Optimizing degradable plastic density prediction: a coarse-to-fine Deep Neural Network approach
Density is an important property for the production of high-quality degradable plastics. Density is useful to determine the type of plastic material and to detect physical changes in the plastic material. In this paper, a novel technique for predicting the density of degradable plastics using Deep N...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Penerbit Universiti Kebangsaan Malaysia
2024
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| Online Access: | http://journalarticle.ukm.my/23655/ http://journalarticle.ukm.my/23655/1/SDD%2017.pdf |
| _version_ | 1848815903837257728 |
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| author | Syamsiah Abu Bakar, Saiful Izzuan Hussain, Mourad, Zirour |
| author_facet | Syamsiah Abu Bakar, Saiful Izzuan Hussain, Mourad, Zirour |
| author_sort | Syamsiah Abu Bakar, |
| building | UKM Institutional Repository |
| collection | Online Access |
| description | Density is an important property for the production of high-quality degradable plastics. Density is useful to determine the type of plastic material and to detect physical changes in the plastic material. In this paper, a novel technique for predicting the density of degradable plastics using Deep Neural Networks (DNN) is presented. The aim was to reduce the dimension of the inputs in order to establish a strong relationship between the inputs using principal component analysis (PCA). The results show that the combination of polyethylene, oil palm biomass, starch and palm oil has a greater impact on predicting the density of degradable plastics. Subsequently, the number of hidden neurons is determined by a coarse-to-fine search to develop the network topology of the DNN model for predicting the density of degradable plastics. The developed DNN model consists of 4 input neurons, 62 neurons in the first hidden layer, 31 neurons in the second hidden layer and one output neuron. The developed DNN model showed high accuracy with the lowest values for RMSE, MAE and MSE, indicating that the use of a DNN model is a suitable method for predicting the density of degradable plastics. Furthermore, this study has the potential to make rapid and accurate predictions about the physical properties of degradable plastics in the context of polymers. |
| first_indexed | 2025-11-15T00:57:23Z |
| format | Article |
| id | oai:generic.eprints.org:23655 |
| institution | Universiti Kebangasaan Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T00:57:23Z |
| publishDate | 2024 |
| publisher | Penerbit Universiti Kebangsaan Malaysia |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | oai:generic.eprints.org:236552024-06-07T08:05:34Z http://journalarticle.ukm.my/23655/ Optimizing degradable plastic density prediction: a coarse-to-fine Deep Neural Network approach Syamsiah Abu Bakar, Saiful Izzuan Hussain, Mourad, Zirour Density is an important property for the production of high-quality degradable plastics. Density is useful to determine the type of plastic material and to detect physical changes in the plastic material. In this paper, a novel technique for predicting the density of degradable plastics using Deep Neural Networks (DNN) is presented. The aim was to reduce the dimension of the inputs in order to establish a strong relationship between the inputs using principal component analysis (PCA). The results show that the combination of polyethylene, oil palm biomass, starch and palm oil has a greater impact on predicting the density of degradable plastics. Subsequently, the number of hidden neurons is determined by a coarse-to-fine search to develop the network topology of the DNN model for predicting the density of degradable plastics. The developed DNN model consists of 4 input neurons, 62 neurons in the first hidden layer, 31 neurons in the second hidden layer and one output neuron. The developed DNN model showed high accuracy with the lowest values for RMSE, MAE and MSE, indicating that the use of a DNN model is a suitable method for predicting the density of degradable plastics. Furthermore, this study has the potential to make rapid and accurate predictions about the physical properties of degradable plastics in the context of polymers. Penerbit Universiti Kebangsaan Malaysia 2024 Article PeerReviewed application/pdf en http://journalarticle.ukm.my/23655/1/SDD%2017.pdf Syamsiah Abu Bakar, and Saiful Izzuan Hussain, and Mourad, Zirour (2024) Optimizing degradable plastic density prediction: a coarse-to-fine Deep Neural Network approach. Sains Malaysiana, 53 (2). pp. 447-459. ISSN 0126-6039 https://www.ukm.my/jsm/english_journals/vol53num2_2024/contentsVol53num2_2024.html |
| spellingShingle | Syamsiah Abu Bakar, Saiful Izzuan Hussain, Mourad, Zirour Optimizing degradable plastic density prediction: a coarse-to-fine Deep Neural Network approach |
| title | Optimizing degradable plastic density prediction: a coarse-to-fine Deep Neural Network approach |
| title_full | Optimizing degradable plastic density prediction: a coarse-to-fine Deep Neural Network approach |
| title_fullStr | Optimizing degradable plastic density prediction: a coarse-to-fine Deep Neural Network approach |
| title_full_unstemmed | Optimizing degradable plastic density prediction: a coarse-to-fine Deep Neural Network approach |
| title_short | Optimizing degradable plastic density prediction: a coarse-to-fine Deep Neural Network approach |
| title_sort | optimizing degradable plastic density prediction: a coarse-to-fine deep neural network approach |
| url | http://journalarticle.ukm.my/23655/ http://journalarticle.ukm.my/23655/ http://journalarticle.ukm.my/23655/1/SDD%2017.pdf |