Anupong, Wongchai and Mehbodniya, Abolfazl and L. Webber, Julian and Bostani, Ali and Dhiman, Gaurav and Singh, Bharat and A. R., Murali Dharan UNSPECIFIED (2023) Deep learning algorithms were used to generate photovoltaic renewable energy in saline water analysis via an oxidation process. Journal of Water Reuse and Desalination. ISSN 2220-1319
|
Text
jwrd0130068.pdf Download (807kB) | Preview |
Abstract
The amount of particles and organic matter in wash-waters and effluent from the processing of fruits and vegetables determines whether they need to be treated to fulfil regulatory standards for their intended use. This research proposes a novel technique in photovoltaic cell-based renewable energy in saline water analysis using the oxidation process and deep learning techniques. Here, the saline water oxidation is carried out based on photovoltaic cell-based renewable and saline water analysis is done using Markov fuzzy-based Q-radial function neural networks (MFQRFNN). The plan is entirely web-oriented to enable better control and effective monitoring of water consumption. This monitoring makes use of a communication system that collects data in the form of irregularly spaced time series. Experimental analysis has been carried out based on water salinity data in terms of accuracy, precision, recall, specificity, computational cost, and kappa coefficient.
| Item Type: | Article |
|---|---|
| Subjects: | Subjects > Engineering |
| Divisions: | Ibero-american International University > Research > Articles and books |
| Depositing User: | Sr Bibliotecario |
| Date Deposited: | 21 Jul 2026 12:26 |
| Last Modified: | 21 Jul 2026 12:26 |
| URI: | https://repositorio.funiber.org/id/eprint/28886 |
Actions (login required)
 |
View Item |
