Search Results - (( risk quantification based algorithm ) OR ( java application optimisation algorithm ))

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  1. 1

    Study and Implementation of Data Mining in Urban Gardening by Mohana, Muniandy, Lee, Eu Vern

    Published 2019
    “…The system is essentially a three-part development, utilising Android, Java Servlets, and Arduino platforms to create an optimised and automated urban-gardening system. …”
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  2. 2

    Access control model based on trust, purpose, and role in materialized view for privacy protection by Salji, Mohd Rafiz

    Published 2019
    “…This may lead to the risk of privacy disclosure to unauthorized and untrusted users. …”
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  3. 3

    Web-based expert system for material selection of natural fiber- reinforced polymer composites by Ahmed Ali, Basheer Ahmed

    Published 2015
    “…Finally, the developed expert system was deployed over the internet with central interactive interface from the server as a web-based application. As Java is platform independent and easy to be deployed in web based application and accessible through the World Wide Web (www), this expert system can be one stop application for materials selection.…”
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  4. 4

    Structured multi-head attention stock index prediction method based adaptive public opinion sentiment vector by Zhao, Cheng, Peng, Zhe, Lan, Xuefeng, Cen, Yuefeng, Wang, Zuxin

    Published 2024
    “…The present study examines the impact of short-term public opinion sentiment on the secondary market, with a focus on the potential for such sentiment to cause dramatic stock price fluctuations and increase investment risk. The quantification of investment sentiment indicators and the persistent analysis of their impact has been a complex and significant area of research. …”
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  5. 5

    Evaluating Power Reliability Dedicated for Sudden Disruptions: Its Application to Determine Capacity on the Basis of Energy Security by Kosai, Shoki, Tan, Chia Kwang, Yamasue, Eiji

    Published 2018
    “…In addition, existing reliability indexes are developed based on past experience, hardly covering the prediction of disruption risks. …”
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  6. 6

    Non-invasive diagnosis of risk in dengue patients using bioelectrical impedance analysis and artificial neural network by Ibrahim, F., Faisal, T., Mohamad Salim, M.I., Taib, M.N.

    Published 2010
    “…Eliminating a weight of 0.05 enhances the dengue's prediction risk classification accuracy of 95.88 for high risk and 96.83 for low risk groups. …”
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