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  • Open Access

    ARTICLE

    Classifying Network Flows through a Multi-Modal 1D CNN Approach Using Unified Traffic Representations

    Ravi Veerabhadrappa*, Poornima Athikatte Sampigerayappa

    Computer Systems Science and Engineering, Vol.49, pp. 333-351, 2025, DOI:10.32604/csse.2025.061285 - 19 March 2025

    Abstract In recent years, the analysis of encrypted network traffic has gained momentum due to the widespread use of Transport Layer Security and Quick UDP Internet Connections protocols, which complicate and prolong the analysis process. Classification models face challenges in understanding and classifying unknown traffic because of issues related to interpret ability and the representation of traffic data. To tackle these complexities, multi-modal representation learning can be employed to extract meaningful features and represent them in a lower-dimensional latent space. Recently, auto-encoder-based multi-modal representation techniques have shown superior performance in representing network traffic. By combining the… More >

  • Open Access

    ARTICLE

    Emotion Detection Using ECG Signals and a Lightweight CNN Model

    Amita U. Dessai*, Hassanali G. Virani

    Computer Systems Science and Engineering, Vol.48, No.5, pp. 1193-1211, 2024, DOI:10.32604/csse.2024.052710 - 13 September 2024

    Abstract Emotion recognition is a growing field that has numerous applications in smart healthcare systems and Human-Computer Interaction (HCI). However, physical methods of emotion recognition such as facial expressions, voice, and text data, do not always indicate true emotions, as users can falsify them. Among the physiological methods of emotion detection, Electrocardiogram (ECG) is a reliable and efficient way of detecting emotions. ECG-enabled smart bands have proven effective in collecting emotional data in uncontrolled environments. Researchers use deep machine learning techniques for emotion recognition using ECG signals, but there is a need to develop efficient models… More >

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