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  • Open Access


    A Credit Card Fraud Detection Model Based on Multi-Feature Fusion and Generative Adversarial Network

    Yalong Xie1, Aiping Li1,*, Biyin Hu2, Liqun Gao1, Hongkui Tu1

    CMC-Computers, Materials & Continua, Vol.76, No.3, pp. 2707-2726, 2023, DOI:10.32604/cmc.2023.037039

    Abstract Credit Card Fraud Detection (CCFD) is an essential technology for banking institutions to control fraud risks and safeguard their reputation. Class imbalance and insufficient representation of feature data relating to credit card transactions are two prevalent issues in the current study field of CCFD, which significantly impact classification models’ performance. To address these issues, this research proposes a novel CCFD model based on Multifeature Fusion and Generative Adversarial Networks (MFGAN). The MFGAN model consists of two modules: a multi-feature fusion module for integrating static and dynamic behavior data of cardholders into a unified highdimensional feature space, and a balance module… More >

  • Open Access


    Imbalanced Classification in Diabetics Using Ensembled Machine Learning

    M. Sandeep Kumar1, Mohammad Zubair Khan2,*, Sukumar Rajendran1, Ayman Noor3, A. Stephen Dass1, J. Prabhu1

    CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 4397-4409, 2022, DOI:10.32604/cmc.2022.025865

    Abstract Diabetics is one of the world’s most common diseases which are caused by continued high levels of blood sugar. The risk of diabetics can be lowered if the diabetic is found at the early stage. In recent days, several machine learning models were developed to predict the diabetic presence at an early stage. In this paper, we propose an embedded-based machine learning model that combines the split-vote method and instance duplication to leverage an imbalanced dataset called PIMA Indian to increase the prediction of diabetics. The proposed method uses both the concept of over-sampling and under-sampling along with model weighting… More >

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