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Synergistic Study of Reduced Graphene Oxide as Interfacial Buffer Layer in HTL-free Perovskite Solar Cells with Carbon Electrode
- Source :
- Journal of Nigerian Society of Physical Sciences, Vol 4, Iss 3 (2022)
- Publication Year :
- 2022
- Publisher :
- Nigerian Society of Physical Sciences, 2022.
-
Abstract
- The application of machine learning algorithms to the detection of fraudulent credit card transactions is a challenging problem domain due to the high imbalance in the datasets and confidentiality of financial data. This implies that legitimate transactions make up a high majority of the datasets such that a weak model with 99% accuracy and faulty predictions may still be assessed as high-performing. To build optimal models, four techniques were used in this research to sample the datasets including the baseline train test split method, the class weighted hyperparameter approach, and the undersampling and oversampling techniques. Three machine learning algorithms were implemented for the development of the models including the Random Forest, XGBoost and TensorFlow Deep Neural Network (DNN). Our observation is that the DNN is more efficient than the other 2 algorithms in modelling the under-sampled dataset while overall, the three algorithms had a better performance in the oversampling technique than in the undersampling technique. However, the Random Forest performed better than the other algorithms in the baseline approach. After comparing our results with some existing state-of-the-art works, we achieved an improved performance using real-world datasets.
- Subjects :
- Perovskite Solar Cells
Reduced Graphene Oxide
buffer layer
HTM
Physics
QC1-999
Subjects
Details
- Language :
- English
- ISSN :
- 27142817 and 27144704
- Volume :
- 4
- Issue :
- 3
- Database :
- Directory of Open Access Journals
- Journal :
- Journal of Nigerian Society of Physical Sciences
- Publication Type :
- Academic Journal
- Accession number :
- edsdoj.7a58e75ad8124cddab53713dfa6e4ed4
- Document Type :
- article
- Full Text :
- https://doi.org/10.46481/jnsps.2022.909