Improving the classification of a nanocomposite using nanoparticles based on a meta-analysis study, recurrent neural network and recurrent neural network Monte-Carlo algorithms
This paper may be the first meta-analysis that presents a comprehensive synthesis of scientific works spanning the last five years, focusing on methodologies and results related to the analysis of nanocomposite using nanoparticles. The primary objective is to identify the optimal algorithm using sof...
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2024-11-01
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| Series: | Nanocomposites |
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| Online Access: | https://www.tandfonline.com/doi/10.1080/20550324.2024.2367181 |
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| author | Rania Loukil Wejden Gazehi Mongi Besbes |
| author_facet | Rania Loukil Wejden Gazehi Mongi Besbes |
| author_sort | Rania Loukil |
| collection | DOAJ |
| description | This paper may be the first meta-analysis that presents a comprehensive synthesis of scientific works spanning the last five years, focusing on methodologies and results related to the analysis of nanocomposite using nanoparticles. The primary objective is to identify the optimal algorithm using software information and leading to better classification methodology. Specifically, this study comes up with the advantages and the drawbacks of the most used algorithms and proposes an enhancement and performance of Recurrent Neural Networks based on Long Short Term Memory (LSTM) neurons. Besides, a comparison of Deep Learning methods for the classification of polymeric nanoparticles, with polypropylene serving as a case study will be implemented. Experiment comparisons are conducted to assess with one physical property, later expanded to four properties and finally to eight properties. Neural networks, including Artificial Neural Networks (ANN), Recurrent Neural Networks (RNN), and Recurrent Neural Networks-Monte Carlo, are employed for simulations. The evaluation criteria encompass accuracy, calculation time, mean square error (MSE) and other metrics. The findings contribute to the selection of an optimal algorithm for the analysis of polymeric nanoparticles, emphasizing the potential of Deep Learning methodologies, particularly Recurrent Neural Networks Monte Carlo, in advancing classification accuracy and efficiency. |
| format | Article |
| id | doaj-art-41d2b3e192b5400e8094955a1fabcec6 |
| institution | OA Journals |
| issn | 2055-0324 2055-0332 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Nanocomposites |
| spelling | doaj-art-41d2b3e192b5400e8094955a1fabcec62025-08-20T02:12:23ZengTaylor & Francis GroupNanocomposites2055-03242055-03322024-11-0110130933710.1080/20550324.2024.2367181Improving the classification of a nanocomposite using nanoparticles based on a meta-analysis study, recurrent neural network and recurrent neural network Monte-Carlo algorithmsRania Loukil0Wejden Gazehi1Mongi Besbes2Laboratory of Robotics Informatics, and Complex Systems, University of Tunis Manar, ENIT, Tunis, TunisiaLaboratory of Robotics Informatics, and Complex Systems, University of Tunis Manar, ENIT, Tunis, TunisiaLaboratory of Robotics Informatics, and Complex Systems, University of Tunis Manar, ENIT, Tunis, TunisiaThis paper may be the first meta-analysis that presents a comprehensive synthesis of scientific works spanning the last five years, focusing on methodologies and results related to the analysis of nanocomposite using nanoparticles. The primary objective is to identify the optimal algorithm using software information and leading to better classification methodology. Specifically, this study comes up with the advantages and the drawbacks of the most used algorithms and proposes an enhancement and performance of Recurrent Neural Networks based on Long Short Term Memory (LSTM) neurons. Besides, a comparison of Deep Learning methods for the classification of polymeric nanoparticles, with polypropylene serving as a case study will be implemented. Experiment comparisons are conducted to assess with one physical property, later expanded to four properties and finally to eight properties. Neural networks, including Artificial Neural Networks (ANN), Recurrent Neural Networks (RNN), and Recurrent Neural Networks-Monte Carlo, are employed for simulations. The evaluation criteria encompass accuracy, calculation time, mean square error (MSE) and other metrics. The findings contribute to the selection of an optimal algorithm for the analysis of polymeric nanoparticles, emphasizing the potential of Deep Learning methodologies, particularly Recurrent Neural Networks Monte Carlo, in advancing classification accuracy and efficiency.https://www.tandfonline.com/doi/10.1080/20550324.2024.2367181Deep learningpolymeric nanoparticlesmeta-analytic studyphysical propertiesclassification recurrent neural networkaccuracy |
| spellingShingle | Rania Loukil Wejden Gazehi Mongi Besbes Improving the classification of a nanocomposite using nanoparticles based on a meta-analysis study, recurrent neural network and recurrent neural network Monte-Carlo algorithms Nanocomposites Deep learning polymeric nanoparticles meta-analytic study physical properties classification recurrent neural network accuracy |
| title | Improving the classification of a nanocomposite using nanoparticles based on a meta-analysis study, recurrent neural network and recurrent neural network Monte-Carlo algorithms |
| title_full | Improving the classification of a nanocomposite using nanoparticles based on a meta-analysis study, recurrent neural network and recurrent neural network Monte-Carlo algorithms |
| title_fullStr | Improving the classification of a nanocomposite using nanoparticles based on a meta-analysis study, recurrent neural network and recurrent neural network Monte-Carlo algorithms |
| title_full_unstemmed | Improving the classification of a nanocomposite using nanoparticles based on a meta-analysis study, recurrent neural network and recurrent neural network Monte-Carlo algorithms |
| title_short | Improving the classification of a nanocomposite using nanoparticles based on a meta-analysis study, recurrent neural network and recurrent neural network Monte-Carlo algorithms |
| title_sort | improving the classification of a nanocomposite using nanoparticles based on a meta analysis study recurrent neural network and recurrent neural network monte carlo algorithms |
| topic | Deep learning polymeric nanoparticles meta-analytic study physical properties classification recurrent neural network accuracy |
| url | https://www.tandfonline.com/doi/10.1080/20550324.2024.2367181 |
| work_keys_str_mv | AT ranialoukil improvingtheclassificationofananocompositeusingnanoparticlesbasedonametaanalysisstudyrecurrentneuralnetworkandrecurrentneuralnetworkmontecarloalgorithms AT wejdengazehi improvingtheclassificationofananocompositeusingnanoparticlesbasedonametaanalysisstudyrecurrentneuralnetworkandrecurrentneuralnetworkmontecarloalgorithms AT mongibesbes improvingtheclassificationofananocompositeusingnanoparticlesbasedonametaanalysisstudyrecurrentneuralnetworkandrecurrentneuralnetworkmontecarloalgorithms |