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All issues Volume 430 (2023) E3S Web Conf., 430 (2023) 01193 References
Open Access
Issue
E3S Web Conf.
Volume 430, 2023
15th International Conference on Materials Processing and Characterization (ICMPC 2023)
Article Number 01193
Number of page(s) 7
DOI https://doi.org/10.1051/e3sconf/202343001193
Published online 06 October 2023
  1. T. Bonny, M. Kashkash, F. Ahmed, An efficient deep reinforcement machine learning-based control reverse osmosis system for water desalination, Desalination. 522 (2022) 115443. https://doi.org/10.1016/j.desal.2021.115443. [CrossRef] [Google Scholar]
  2. S. Al Aani, T. Bonny, S.W. Hasan, N. Hilal, Can machine language and artificial intelligence revolutionize process automation for water treatment and desalination?, Desalination. 458 (2019) 84–96. [CrossRef] [Google Scholar]
  3. R. Mahadeva, M. Kumar, S.P. Patole, G. Manik, Employing artificial neural network for accurate modeling, simulation and performance analysis of an RO-based desalination process, Sustain. Comput. Informatics Syst. 35 (2022) 100735. [Google Scholar]
  4. P. Behnam, M. Faegh, M. Khiadani, A review on state-of-the-art applications of data-driven methods in desalination systems, Desalination. 532, (2023) 115744. https://doi.org/10.1016/j.desal.2022.115744. [CrossRef] [Google Scholar]
  5. S. Al Aani, T. Bonny, S.W. Hasan, N. Hilal, Can machine language and artificial intelligence revolutionize process automation for water treatment and desalination?, Desalination. 458 (2019), 84–96. https://doi.org/10.1016/j.desal.2019.02.005. [CrossRef] [Google Scholar]
  6. R. Mahadeva, G. Manik, O.P. Verma, S. Sinha, Modelling and simulation of desalination process using artificial neural network: A review, Desalin. Water Treat. 122 (2018) 351–364. [CrossRef] [Google Scholar]
  7. S.S. Ray, R.K. Verma, A. Singh, M. Ganesapillai, Y.N. Kwon, A holistic review on how artificial intelligence has redefined water treatment and seawater desalination processes, Desalination. 546 (2023) 116221. https://doi.org/10.1016/j.desal.2022.116221. [CrossRef] [Google Scholar]
  8. R. Kumar, R. Mahadeva, An Experimental Measurement and Control of Human Body Stomach Using Electrical Impedance Tomography, J. Circuits, Syst. Comput. 30 (2021) 1–17. [Google Scholar]
  9. V. Gupta, R. Mahle, A.B. Jayaswal, Design and implementation of TDBLMS adaptive filter and comparison of PSNR values of various de-noised images, in: Int. Conf. Commun. Signal Process. ICCSP 2013 - Proc., (2013), https://doi.org/10.1109/iccsp.2013.6577177. [Google Scholar]
  10. S. Sheikh, B. Suthar, Tamanna, M. Uddin, Comparative study of noise and digital filters for image processing, Int. Conf. Innov. Control. Commun. Inf. Syst. ICICCI 2017, 1–6, (2019) https://doi.org/10.1109/ICICCIS.2017.8660897. [Google Scholar]
  11. V. Patel, V. Chaurasia, R. Mahadeva, S.P. Patole, GARL-Net: Graph Based Adaptive Regularized Learning Deep Network for Breast Cancer Classification, IEEE Access. 11 (2023) 9095–9112. https://doi.org/10.1109/ACCESS.2023.3239671. [CrossRef] [Google Scholar]
  12. A.K. Itawadiya, R. Mahle, V. Patel, D. Kumar, Design a DSP operations using vedic mathematics, 2013 Int Conf. Commun. Signal Process.. 897–902, (2013) https://doi.org/10.1109/iccsp.2013.6577186. [Google Scholar]
  13. R. Mahadeva, M. Kumar, A. Goel, S.P. Patole, G. Manik, A Novel AGPSO3-based ANN Prediction Approach: Application to the RO Desalination Plant, Arab. J. Sci. Eng. (2023) 1–12. https://doi.org/10.1007/s13369-023-07631-0. [Google Scholar]
  14. P. Kumar, S.B. Prasad, D. Patel, K. Kumar, S. Dixit, S.N. Nikolaevna, Optimization of cycle time assembly line for mass manufacturing, Int. J. Interact. Des. Manuf., 1–12, (2023). [Google Scholar]
  15. R. Mahadeva, M. Kumar, S.P. Patole, G. Manik, PID Control Design using AGPSO Technique and its Application in TITO Reverse Osmosis Desalination Plant, IEEE Access, 10 (2022) 125881–125892. https://doi.org/10.1109/ACCESS.2022.3224127. [CrossRef] [Google Scholar]
  16. P. Singh, A. Adebanjo, N. Shafiq, S.N.A. Razak, V. Kumar, S.A. Farhan, I. Adebanjo, A. Singh, S. Dixit, S. Singh, Development of performance-based models for green concrete using multiple linear regression and artificial neural network, Int. J. Interact. Des. Manuf., 1–12, (2023). [Google Scholar]
  17. B. Suthar, S. Jung, Design and Feasibility Analysis of a Foldable Robot Arm for Drones Using a Twisted String Actuator: FRAD-TSA, IEEE Robot. Autom. Lett. 6 (2021) 5769–5775. https://doi.org/10.1109/LRA.2021.3084890. [CrossRef] [Google Scholar]
  18. R. Mahadeva, R. Mehta, G. Manik, A. Bhattacharya, An experimental and computational investigation of poly(piperizinamide) thin film composite membrane for salts separation from water using Artificial Neural Network, Desalin. Water Treat. 224 (2021) 106–121. [CrossRef] [Google Scholar]
  19. E.S. Salami, M. Ehetshami, A. Karimi-Jashni, M. Salari, S. Nikbakht Sheibani, A. Ehteshami, A mathematical method and artificial neural network modeling to simulate osmosis membrane’s performance, Model. Earth Syst. Environ. 2 (2016) 1–11. [CrossRef] [Google Scholar]
  20. M. Elimelech, W.A. Phillip, The future of seawater desalination: Energy, technology, and the environment, Science (80-. ). 333 (2011) 712–717. [CrossRef] [PubMed] [Google Scholar]
  21. O. A. Shvetsova and B. Suthar, in 2018 IEEE International Conference” Quality Management, Transport and Information Security, Information Technologies”(IT&QM&IS) (IEEE, 2018), pp. 873–876 [Google Scholar]
  22. S. Dixit, S. N. Mandal, J. V. Thanikal, and K. Saurabh, Ain Shams Engineering Journal 10, 555 (2019) [CrossRef] [Google Scholar]
  23. S. Dixit, S. N. Mandal, J. V. Thanikal, and K. Saurabh, in E3S Web of Conferences (2019) [Google Scholar]
  24. Y. Supriya, V. Srinivasa Reddy, M. V Seshagiri Rao, and S. Shrihari, International Journal of Recent Technology and Engineering 8, 5381 (2019) [Google Scholar]
  25. D. Srinivasacharya and D. Srikanth, Comptes Rendus - Mecanique 336, 820 (2008) [CrossRef] [Google Scholar]

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