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  • Open Access


    DNEF: A New Ensemble Framework Based on Deep Network Structure

    Siyu Yang1, Ge Song1,*, Yuqiao Deng2, Changyu Liu1, Zhuoyu Ou1

    CMC-Computers, Materials & Continua, Vol.77, No.3, pp. 4055-4072, 2023, DOI:10.32604/cmc.2023.042277

    Abstract Deep neural networks have achieved tremendous success in various fields, and the structure of these networks is a key factor in their success. In this paper, we focus on the research of ensemble learning based on deep network structure and propose a new deep network ensemble framework (DNEF). Unlike other ensemble learning models, DNEF is an ensemble learning architecture of network structures, with serial iteration between the hidden layers, while base classifiers are trained in parallel within these hidden layers. Specifically, DNEF uses randomly sampled data as input and implements serial iteration based on the… More >

  • Open Access


    Detecting and Classifying Darknet Traffic Using Deep Network Chains

    Amr Munshi1,2,*, Majid Alotaibi1,2, Saud Alotaibi2,3, Wesam Al-Sabban2,3, Nasser Allheeib4

    Computer Systems Science and Engineering, Vol.47, No.1, pp. 891-902, 2023, DOI:10.32604/csse.2023.039374

    Abstract The anonymity of the darknet makes it attractive to secure communication lines from censorship. The analysis, monitoring, and categorization of Internet network traffic are essential for detecting darknet traffic that can generate a comprehensive characterization of dangerous users and assist in tracing malicious activities and reducing cybercrime. Furthermore, classifying darknet traffic is essential for real-time applications such as the timely monitoring of malware before attacks occur. This paper presents a two-stage deep network chain for detecting and classifying darknet traffic. In the first stage, anonymized darknet traffic, including VPN and Tor traffic related to hidden… More >

  • Open Access


    Modelling a Fused Deep Network Model for Pneumonia Prediction

    M. A. Ramitha*, N. Mohanasundaram, R. Santhosh

    Computer Systems Science and Engineering, Vol.46, No.3, pp. 2725-2739, 2023, DOI:10.32604/csse.2023.030504

    Abstract Deep Learning (DL) is known for its golden standard computing paradigm in the learning community. However, it turns out to be an extensively utilized computing approach in the ML field. Therefore, attaining superior outcomes over cognitive tasks based on human performance. The primary benefit of DL is its competency in learning massive data. The DL-based technologies have grown faster and are widely adopted to handle the conventional approaches resourcefully. Specifically, various DL approaches outperform the conventional ML approaches in real-time applications. Indeed, various research works are reviewed to understand the significance of the individual DL… More >

  • Open Access


    Guided Dropout: Improving Deep Networks Without Increased Computation

    Yifeng Liu1, Yangyang Li1,*, Zhongxiong Xu1, Xiaohan Liu1, Haiyong Xie2, Huacheng Zeng3

    Intelligent Automation & Soft Computing, Vol.36, No.3, pp. 2519-2528, 2023, DOI:10.32604/iasc.2023.033286

    Abstract Deep convolution neural networks are going deeper and deeper. However, the complexity of models is prone to overfitting in training. Dropout, one of the crucial tricks, prevents units from co-adapting too much by randomly dropping neurons during training. It effectively improves the performance of deep networks but ignores the importance of the differences between neurons. To optimize this issue, this paper presents a new dropout method called guided dropout, which selects the neurons to switch off according to the differences between the convolution kernel and preserves the informative neurons. It uses an unsupervised clustering algorithm… More >

  • Open Access


    MDNN: Predicting Student Engagement via Gaze Direction and Facial Expression in Collaborative Learning

    Yi Chen1,*, Jin Zhou1, Qianting Gao2, Jing Gao1, Wei Zhang3

    CMES-Computer Modeling in Engineering & Sciences, Vol.136, No.1, pp. 381-401, 2023, DOI:10.32604/cmes.2023.023234

    Abstract Prediction of students’ engagement in a Collaborative Learning setting is essential to improve the quality of learning. Collaborative learning is a strategy of learning through groups or teams. When cooperative learning behavior occurs, each student in the group should participate in teaching activities. Researchers showed that students who are actively involved in a class gain more. Gaze behavior and facial expression are important nonverbal indicators to reveal engagement in collaborative learning environments. Previous studies require the wearing of sensor devices or eye tracker devices, which have cost barriers and technical interference for daily teaching practice. More >

  • Open Access


    Action Recognition Based on CSI Signal Using Improved Deep Residual Network Model

    Jian Zhao1, Shangwu Chong1, Liang Huang1, Xin Li1, Chen He1, Jian Jia2,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.130, No.3, pp. 1827-1851, 2022, DOI:10.32604/cmes.2022.017654

    Abstract In this paper, we propose an improved deep residual network model to recognize human actions. Action data is composed of channel state information signals, which are continuous fine-grained signals. We replaced the traditional identity connection with the shrinking threshold module. The module automatically adjusts the threshold of the action data signal, and filters out signals that are not related to the principal components. We use the attention mechanism to improve the memory of the network model to the action signal, so as to better recognize the action. To verify the validity of the experiment more More >

  • Open Access


    Diagnosis of Various Skin Cancer Lesions Based on Fine-Tuned ResNet50 Deep Network

    Sameh Abd ElGhany1,2, Mai Ramadan Ibraheem3, Madallah Alruwaili4, Mohammed Elmogy5,*

    CMC-Computers, Materials & Continua, Vol.68, No.1, pp. 117-135, 2021, DOI:10.32604/cmc.2021.016102

    Abstract With the massive success of deep networks, there have been significant efforts to analyze cancer diseases, especially skin cancer. For this purpose, this work investigates the capability of deep networks in diagnosing a variety of dermoscopic lesion images. This paper aims to develop and fine-tune a deep learning architecture to diagnose different skin cancer grades based on dermatoscopic images. Fine-tuning is a powerful method to obtain enhanced classification results by the customized pre-trained network. Regularization, batch normalization, and hyperparameter optimization are performed for fine-tuning the proposed deep network. The proposed fine-tuned ResNet50 model successfully classified More >

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