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Synergistic Study of Reduced Graphene Oxide as Interfacial Buffer Layer in HTL-free Perovskite Solar Cells with Carbon Electrode

Authors :
Sherifdeen O. Bolarinwa
Eli Danladi
Andrew Ichoja
Muhammad Y. Onimisia
Christopher U. Achem
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.

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