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Search Results (20)
  • Open Access

    ARTICLE

    Handling Class Imbalance in Online Transaction Fraud Detection

    Kanika1, Jimmy Singla1, Ali Kashif Bashir2, Yunyoung Nam3,*, Najam UI Hasan4, Usman Tariq5

    CMC-Computers, Materials & Continua, Vol.70, No.2, pp. 2861-2877, 2022, DOI:10.32604/cmc.2022.019990

    Abstract With the rise of internet facilities, a greater number of people have started doing online transactions at an exponential rate in recent years as the online transaction system has eliminated the need of going to the bank physically for every transaction. However, the fraud cases have also increased causing the loss of money to the consumers. Hence, an effective fraud detection system is the need of the hour which can detect fraudulent transactions automatically in real-time. Generally, the genuine transactions are large in number than the fraudulent transactions which leads to the class imbalance problem. In this research work, an… More >

  • Open Access

    ARTICLE

    Efficient Concurrent L1-Minimization Solvers on GPUs

    Xinyue Chu1, Jiaquan Gao1,*, Bo Sheng2

    Computer Systems Science and Engineering, Vol.38, No.3, pp. 305-320, 2021, DOI:10.32604/csse.2021.017144

    Abstract Given that the concurrent L1-minimization (L1-min) problem is often required in some real applications, we investigate how to solve it in parallel on GPUs in this paper. First, we propose a novel self-adaptive warp implementation of the matrix-vector multiplication (Ax) and a novel self-adaptive thread implementation of the matrix-vector multiplication (ATx), respectively, on the GPU. The vector-operation and inner-product decision trees are adopted to choose the optimal vector-operation and inner-product kernels for vectors of any size. Second, based on the above proposed kernels, the iterative shrinkage-thresholding algorithm is utilized to present two concurrent L1-min solvers from the perspective of the… More >

  • Open Access

    ARTICLE

    An Improved Jellyfish Algorithm for Multilevel Thresholding of Magnetic Resonance Brain Image Segmentations

    Mohamed Abdel-Basset1, Reda Mohamed1, Mohamed Abouhawwash2,3, Ripon K. Chakrabortty4, Michael J. Ryan4, Yunyoung Nam5,*

    CMC-Computers, Materials & Continua, Vol.68, No.3, pp. 2961-2977, 2021, DOI:10.32604/cmc.2021.016956

    Abstract Image segmentation is vital when analyzing medical images, especially magnetic resonance (MR) images of the brain. Recently, several image segmentation techniques based on multilevel thresholding have been proposed for medical image segmentation; however, the algorithms become trapped in local minima and have low convergence speeds, particularly as the number of threshold levels increases. Consequently, in this paper, we develop a new multilevel thresholding image segmentation technique based on the jellyfish search algorithm (JSA) (an optimizer). We modify the JSA to prevent descents into local minima, and we accelerate convergence toward optimal solutions. The improvement is achieved by applying two novel… More >

  • Open Access

    ARTICLE

    Threshold Parameters Selection for Empirical Mode Decomposition-Based EMG Signal Denoising

    Hassan Ashraf1, Asim Waris1,*, Syed Omer Gilani1, Muhammad Umair Tariq1, Hani Alquhayz2

    Intelligent Automation & Soft Computing, Vol.27, No.3, pp. 799-815, 2021, DOI:10.32604/iasc.2021.014765

    Abstract Empirical Mode Decomposition (EMD) is a data-driven and fully adaptive signal decomposition technique to decompose a signal into its Intrinsic Mode Functions (IMF). EMD has attained great attention due to its capabilities to process a signal in the frequency-time domain without altering the signal into the frequency domain. EMD-based signal denoising techniques have shown great potential to denoise nonlinear and nonstationary signals without compromising the signal’s characteristics. The denoising procedure comprises three steps, i.e., signal decomposition, IMF thresholding, and signal reconstruction. Thresholding is performed to assess which IMFs contain noise. In this study, Interval Thresholding (IT), Iterative Interval Thresholding (IIT),… More >

  • Open Access

    ARTICLE

    Novel Adaptive Binarization Method for Degraded Document Images

    Siti Norul Huda Sheikh Abdullah1, Saad M. Ismail1,2, Mohammad Kamrul Hasan1,*, Palaiahnakote Shivakumara3

    CMC-Computers, Materials & Continua, Vol.67, No.3, pp. 3815-3832, 2021, DOI:10.32604/cmc.2021.014610

    Abstract Achieving a good recognition rate for degraded document images is difficult as degraded document images suffer from low contrast, bleed-through, and nonuniform illumination effects. Unlike the existing baseline thresholding techniques that use fixed thresholds and windows, the proposed method introduces a concept for obtaining dynamic windows according to the image content to achieve better binarization. To enhance a low-contrast image, we proposed a new mean histogram stretching method for suppressing noisy pixels in the background and, simultaneously, increasing pixel contrast at edges or near edges, which results in an enhanced image. For the enhanced image, we propose a new method… More >

  • Open Access

    ARTICLE

    Tumor Classfication UsingG Automatic Multi-thresholding

    Li-Hong Juanga, Ming-Ni Wub

    Intelligent Automation & Soft Computing, Vol.24, No.2, pp. 257-266, 2018, DOI:10.1080/10798587.2016.1272778

    Abstract In this paper we explore these math approaches for medical image applications. The application of the proposed method for detection tumor will be able to distinguish exactly tumor size and region. In this research, some major design and experimental results of tumor objects detection method for medical brain images is developed to utilize an automatic multi-thresholding method to handle this problem by combining the histogram analysis and the Otsu clustering. The histogram evaluations can decide the superior number of clusters firstly. The Otsu classification algorithm solves the given medical image by continuously separating the input gray-level image by multi-thresholding until… More >

  • Open Access

    ARTICLE

    Image Segmentation of Brain MR Images Using Otsu’s Based Hybrid WCMFO Algorithm

    A. Renugambal1, *, K. Selva Bhuvaneswari2

    CMC-Computers, Materials & Continua, Vol.64, No.2, pp. 681-700, 2020, DOI:10.32604/cmc.2020.09519

    Abstract In this study, a novel hybrid Water Cycle Moth-Flame Optimization (WCMFO) algorithm is proposed for multilevel thresholding brain image segmentation in Magnetic Resonance (MR) image slices. WCMFO constitutes a hybrid between the two techniques, comprising the water cycle and moth-flame optimization algorithms. The optimal thresholds are obtained by maximizing the between class variance (Otsu’s function) of the image. To test the performance of threshold searching process, the proposed algorithm has been evaluated on standard benchmark of ten axial T2-weighted brain MR images for image segmentation. The experimental outcomes infer that it produces better optimal threshold values at a greater and… More >

  • Open Access

    ARTICLE

    Hybrid Deep VGG-NET Convolutional Classifier for Video Smoke Detection

    Princy Matlani1,*, Manish Shrivastava1

    CMES-Computer Modeling in Engineering & Sciences, Vol.119, No.3, pp. 427-458, 2019, DOI:10.32604/cmes.2019.04985

    Abstract Real-time wild smoke detection utilizing machine based identification method is not produced proper accuracy, and it is not suitable for accurate prediction. However, various video smoke detection approaches involve minimum lighting, and it is required for the cameras to identify the existence of smoke particles in a scene. To overcome such challenges, our proposed work introduces a novel concept like deep VGG-Net Convolutional Neural Network (CNN) for the classification of smoke particles. This Deep Feature Synthesis algorithm automatically generated the characteristics for relational datasets. Also hybrid ABC optimization rectifies the problem related to the slow convergence since complexity is reduced.… More >

  • Open Access

    ARTICLE

    Thermo-Mechanical Analysis of Restored Molar Tooth using Finite Element Analysis

    R. V. Uddanwadiker*

    Molecular & Cellular Biomechanics, Vol.10, No.4, pp. 289-302, 2013, DOI:10.3970/mcb.2013.010.289

    Abstract The aim of the study is to find most optimum combination of crown material and adhesive to avoid loosening and thereby failure of restored tooth. This study describes the Thermo-Mechanical analysis of restored molar tooth crown for determination of the stress levels due to thermal and mechanical loads on restored molar tooth. The potential use of the 3-D model was demonstrated and analyzed using different materials for crown. Thermal strain, stress and deformation were measured at hot and cold conditions in ANSYS and correlated with analytical calculation and existing experimental data for model validation and optimization. It is concluded that… More >

  • Open Access

    ARTICLE

    Automatic Delineation of Lung Parenchyma Based on Multilevel Thresholding and Gaussian Mixture Modelling

    S. Gopalakrishnan1, *, A. Kandaswamy2

    CMES-Computer Modeling in Engineering & Sciences, Vol.114, No.2, pp. 141-152, 2018, DOI:10.3970/cmes.2018.114.141

    Abstract Delineation of the lung parenchyma in the thoracic Computed Tomography (CT) is an important processing step for most of the pulmonary image analysis such as lung volume extraction, lung nodule detection and pulmonary vessel segmentation. An automatic method for accurate delineation of lung parenchyma in thoracic Computed Tomography images is presented in this paper. The proposed method involves a segmentation phase followed by a lung boundary correction technique. The tissues in the thoracic Computed Tomography can be represented by a number of Gaussians. We propose a histogram utilized Adaptive Multilevel Thresholding (AMT) for estimating the total number of Gaussians and… More >

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