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  • Open Access

    ARTICLE

    Deep Learning-Based Robust Morphed Face Authentication Framework for Online Systems

    Harsh Mankodiya1, Priyal Palkhiwala1, Rajesh Gupta1,*, Nilesh Kumar Jadav1, Sudeep Tanwar1, Osama Alfarraj2, Amr Tolba2, Maria Simona Raboaca3,4,*, Verdes Marina5

    CMC-Computers, Materials & Continua, Vol.77, No.1, pp. 1123-1142, 2023, DOI:10.32604/cmc.2023.038556

    Abstract The amalgamation of artificial intelligence (AI) with various areas has been in the picture for the past few years. AI has enhanced the functioning of several services, such as accomplishing better budgets, automating multiple tasks, and data-driven decision-making. Conducting hassle-free polling has been one of them. However, at the onset of the coronavirus in 2020, almost all worldly affairs occurred online, and many sectors switched to digital mode. This allows attackers to find security loopholes in digital systems and exploit them for their lucrative business. This paper proposes a three-layered deep learning (DL)-based authentication framework to develop a secure online… More >

  • Open Access

    ARTICLE

    Fuzzy Reputation Based Trust Mechanism for Mitigating Attacks in MANET

    S. Maheswari, R. Vijayabhasker*

    Intelligent Automation & Soft Computing, Vol.35, No.3, pp. 3677-3692, 2023, DOI:10.32604/iasc.2023.031422

    Abstract Mobile Ad-hoc Networks (MANET) usage across the globe is increasing by the day. Evaluating a node’s trust value has significant advantages since such network applications only run efficiently by involving trustable nodes. The trust values are estimated based on the reputation values of each node in the network by using different mechanisms. However, these mechanisms have various challenging issues which degrade the network performance. Hence, a novel Quality of Service (QoS) Trust Estimation with Black/Gray hole Attack Detection approach is proposed in this research work. Initially, the QoS-based trust estimation is proposed by using a Fuzzy logic scheme. The trust… More >

  • Open Access

    ARTICLE

    Attribute Weighted Naïve Bayes Classifier

    Lee-Kien Foo*, Sook-Ling Chua, Neveen Ibrahim

    CMC-Computers, Materials & Continua, Vol.71, No.1, pp. 1945-1957, 2022, DOI:10.32604/cmc.2022.022011

    Abstract The naïve Bayes classifier is one of the commonly used data mining methods for classification. Despite its simplicity, naïve Bayes is effective and computationally efficient. Although the strong attribute independence assumption in the naïve Bayes classifier makes it a tractable method for learning, this assumption may not hold in real-world applications. Many enhancements to the basic algorithm have been proposed in order to alleviate the violation of attribute independence assumption. While these methods improve the classification performance, they do not necessarily retain the mathematical structure of the naïve Bayes model and some at the expense of computational time. One approach… More >

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