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  • Open Access

    ARTICLE

    Assessment of Sentiment Analysis Using Information Gain Based Feature Selection Approach

    R. Madhumathi1,*, A. Meena Kowshalya2, R. Shruthi1

    Computer Systems Science and Engineering, Vol.43, No.2, pp. 849-860, 2022, DOI:10.32604/csse.2022.023568

    Abstract Sentiment analysis is the process of determining the intention or emotion behind an article. The subjective information from the context is analyzed by the sentimental analysis of the people’s opinion. The data that is analyzed quantifies the reactions or sentiments and reveals the information’s contextual polarity. In social behavior, sentiment can be thought of as a latent variable. Measuring and comprehending this behavior could help us to better understand the social issues. Because sentiments are domain specific, sentimental analysis in a specific context is critical in any real-world scenario. Textual sentiment analysis is done in sentence, document level and feature… 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 >

  • Open Access

    ARTICLE

    Improved Dragonfly Optimizer for Intrusion Detection Using Deep Clustering CNN-PSO Classifier

    K. S. Bhuvaneshwari1, K. Venkatachalam2, S. Hubálovský3,*, P. Trojovský4, P. Prabu5

    CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 5949-5965, 2022, DOI:10.32604/cmc.2022.020769

    Abstract With the rapid growth of internet based services and the data generated on these services are attracted by the attackers to intrude the networking services and information. Based on the characteristics of these intruders, many researchers attempted to aim to detect the intrusion with the help of automating process. Since, the large volume of data is generated and transferred through network, the security and performance are remained an issue. IDS (Intrusion Detection System) was developed to detect and prevent the intruders and secure the network systems. The performance and loss are still an issue because of the features space grows… More >

  • Open Access

    ARTICLE

    Few-Shot Learning for Discovering Anomalous Behaviors in Edge Networks

    Merna Gamal1, Hala M. Abbas2, Nour Moustafa3,*, Elena Sitnikova3, Rowayda A. Sadek1

    CMC-Computers, Materials & Continua, Vol.69, No.2, pp. 1823-1837, 2021, DOI:10.32604/cmc.2021.012877

    Abstract Intrusion Detection Systems (IDSs) have a great interest these days to discover complex attack events and protect the critical infrastructures of the Internet of Things (IoT) networks. Existing IDSs based on shallow and deep network architectures demand high computational resources and high volumes of data to establish an adaptive detection engine that discovers new families of attacks from the edge of IoT networks. However, attackers exploit network gateways at the edge using new attacking scenarios (i.e., zero-day attacks), such as ransomware and Distributed Denial of Service (DDoS) attacks. This paper proposes new IDS based on Few-Shot Deep Learning, named CNN-IDS,… More >

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