) technique in predicting the structural characteristics of water exposed by the plasma discharge is studied. For this purpose, the structural characteristics of water including pH, Electrical Conductivity (EC), Oxidation Reduction Potential (ORP), Total Dissolved Solution (TDS), and salt is experimentally measured before and after exposing the plasma. The plasma discharge medium consists of air and water. The applied voltage and the time duration of plasma application are considered as operational variables. Also, Support Vector Regression (SVR), as a strong algorithm of Machine Learning (ML), is applied on the data to train a model for accurately predicting the water characteristics as the new data. It is shown that pH value is reduced at higher applied voltages and time of plasma treatment while EC, ORP, TDS, and salt are increased. It was also found that the SVR model can predict the main characteristics of water with a high R2 score of 0.998. The results obtained by SVR in the prediction of water characteristics are compared with the performance of Artificial Neural Network (ANN) as another interesting ML algorithm, showing the better performance of the SVR algorithm than ANN one.}, number={4}, journal={Journal of Theoretical and Applied Physics}, publisher={OICC Press}, author={Karimian, Saeed and Falahat, Shahrzad and Bakhsh, Zahra Emam and Rad, Mohammad Javad Ghavami and Barkhordari, Ali}, year={2024}, month={Aug.}, keywords={Artificial Neural Network (ANN), Plasma discharge, Water Characteristics, Machine Learning (ML), Support Vector Regression (SVR)} }