Research Articles in Computer Science

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    A Review of Fraudulent Practices in Healthcare Insurance and Machine Learning-Based Investigation Approaches
    (IEEE, 2023-02-02) Aishat Salau; Nwojo Agwu Nnanna; Moussa Mahamat Boukar
    Healthcare insurance fraud is a complex and costly problem that has become a concern globally. Traditional methods of detecting fraudulent claims and requests are time-consuming and often ineffective. Machine learning methods offer potential solutions to this problem by improving fraud investigation and prevention in health insurance systems. This paper presents a comprehensive review of machine learning-based approaches for addressing healthcare insurance fraud, as well as associated challenges and limitations. Despite limitations, our findings suggest that fraud could be effectively tackled by addressing the challenges identified. Areas for further research were also highlighted.
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    Remote Sensing Image Classification for Land Cover Mapping in Developing Countries
    (IJCSNS International Journal of Computer Science and Network Security, 2022-02-05) Nwojo Agwu Nnanna; Moussa Mahamat Boukar
    Convolutional Neural networks (CNNs) are a category of deep learning networks that have proven very effective in computer vision tasks such as image classification. Notwithstanding, not much has been seen in its use for remote sensing image classification in developing countries. This is majorly due to the scarcity of training data. Recently, transfer learning technique has successfully been used to develop state-of-the art models for remote sensing (RS) image classification tasks using training and testing data from well-known RS data repositories. However, the ability of such model to classify RS test data from a different dataset has not been sufficiently investigated. In this paper, we propose a deep CNN model that can classify RS test data from a dataset different from the training dataset. To achieve our objective, we first, re-trained a ResNet-50 model using EuroSAT, a large-scale RS dataset to develop a base model then we integrated Augmentation and Ensemble learning to improve its generalization ability. We further experimented on the ability of this model to classify a novel dataset (Nig_Images). The final classification results shows that our model achieves a 96% and 80% accuracy on EuroSAT and Nig_Images test data respectively. Adequate knowledge and usage of this framework is expected to encourage research and the usage of deep CNNs for land cover mapping in cases of lack of training data as obtainable in developing countries.