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  • Open Access


    A Gaussian Noise-Based Algorithm for Enhancing Backdoor Attacks

    Hong Huang, Yunfei Wang*, Guotao Yuan, Xin Li

    CMC-Computers, Materials & Continua, Vol.80, No.1, pp. 361-387, 2024, DOI:10.32604/cmc.2024.051633

    Abstract Deep Neural Networks (DNNs) are integral to various aspects of modern life, enhancing work efficiency. Nonetheless, their susceptibility to diverse attack methods, including backdoor attacks, raises security concerns. We aim to investigate backdoor attack methods for image categorization tasks, to promote the development of DNN towards higher security. Research on backdoor attacks currently faces significant challenges due to the distinct and abnormal data patterns of malicious samples, and the meticulous data screening by developers, hindering practical attack implementation. To overcome these challenges, this study proposes a Gaussian Noise-Targeted Universal Adversarial Perturbation (GN-TUAP) algorithm. This approach… More >

  • Open Access


    Technique for Multi-Pass Turning Optimization Based on Gaussian Quantum-Behaved Bat Algorithm

    Shutong Xie, Zongbao He, Xingwang Huang*

    CMES-Computer Modeling in Engineering & Sciences, Vol.136, No.2, pp. 1575-1602, 2023, DOI:10.32604/cmes.2023.025812

    Abstract The multi-pass turning operation is one of the most commonly used machining methods in manufacturing field. The main objective of this operation is to minimize the unit production cost. This paper proposes a Gaussian quantum-behaved bat algorithm (GQBA) to solve the problem of multi-pass turning operation. The proposed algorithm mainly includes the following two improvements. The first improvement is to incorporate the current optimal positions of quantum bats and the global best position into the stochastic attractor to facilitate population diversification. The second improvement is to use a Gaussian distribution instead of the uniform distribution… More >

  • Open Access


    Build Gaussian Distribution Under Deep Features for Anomaly Detection and Localization

    Mei Wang1,*, Hao Xu2, Yadang Chen1

    Journal of New Media, Vol.4, No.4, pp. 179-190, 2022, DOI:10.32604/jnm.2022.032447

    Abstract Anomaly detection in images has attracted a lot of attention in the field of computer vision. It aims at identifying images that deviate from the norm and segmenting the defect within images. However, anomalous samples are difficult to collect comprehensively, and labeled data is costly to obtain in many practical scenarios. We proposes a simple framework for unsupervised anomaly detection. Specifically, the proposed method directly employs CNN pre-trained on ImageNet to extract deep features from normal images and reduce dimensionality based on Principal Components Analysis (PCA), then build the distribution of normal features via the More >

  • Open Access


    Stacking Ensemble Learning-Based Convolutional Gated Recurrent Neural Network for Diabetes Miletus

    G. Geetha1,2,*, K. Mohana Prasad1

    Intelligent Automation & Soft Computing, Vol.36, No.1, pp. 703-718, 2023, DOI:10.32604/iasc.2023.032530

    Abstract Diabetes mellitus is a metabolic disease in which blood glucose levels rise as a result of pancreatic insulin production failure. It causes hyperglycemia and chronic multiorgan dysfunction, including blindness, renal failure, and cardiovascular disease, if left untreated. One of the essential checks that are needed to be performed frequently in Type 1 Diabetes Mellitus is a blood test, this procedure involves extracting blood quite frequently, which leads to subject discomfort increasing the possibility of infection when the procedure is often recurring. Existing methods used for diabetes classification have less classification accuracy and suffer from vanishing… More >

  • Open Access


    Oversampling Method Based on Gaussian Distribution and K-Means Clustering

    Masoud Muhammed Hassan1, Adel Sabry Eesa1,*, Ahmed Jameel Mohammed2, Wahab Kh. Arabo1

    CMC-Computers, Materials & Continua, Vol.69, No.1, pp. 451-469, 2021, DOI:10.32604/cmc.2021.018280

    Abstract Learning from imbalanced data is one of the greatest challenging problems in binary classification, and this problem has gained more importance in recent years. When the class distribution is imbalanced, classical machine learning algorithms tend to move strongly towards the majority class and disregard the minority. Therefore, the accuracy may be high, but the model cannot recognize data instances in the minority class to classify them, leading to many misclassifications. Different methods have been proposed in the literature to handle the imbalance problem, but most are complicated and tend to simulate unnecessary noise. In this More >

  • Open Access


    Modeling Liver Cancer and Leukemia Data Using Arcsine-Gaussian Distribution

    Farouq Mohammad A. Alam1, Sharifah Alrajhi1, Mazen Nassar1,2, Ahmed Z. Afify3,*

    CMC-Computers, Materials & Continua, Vol.67, No.2, pp. 2185-2202, 2021, DOI:10.32604/cmc.2021.015089

    Abstract The main objective of this paper is to discuss a general family of distributions generated from the symmetrical arcsine distribution. The considered family includes various asymmetrical and symmetrical probability distributions as special cases. A particular case of a symmetrical probability distribution from this family is the Arcsine–Gaussian distribution. Key statistical properties of this distribution including quantile, mean residual life, order statistics and moments are derived. The Arcsine–Gaussian parameters are estimated using two classical estimation methods called moments and maximum likelihood methods. A simulation study which provides asymptotic distribution of all considered point estimators, 90% and… More >

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