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  • Open Access


    CMAES-WFD: Adversarial Website Fingerprinting Defense Based on Covariance Matrix Adaptation Evolution Strategy

    Di Wang, Yuefei Zhu, Jinlong Fei*, Maohua Guo

    CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 2253-2276, 2024, DOI:10.32604/cmc.2024.049504

    Abstract Website fingerprinting, also known as WF, is a traffic analysis attack that enables local eavesdroppers to infer a user’s browsing destination, even when using the Tor anonymity network. While advanced attacks based on deep neural network (DNN) can perform feature engineering and attain accuracy rates of over 98%, research has demonstrated that DNN is vulnerable to adversarial samples. As a result, many researchers have explored using adversarial samples as a defense mechanism against DNN-based WF attacks and have achieved considerable success. However, these methods suffer from high bandwidth overhead or require access to the target model, which is unrealistic. This… More >

  • Open Access


    MoBShield: A Novel XML Approach for Securing Mobile Banking

    Saeed Seraj1, Ali Safaa Sadiq1,*, Omprakash Kaiwartya1, Mohammad Aljaidi2, Alexandros Konios1, Mohammed Ali3, Mohammed Abazeed3

    CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 2123-2149, 2024, DOI:10.32604/cmc.2024.048914

    Abstract Mobile banking security has witnessed significant R&D attention from both financial institutions and academia. This is due to the growing number of mobile baking applications and their reachability and usefulness to society. However, these applications are also attractive prey for cybercriminals, who use a variety of malware to steal personal banking information. Related literature in mobile banking security requires many permissions that are not necessary for the application’s intended security functionality. In this context, this paper presents a novel efficient permission identification approach for securing mobile banking (MoBShield) to detect and prevent malware. A permission-based dataset is generated for mobile… More >

  • Open Access


    U-Net Inspired Deep Neural Network-Based Smoke Plume Detection in Satellite Images

    Ananthakrishnan Balasundaram1,2, Ayesha Shaik1,2,*, Japmann Kaur Banga2, Aman Kumar Singh2

    CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 779-799, 2024, DOI:10.32604/cmc.2024.048362

    Abstract Industrial activities, through the human-induced release of Green House Gas (GHG) emissions, have been identified as the primary cause of global warming. Accurate and quantitative monitoring of these emissions is essential for a comprehensive understanding of their impact on the Earth’s climate and for effectively enforcing emission regulations at a large scale. This work examines the feasibility of detecting and quantifying industrial smoke plumes using freely accessible geo-satellite imagery. The existing system has so many lagging factors such as limitations in accuracy, robustness, and efficiency and these factors hinder the effectiveness in supporting timely response to industrial fires. In this… More >

  • Open Access


    A Survey on Chinese Sign Language Recognition: From Traditional Methods to Artificial Intelligence

    Xianwei Jiang1, Yanqiong Zhang1,*, Juan Lei1, Yudong Zhang2,3,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.1, pp. 1-40, 2024, DOI:10.32604/cmes.2024.047649

    Abstract Research on Chinese Sign Language (CSL) provides convenience and support for individuals with hearing impairments to communicate and integrate into society. This article reviews the relevant literature on Chinese Sign Language Recognition (CSLR) in the past 20 years. Hidden Markov Models (HMM), Support Vector Machines (SVM), and Dynamic Time Warping (DTW) were found to be the most commonly employed technologies among traditional identification methods. Benefiting from the rapid development of computer vision and artificial intelligence technology, Convolutional Neural Networks (CNN), 3D-CNN, YOLO, Capsule Network (CapsNet) and various deep neural networks have sprung up. Deep Neural Networks (DNNs) and their derived… More >

  • Open Access


    Nonparametric Statistical Feature Scaling Based Quadratic Regressive Convolution Deep Neural Network for Software Fault Prediction

    Sureka Sivavelu, Venkatesh Palanisamy*

    CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 3469-3487, 2024, DOI:10.32604/cmc.2024.047407

    Abstract The development of defect prediction plays a significant role in improving software quality. Such predictions are used to identify defective modules before the testing and to minimize the time and cost. The software with defects negatively impacts operational costs and finally affects customer satisfaction. Numerous approaches exist to predict software defects. However, the timely and accurate software bugs are the major challenging issues. To improve the timely and accurate software defect prediction, a novel technique called Nonparametric Statistical feature scaled QuAdratic regressive convolution Deep nEural Network (SQADEN) is introduced. The proposed SQADEN technique mainly includes two major processes namely metric… More >

  • Open Access


    Privacy-Preserving Federated Deep Learning Diagnostic Method for Multi-Stage Diseases

    Jinbo Yang1, Hai Huang1, Lailai Yin2, Jiaxing Qu3, Wanjuan Xie4,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.3, pp. 3085-3099, 2024, DOI:10.32604/cmes.2023.045417

    Abstract Diagnosing multi-stage diseases typically requires doctors to consider multiple data sources, including clinical symptoms, physical signs, biochemical test results, imaging findings, pathological examination data, and even genetic data. When applying machine learning modeling to predict and diagnose multi-stage diseases, several challenges need to be addressed. Firstly, the model needs to handle multimodal data, as the data used by doctors for diagnosis includes image data, natural language data, and structured data. Secondly, privacy of patients’ data needs to be protected, as these data contain the most sensitive and private information. Lastly, considering the practicality of the model, the computational requirements should… More >

  • Open Access


    A Method for Detecting and Recognizing Yi Character Based on Deep Learning

    Haipeng Sun1,2, Xueyan Ding1,2,*, Jian Sun1,2, Hua Yu3, Jianxin Zhang1,2,*

    CMC-Computers, Materials & Continua, Vol.78, No.2, pp. 2721-2739, 2024, DOI:10.32604/cmc.2024.046449

    Abstract Aiming at the challenges associated with the absence of a labeled dataset for Yi characters and the complexity of Yi character detection and recognition, we present a deep learning-based approach for Yi character detection and recognition. In the detection stage, an improved Differentiable Binarization Network (DBNet) framework is introduced to detect Yi characters, in which the Omni-dimensional Dynamic Convolution (ODConv) is combined with the ResNet-18 feature extraction module to obtain multi-dimensional complementary features, thereby improving the accuracy of Yi character detection. Then, the feature pyramid network fusion module is used to further extract Yi character image features, improving target recognition… More >

  • Open Access


    Machine Learning Techniques Using Deep Instinctive Encoder-Based Feature Extraction for Optimized Breast Cancer Detection

    Vaishnawi Priyadarshni1, Sanjay Kumar Sharma1, Mohammad Khalid Imam Rahmani2,*, Baijnath Kaushik3, Rania Almajalid2,*

    CMC-Computers, Materials & Continua, Vol.78, No.2, pp. 2441-2468, 2024, DOI:10.32604/cmc.2024.044963

    Abstract Breast cancer (BC) is one of the leading causes of death among women worldwide, as it has emerged as the most commonly diagnosed malignancy in women. Early detection and effective treatment of BC can help save women’s lives. Developing an efficient technology-based detection system can lead to non-destructive and preliminary cancer detection techniques. This paper proposes a comprehensive framework that can effectively diagnose cancerous cells from benign cells using the Curated Breast Imaging Subset of the Digital Database for Screening Mammography (CBIS-DDSM) data set. The novelty of the proposed framework lies in the integration of various techniques, where the fusion… More >

  • Open Access


    Optimizing Deep Neural Networks for Face Recognition to Increase Training Speed and Improve Model Accuracy

    Mostafa Diba*, Hossein Khosravi

    Intelligent Automation & Soft Computing, Vol.38, No.3, pp. 315-332, 2023, DOI:10.32604/iasc.2023.046590

    Abstract Convolutional neural networks continually evolve to enhance accuracy in addressing various problems, leading to an increase in computational cost and model size. This paper introduces a novel approach for pruning face recognition models based on convolutional neural networks. The proposed method identifies and removes inefficient filters based on the information volume in feature maps. In each layer, some feature maps lack useful information, and there exists a correlation between certain feature maps. Filters associated with these two types of feature maps impose additional computational costs on the model. By eliminating filters related to these categories of feature maps, the reduction… More >

  • Open Access


    Deep Neural Network Architecture Search via Decomposition-Based Multi-Objective Stochastic Fractal Search

    Hongshang Xu1, Bei Dong1,2,*, Xiaochang Liu1, Xiaojun Wu1,2

    Intelligent Automation & Soft Computing, Vol.38, No.2, pp. 185-202, 2023, DOI:10.32604/iasc.2023.041177

    Abstract Deep neural networks often outperform classical machine learning algorithms in solving real-world problems. However, designing better networks usually requires domain expertise and consumes significant time and computing resources. Moreover, when the task changes, the original network architecture becomes outdated and requires redesigning. Thus, Neural Architecture Search (NAS) has gained attention as an effective approach to automatically generate optimal network architectures. Most NAS methods mainly focus on achieving high performance while ignoring architectural complexity. A myriad of research has revealed that network performance and structural complexity are often positively correlated. Nevertheless, complex network structures will bring enormous computing resources. To cope… More >

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