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  • Open Access


    Credit Card Fraud Detection Using Improved Deep Learning Models

    Sumaya S. Sulaiman1,2,*, Ibraheem Nadher3, Sarab M. Hameed2

    CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 1049-1069, 2024, DOI:10.32604/cmc.2023.046051

    Abstract Fraud of credit cards is a major issue for financial organizations and individuals. As fraudulent actions become more complex, a demand for better fraud detection systems is rising. Deep learning approaches have shown promise in several fields, including detecting credit card fraud. However, the efficacy of these models is heavily dependent on the careful selection of appropriate hyperparameters. This paper introduces models that integrate deep learning models with hyperparameter tuning techniques to learn the patterns and relationships within credit card transaction data, thereby improving fraud detection. Three deep learning models: AutoEncoder (AE), Convolution Neural Network (CNN), and Long Short-Term Memory… More >

  • Open Access


    Deep Learning Based Sentiment Analysis of COVID-19 Tweets via Resampling and Label Analysis

    Mamoona Humayun1,*, Danish Javed2, Nz Jhanjhi2, Maram Fahaad Almufareh1, Saleh Naif Almuayqil1

    Computer Systems Science and Engineering, Vol.47, No.1, pp. 575-591, 2023, DOI:10.32604/csse.2023.038765

    Abstract Twitter has emerged as a platform that produces new data every day through its users which can be utilized for various purposes. People express their unique ideas and views on multiple topics thus providing vast knowledge. Sentiment analysis is critical from the corporate and political perspectives as it can impact decision-making. Since the proliferation of COVID-19, it has become an important challenge to detect the sentiment of COVID-19-related tweets so that people’s opinions can be tracked. The purpose of this research is to detect the sentiment of people regarding this problem with limited data as it can be challenging considering… More >

  • Open Access


    Imbalanced Data Classification Using SVM Based on Improved Simulated Annealing Featuring Synthetic Data Generation and Reduction

    Hussein Ibrahim Hussein1, Said Amirul Anwar2,*, Muhammad Imran Ahmad2

    CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 547-564, 2023, DOI:10.32604/cmc.2023.036025

    Abstract Imbalanced data classification is one of the major problems in machine learning. This imbalanced dataset typically has significant differences in the number of data samples between its classes. In most cases, the performance of the machine learning algorithm such as Support Vector Machine (SVM) is affected when dealing with an imbalanced dataset. The classification accuracy is mostly skewed toward the majority class and poor results are exhibited in the prediction of minority-class samples. In this paper, a hybrid approach combining data pre-processing technique and SVM algorithm based on improved Simulated Annealing (SA) was proposed. Firstly, the data pre-processing technique which… More >

  • Open Access


    Medical Image Demosaicing Based Design of Newton Gregory Interpolation Algorithm

    E. P. Kannan1,*, S. S. Vinsley2, T. V. Chithra3

    Intelligent Automation & Soft Computing, Vol.34, No.3, pp. 1675-1691, 2022, DOI:10.32604/iasc.2022.022707

    Abstract In this paper, Field-Programmable Gate Array (FPGA) implementation-based image demosaicing is carried out. The Newton Gregory interpolation algorithm is designed based on FPGA frame work. Interpolation is the method of assessing the value of a function for any in-between value of self-regulating variable, whereas the method of computing the value of the function outside the specified range is named extrapolation. The natural images are collected from Kodak image database and medical images are collected from UPOL (University of Phoenix Online) database. The proposed algorithm is executed on using Xilinx ISE (Integrated Synthesis Environment) Design Suite 14.2 and is confirmed on… More >

  • Open Access


    Aluminum Alloy Fatigue Crack Damage Prediction Based on Lamb Wave-Systematic Resampling Particle Filter Method

    Gaozheng Zhao1, Changchao Liu1, Lingyu Sun1, Ning Yang2, Lei Zhang1, Mingshun Jiang1, Lei Jia1, Qingmei Sui1,*

    Structural Durability & Health Monitoring, Vol.16, No.1, pp. 81-96, 2022, DOI:10.32604/sdhm.2022.016905

    Abstract Fatigue crack prediction is a critical aspect of prognostics and health management research. The particle filter algorithm based on Lamb wave is a potential tool to solve the nonlinear and non-Gaussian problems on fatigue growth, and it is widely used to predict the state of fatigue crack. This paper proposes a method of lamb wave-based early fatigue microcrack prediction with the aid of particle filters. With this method, which the changes in signal characteristics under different fatigue crack lengths are analyzed, and the state- and observation-equations of crack extension are established. Furthermore, an experiment is conducted to verify the feasibility… More >

  • Open Access


    Resampling Factor Estimation via Dual-Stream Convolutional Neural Network

    Shangjun Luo1, Junwei Luo1, Wei Lu1,*, Yanmei Fang1, Jinhua Zeng2, Shaopei Shi2, Yue Zhang3,4

    CMC-Computers, Materials & Continua, Vol.66, No.1, pp. 647-657, 2021, DOI:10.32604/cmc.2020.012869

    Abstract The estimation of image resampling factors is an important problem in image forensics. Among all the resampling factor estimation methods, spectrumbased methods are one of the most widely used methods and have attracted a lot of research interest. However, because of inherent ambiguity, spectrum-based methods fail to discriminate upscale and downscale operations without any prior information. In general, the application of resampling leaves detectable traces in both spatial domain and frequency domain of a resampled image. Firstly, the resampling process will introduce correlations between neighboring pixels. In this case, a set of periodic pixels that are correlated to their neighbors… More >

  • Open Access


    Speech Resampling Detection Based on Inconsistency of Band Energy

    Zhifeng Wang1, Diqun Yan1,*, Rangding Wang1, Li Xiang1, Tingting Wu1

    CMC-Computers, Materials & Continua, Vol.56, No.2, pp. 247-259, 2018, DOI: 10.3970/cmc.2018.02902

    Abstract Speech resampling is a typical tempering behavior, which is often integrated into various speech forgeries, such as splicing, electronic disguising, quality faking and so on. By analyzing the principle of resampling, we found that, compared with natural speech, the inconsistency between the bandwidth of the resampled speech and its sampling ratio will be caused because the interpolation process in resampling is imperfect. Based on our observation, a new resampling detection algorithm based on the inconsistency of band energy is proposed. First, according to the sampling ratio of the suspected speech, a band-pass Butterworth filter is designed to filter out the… More >

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