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  • Open Access


    Insider Attack Detection Using Deep Belief Neural Network in Cloud Computing

    A. S. Anakath1,*, R. Kannadasan2, Niju P. Joseph3, P. Boominathan4, G. R. Sreekanth5

    Computer Systems Science and Engineering, Vol.41, No.2, pp. 479-492, 2022, DOI:10.32604/csse.2022.019940

    Abstract Cloud computing is a high network infrastructure where users, owners, third users, authorized users, and customers can access and store their information quickly. The use of cloud computing has realized the rapid increase of information in every field and the need for a centralized location for processing efficiently. This cloud is nowadays highly affected by internal threats of the user. Sensitive applications such as banking, hospital, and business are more likely affected by real user threats. An intruder is presented as a user and set as a member of the network. After becoming an insider in the network, they will… More >

  • Open Access


    Multi-View Multi-Modal Head-Gaze Estimation for Advanced Indoor User Interaction

    Jung-Hwa Kim1, Jin-Woo Jeong2,*

    CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 5107-5132, 2022, DOI:10.32604/cmc.2022.021107

    Abstract Gaze estimation is one of the most promising technologies for supporting indoor monitoring and interaction systems. However, previous gaze estimation techniques generally work only in a controlled laboratory environment because they require a number of high-resolution eye images. This makes them unsuitable for welfare and healthcare facilities with the following challenging characteristics: 1) users’ continuous movements, 2) various lighting conditions, and 3) a limited amount of available data. To address these issues, we introduce a multi-view multi-modal head-gaze estimation system that translates the user’s head orientation into the gaze direction. The proposed system captures the user using multiple cameras with… More >

  • Open Access


    User Interaction Based Recommender System Using Machine Learning

    R. Sabitha1, S. Vaishnavi2,*, S. Karthik1, R. M. Bhavadharini3

    Intelligent Automation & Soft Computing, Vol.31, No.2, pp. 1037-1049, 2022, DOI:10.32604/iasc.2022.018985

    Abstract In the present scenario of electronic commerce (E-Commerce), the in-depth knowledge of user interaction with resources has become a significant research concern that impacts more on analytical evaluations of recommender systems. For staying in aggressive E-Commerce, various products and services regarding distinctive requirements must be provided on time. Moreover, because of the large amount of product information available online, Recommender Systems (RS) are required to analyze the availability of consumers, which improves the decision-making of customers with detailed product knowledge and reduces time consumption. With that note, this paper derives a new model called User Interaction based Recommender System (UI-RS)… More >

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