Computational intelligence of Bayesian regularization backpropagation neural networks to study the thermo-bioconvection flow of hybrid-nanofluid with thermal Radiation: Biotechnological perspectives
This study aims to develop a deep neural network that utilizes Bayesian regularization to investigate the performance of gyrotactic and oxytactic microbes in hybrid nanofluid flow over a sheet, taking into account local thermal non-equilibrium effects and thermal radiation. Two different activation...
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| Vydáno v: | Journal of Radiation Research and Applied Sciences Ročník 18; číslo 3; s. 101777 |
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| Médium: | Journal Article |
| Jazyk: | angličtina |
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Elsevier B.V
01.09.2025
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| ISSN: | 1687-8507 |
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| Abstract | This study aims to develop a deep neural network that utilizes Bayesian regularization to investigate the performance of gyrotactic and oxytactic microbes in hybrid nanofluid flow over a sheet, taking into account local thermal non-equilibrium effects and thermal radiation. Two different activation functions, namely radial basis and log-sigmoid utilized in the designed network. The model improves the efficiency of pollutant removal by optimizing the dynamics of gyrotactic and oxytactic microbes under local thermal non-equilibrium conditions. The enhanced microbial activity for the degradation of organic waste and the breakdown of pollutants are made easier by the hybrid nanofluid's improved thermal and physicochemical characteristics. 80 % of the data fixed for the training of the deep neural network and 20 % data employed for the testing purpose. The base fluid water is coupled with titanium dioxide (TiO2) and iron oxide (Fe3O4) nanoparticles to produce a hybrid nanofluid. The input dataset of the network generated using the bvp4c command. To examine how the various parameters on the suggested model varied, three distinct examples were created. Various statistical metrics are taken into consideration to assess the network's accuracy and precision.
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| AbstractList | This study aims to develop a deep neural network that utilizes Bayesian regularization to investigate the performance of gyrotactic and oxytactic microbes in hybrid nanofluid flow over a sheet, taking into account local thermal non-equilibrium effects and thermal radiation. Two different activation functions, namely radial basis and log-sigmoid utilized in the designed network. The model improves the efficiency of pollutant removal by optimizing the dynamics of gyrotactic and oxytactic microbes under local thermal non-equilibrium conditions. The enhanced microbial activity for the degradation of organic waste and the breakdown of pollutants are made easier by the hybrid nanofluid's improved thermal and physicochemical characteristics. 80 % of the data fixed for the training of the deep neural network and 20 % data employed for the testing purpose. The base fluid water is coupled with titanium dioxide (TiO2) and iron oxide (Fe3O4) nanoparticles to produce a hybrid nanofluid. The input dataset of the network generated using the bvp4c command. To examine how the various parameters on the suggested model varied, three distinct examples were created. Various statistical metrics are taken into consideration to assess the network's accuracy and precision.
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| ArticleNumber | 101777 |
| Author | Khan, Ilyas Rajab, Adnan Burhan Zayani, Hafedh Mahmoud Medani, Mohamed Faiz, Zeshan Ben Khedher, Nidhal Abbas, Munawar Elaissi, Samira |
| Author_xml | – sequence: 1 givenname: Munawar surname: Abbas fullname: Abbas, Munawar email: m.abbas7478@gmail.com organization: Department of Mathematics, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Saveetha University, Chennai, 602105, Tamil Nadu, India – sequence: 2 givenname: Mohamed surname: Medani fullname: Medani, Mohamed organization: Applied College of Mahail Aseer, King Khalid University, Muhayil Aseer, 62529, Saudi Arabia – sequence: 3 givenname: Adnan Burhan surname: Rajab fullname: Rajab, Adnan Burhan organization: Department of Computer Engineering, College of Engineering, Knowledge University, Erbil, 44001, Iraq – sequence: 4 givenname: Samira surname: Elaissi fullname: Elaissi, Samira organization: Department of Physics, College of Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh, 11671, Saudi Arabia – sequence: 5 givenname: Hafedh Mahmoud surname: Zayani fullname: Zayani, Hafedh Mahmoud email: hafedh.zayani@nbu.edu.sa organization: Department of Electrical Engineering, College of Engineering, Northern Border University, Arar, Saudi Arabia – sequence: 6 givenname: Zeshan surname: Faiz fullname: Faiz, Zeshan organization: Department of Mathematics, COMSATS University Islamabad, Islamabad Campus, Park Road, Chak Shahzad, Islamabad, 45550, Pakistan – sequence: 7 givenname: Ilyas surname: Khan fullname: Khan, Ilyas email: i.said@mu.edu.sa organization: Department of Mathematics, College of Science Al-Zulfi, Majmaah University, Al-Majmaah, 11952, Saudi Arabia – sequence: 8 givenname: Nidhal surname: Ben Khedher fullname: Ben Khedher, Nidhal organization: Department of Mechanical Engineering, College of Engineering, University of Ha'il, 81451, Ha'il City, Saudi Arabia |
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| Issue | 3 |
| Keywords | Oxytactic and gyrotactic microbe Thermophoretic particle deposition Bayesian regularization algorithm Local thermal non-equilibrium conditions Hybrid nanofluid |
| Language | English |
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| Title | Computational intelligence of Bayesian regularization backpropagation neural networks to study the thermo-bioconvection flow of hybrid-nanofluid with thermal Radiation: Biotechnological perspectives |
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