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  • Open Access

    ARTICLE

    Cybersecurity Threats Detection Using Optimized Machine Learning Frameworks

    Nadir Omer1,*, Ahmed H. Samak2, Ahmed I. Taloba3,4, Rasha M. Abd El-Aziz3,5

    Computer Systems Science and Engineering, Vol.48, No.1, pp. 77-95, 2024, DOI:10.32604/csse.2023.039265

    Abstract Today’s world depends on the Internet to meet all its daily needs. The usage of the Internet is growing rapidly. The world is using the Internet more frequently than ever. The hazards of harmful attacks have also increased due to the growing reliance on the Internet. Hazards to cyber security are actions taken by someone with malicious intent to steal data, destroy computer systems, or disrupt them. Due to rising cyber security concerns, cyber security has emerged as the key component in the fight against all online threats, forgeries, and assaults. A device capable of identifying network irregularities and cyber-attacks… More >

  • Open Access

    PROCEEDINGS

    A Machine Learning Framework for Isogeometric Topology Optimization

    Haobo Zhang1, Ziao Zhuang1, Chen Yu2, Zhaohui Xia1,*

    The International Conference on Computational & Experimental Engineering and Sciences, Vol.27, No.3, pp. 1-2, 2023, DOI:10.32604/icces.2023.09091

    Abstract Topology optimization (TO) is an important and powerful tool to obtain efficient and lightweight structures in conceptional design stage and a series of representative methods are implemented [1-5]. TO are mainly based on the classical finite element analysis (FEA), resulting in an inconsistency between geometric model and analytical model. Besides, there are some drawbacks of low analysis accuracy, poor continuity between adjacent elements, and high computational cost for high-order meshes. Thus, isogeometric analysis (IGA) is proposed [6] to replace FEA in TO. Using the Non-Uniform Rational B-Splines (NURBS), IGA successfully eliminates the defects of the conventional FEA and forms a… More >

  • Open Access

    ARTICLE

    Impact Assessment of COVID-19 Pandemic Through Machine Learning Models

    Fawaz Jaber Alsolami1, Abdullah Saad Al-Malaise ALGhamdi2, Asif Irshad Khan1,*, Yoosef B. Abushark1, Abdulmohsen Almalawi1, Farrukh Saleem2, Alka Agrawal3, Rajeev Kumar3,4, Raees Ahmad Khan3

    CMC-Computers, Materials & Continua, Vol.68, No.3, pp. 2895-2912, 2021, DOI:10.32604/cmc.2021.017469

    Abstract Ever since its outbreak in the Wuhan city of China, COVID-19 pandemic has engulfed more than 211 countries in the world, leaving a trail of unprecedented fatalities. Even more debilitating than the infection itself, were the restrictions like lockdowns and quarantine measures taken to contain the spread of Coronavirus. Such enforced alienation affected both the mental and social condition of people significantly. Social interactions and congregations are not only integral part of work life but also form the basis of human evolvement. However, COVID-19 brought all such communication to a grinding halt. Digital interactions have failed to enthuse the fervor… More >

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