Home / Advanced Search

  • Title/Keywords

  • Author/Affliations

  • Journal

  • Article Type

  • Start Year

  • End Year

Update SearchingClear
  • Articles
  • Online
Search Results (6)
  • Open Access

    ARTICLE

    Learning Dual-Layer User Representation for Enhanced Item Recommendation

    Fuxi Zhu1, Jin Xie2,*, Mohammed Alshahrani3

    CMC-Computers, Materials & Continua, Vol.80, No.1, pp. 949-971, 2024, DOI:10.32604/cmc.2024.051046 - 18 July 2024

    Abstract User representation learning is crucial for capturing different user preferences, but it is also critical challenging because user intentions are latent and dispersed in complex and different patterns of user-generated data, and thus cannot be measured directly. Text-based data models can learn user representations by mining latent semantics, which is beneficial to enhancing the semantic function of user representations. However, these technologies only extract common features in historical records and cannot represent changes in user intentions. However, sequential feature can express the user’s interests and intentions that change time by time. But the sequential recommendation… More >

  • Open Access

    ARTICLE

    Latent Semantic Based Fuzzy Kernel Support Vector Machine for Automatic Content Summarization

    T. Vetriselvi1,*, J. Albert Mayan2, K. V. Priyadharshini3, K. Sathyamoorthy4, S. Venkata Lakshmi5, P. Vishnu Raja6

    Intelligent Automation & Soft Computing, Vol.34, No.3, pp. 1537-1551, 2022, DOI:10.32604/iasc.2022.025235 - 25 May 2022

    Abstract Recently, the bounteous amount of data/information has been available on the Internet which makes it very complicated to the customers to calculate the preferred data. Because the huge amount of data in a system is mandated to discover the most proper data from the corpus. Content summarization selects and extracts the related sentence depends upon the calculation of the score and rank of the corpus. Automatic content summarization technique translates from the higher corpus into smaller concise description. This chooses the very important level of the texts and implements the complete statistics summary. This paper… More >

  • Open Access

    ARTICLE

    Regulation Relatedness Map Creation Method with Latent Semantic Analysis

    Mehmet Murat Huyut1,*, Batuhan Kocaoğlu2, Ünzile Meram3

    CMC-Computers, Materials & Continua, Vol.72, No.1, pp. 2093-2107, 2022, DOI:10.32604/cmc.2022.024190 - 24 February 2022

    Abstract Regulatory authorities create a lot of legislation that must be followed. These create complex compliance requirements and time-consuming processes to find regulatory non-compliance. While the regulations establish rules in the relevant areas, recommendations and best practices for compliance are not generally mentioned. Best practices are often used to find a solution to this problem. There are numerous governance, management, and security frameworks in Information Technology (IT) area to guide businesses to run their processes at a much more mature level. Best practice maps can used to map another best practice, and users can adapt themselves… More >

  • Open Access

    ARTICLE

    Semantic Based Greedy Levy Gradient Boosting Algorithm for Phishing Detection

    R. Sakunthala Jenni*, S. Shankar

    Computer Systems Science and Engineering, Vol.41, No.2, pp. 525-538, 2022, DOI:10.32604/csse.2022.019300 - 25 October 2021

    Abstract The detection of phishing and legitimate websites is considered a great challenge for web service providers because the users of such websites are indistinguishable. Phishing websites also create traffic in the entire network. Another phishing issue is the broadening malware of the entire network, thus highlighting the demand for their detection while massive datasets (i.e., big data) are processed. Despite the application of boosting mechanisms in phishing detection, these methods are prone to significant errors in their output, specifically due to the combination of all website features in the training state. The upcoming big data… More >

  • Open Access

    ARTICLE

    Understanding Research Trends in Android Malware Research Using Information Modelling Techniques

    Jaiteg Singh1, Tanya Gera1, Farman Ali2, Deepak Thakur1, Karamjeet Singh3, Kyung-sup Kwak4,*

    CMC-Computers, Materials & Continua, Vol.66, No.3, pp. 2655-2670, 2021, DOI:10.32604/cmc.2021.014504 - 28 December 2020

    Abstract Android has been dominating the smartphone market for more than a decade and has managed to capture 87.8% of the market share. Such popularity of Android has drawn the attention of cybercriminals and malware developers. The malicious applications can steal sensitive information like contacts, read personal messages, record calls, send messages to premium-rate numbers, cause financial loss, gain access to the gallery and can access the user’s geographic location. Numerous surveys on Android security have primarily focused on types of malware attack, their propagation, and techniques to mitigate them. To the best of our knowledge,… More >

  • Open Access

    ARTICLE

    Attention-Aware Network with Latent Semantic Analysis for Clothing Invariant Gait Recognition

    Hefei Ling1, Jia Wu1, Ping Li1,*, Jialie Shen2

    CMC-Computers, Materials & Continua, Vol.60, No.3, pp. 1041-1054, 2019, DOI:10.32604/cmc.2019.05605

    Abstract Gait recognition is a complicated task due to the existence of co-factors like carrying conditions, clothing, viewpoints, and surfaces which change the appearance of gait more or less. Among those co-factors, clothing analysis is the most challenging one in the area. Conventional methods which are proposed for clothing invariant gait recognition show the body parts and the underlying relationships from them are important for gait recognition. Fortunately, attention mechanism shows dramatic performance for highlighting discriminative regions. Meanwhile, latent semantic analysis is known for the ability of capturing latent semantic variables to represent the underlying attributes More >

Displaying 1-10 on page 1 of 6. Per Page