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  • Open Access


    Machine Learning Security Defense Algorithms Based on Metadata Correlation Features

    Ruchun Jia, Jianwei Zhang*, Yi Lin

    CMC-Computers, Materials & Continua, Vol.78, No.2, pp. 2391-2418, 2024, DOI:10.32604/cmc.2024.044149

    Abstract With the popularization of the Internet and the development of technology, cyber threats are increasing day by day. Threats such as malware, hacking, and data breaches have had a serious impact on cybersecurity. The network security environment in the era of big data presents the characteristics of large amounts of data, high diversity, and high real-time requirements. Traditional security defense methods and tools have been unable to cope with the complex and changing network security threats. This paper proposes a machine-learning security defense algorithm based on metadata association features. Emphasize control over unauthorized users through… More >

  • Open Access


    Deep Capsule Residual Networks for Better Diagnosis Rate in Medical Noisy Images

    P. S. Arthy1,*, A. Kavitha2

    Intelligent Automation & Soft Computing, Vol.36, No.3, pp. 2959-2971, 2023, DOI:10.32604/iasc.2023.032511

    Abstract With the advent of Machine and Deep Learning algorithms, medical image diagnosis has a new perception of diagnosis and clinical treatment. Regrettably, medical images are more susceptible to capturing noises despite the peak in intelligent imaging techniques. However, the presence of noise images degrades both the diagnosis and clinical treatment processes. The existing intelligent methods suffer from the deficiency in handling the diverse range of noise in the versatile medical images. This paper proposes a novel deep learning network which learns from the substantial extent of noise in medical data samples to alleviate this challenge.… More >

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