Deep Learning-Based Prediction of Physical Stability considering Class Imbalance for Amorphous Solid Dispersions
This research is aimed at predicting the physical stability for amorphous solid dispersion by utilizing deep learning methods. We propose a prediction model that effectively learns from a small dataset that is imbalanced in terms of class. In order to overcome the imbalance problem, our model perfor...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2022-01-01
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| Series: | Journal of Chemistry |
| Online Access: | http://dx.doi.org/10.1155/2022/4148443 |
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| _version_ | 1832551296617414656 |
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| author | Hanbyul Lee Junghyun Kim Suyeon Kim Jimin Yoo Guang J. Choi Young-Seob Jeong |
| author_facet | Hanbyul Lee Junghyun Kim Suyeon Kim Jimin Yoo Guang J. Choi Young-Seob Jeong |
| author_sort | Hanbyul Lee |
| collection | DOAJ |
| description | This research is aimed at predicting the physical stability for amorphous solid dispersion by utilizing deep learning methods. We propose a prediction model that effectively learns from a small dataset that is imbalanced in terms of class. In order to overcome the imbalance problem, our model performs a hybrid sampling which combines synthetic minority oversampling technique (SMOTE) algorithm with edited nearest neighbor (ENN) algorithm and reduces the dimensionality of the dataset using principal component analysis (PCA) algorithm during data preprocessing. After the preprocessing, it performs the learning process using a carefully designed neural network of simple but effective structure. Experimental results show that the proposed model has faster training convergence speed and better test performance compared to the existing DNN model. Furthermore, it significantly reduces the computational complexity of both training and test processes. |
| format | Article |
| id | doaj-art-fa079cf54b5646899a9ca98c67707bf2 |
| institution | Kabale University |
| issn | 2090-9071 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Chemistry |
| spelling | doaj-art-fa079cf54b5646899a9ca98c67707bf22025-02-03T06:01:51ZengWileyJournal of Chemistry2090-90712022-01-01202210.1155/2022/4148443Deep Learning-Based Prediction of Physical Stability considering Class Imbalance for Amorphous Solid DispersionsHanbyul Lee0Junghyun Kim1Suyeon Kim2Jimin Yoo3Guang J. Choi4Young-Seob Jeong5Department of Bigdata EngineeringDepartment of Bigdata EngineeringDepartment of Bigdata EngineeringDepartment of Bigdata EngineeringDepartment of Medical SciencesDepartment of Computer EngineeringThis research is aimed at predicting the physical stability for amorphous solid dispersion by utilizing deep learning methods. We propose a prediction model that effectively learns from a small dataset that is imbalanced in terms of class. In order to overcome the imbalance problem, our model performs a hybrid sampling which combines synthetic minority oversampling technique (SMOTE) algorithm with edited nearest neighbor (ENN) algorithm and reduces the dimensionality of the dataset using principal component analysis (PCA) algorithm during data preprocessing. After the preprocessing, it performs the learning process using a carefully designed neural network of simple but effective structure. Experimental results show that the proposed model has faster training convergence speed and better test performance compared to the existing DNN model. Furthermore, it significantly reduces the computational complexity of both training and test processes.http://dx.doi.org/10.1155/2022/4148443 |
| spellingShingle | Hanbyul Lee Junghyun Kim Suyeon Kim Jimin Yoo Guang J. Choi Young-Seob Jeong Deep Learning-Based Prediction of Physical Stability considering Class Imbalance for Amorphous Solid Dispersions Journal of Chemistry |
| title | Deep Learning-Based Prediction of Physical Stability considering Class Imbalance for Amorphous Solid Dispersions |
| title_full | Deep Learning-Based Prediction of Physical Stability considering Class Imbalance for Amorphous Solid Dispersions |
| title_fullStr | Deep Learning-Based Prediction of Physical Stability considering Class Imbalance for Amorphous Solid Dispersions |
| title_full_unstemmed | Deep Learning-Based Prediction of Physical Stability considering Class Imbalance for Amorphous Solid Dispersions |
| title_short | Deep Learning-Based Prediction of Physical Stability considering Class Imbalance for Amorphous Solid Dispersions |
| title_sort | deep learning based prediction of physical stability considering class imbalance for amorphous solid dispersions |
| url | http://dx.doi.org/10.1155/2022/4148443 |
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