SCOPUS 2024

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Browsing SCOPUS 2024 by Author "Aunkrisa Sangchumnong"

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    Framework for evaluation and providing Security in the tourism industry for a Trustworthy Rating System
    (Institute of Electrical and Electronics Engineers Inc., 2024) Girish B.C. Kumar; Gyanendra Kumar; Aunkrisa Sangchumnong; Parma Nand; Manoj Gupta; Chanyanan Somthawinpongsai; Vikram Bali; Aziz Nanthaamornphong; G.B.C. Kumar; Department of CSE, Sharda University, Greater Noida, UP, India; email: girishshekar.89@gmail.com
    All the customers are well enabled for online transactions including the purchase of cosmetics, hospitalization, booking of airways, and the like, here the service in form can be centrally done, and the means of display of satisfaction by the customer in as much as feedback for the same is concerned is provided. By using this rating system, other customers can be attracted to seek the services of the firm in the future. They can also convey interest in the same transactions. Nevertheless, the data statistics of the customers collected can be modified, or removed by the management people or some authorized individual if they tend to provoke unfavorable feedback on their service, therefore to obtain reliable amicable in the context of rating using, which is based on Blockchain technology, here nodes are decentralized and are largely disseminated over the network, In the proposed system the data collected cannot be manipulated by the management or some other unauthorized individual, The Here, the participants traded directly with each other with no middlemen or a central authority to regulate the process. The Test Net is another technology that has been employed to construct rating systems. This is a blockchain instance that is employed alongside the same or maybe the most current version of the software as can be utilized without posing a risk to the primary chain or actual money. © 2024 IEEE.
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    Virtual Learning Environment - Evaluation of LearnerÕs Behavior Using Topic Models
    (Institute of Electrical and Electronics Engineers Inc., 2024) N.A. Deepak; Gyanendra Kumar; Aunkrisa Sangchumnong; R.S. Chaithra; Sur Singh Rawat; Aziz Nanthaamornphong; Girish B.C. Kumar; Manoj Gupta; N.A. Deepak; RV Institute of Technology and Management, Bengaluru, India; email: deepakna.rvitm@rvei.edu.in
    Online learning platforms come with a number of difficulties. To identify the student who does not do the given assignment within the allotted time. Researchers have been attempting to solve this issue in the literature of late, however most algorithms are unable to produce linearly separable learner clusters and correctly classify the input documents. In an attempt to overcome these problems, the suggested methodology builds clusters of linearly separable learners by applying topic models such as Latent Dirichlet Allocation (LDA). First, the necessary features are retrieved and converted into an appropriate LDA of words and phrases. The topic-modeling algorithm (LDA) is then fed the words to create clusters of related content or learners. A number of experiments were carried out to assess how well various predictive models performed. The results show the topic-modeling algorithm LDA attains significant clustering of documents over the other state-of-art. © 2024 IEEE.