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  1. 1
    Development Of Machine Learning User Interface For Pump Diagnostics

    Development Of Machine Learning User Interface For Pump Diagnostics by Lee, Zhao Yang

    Published 2022
    “…Build up a user interface by using Visual Studio Code (VSC) to run the coding of Cascading Style Sheet (CSS), Hyper Text Markup Language (HTML) and JavaScript (JS) as a webpage and connect to Azure Machine Learning Model and this will allow the user from using the model from a webpage when they have active internet with any devices.…”
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  2. 2
    Mathematical simulation for 3-dimensional temperature visualization on open source-based grid computing platform

    Mathematical simulation for 3-dimensional temperature visualization on open source-based grid computing platform by Alias, Norma, Satam, Noriza, Abd. Ghaffar, Zarith Safiza, Darwis, Roziha, Hamzah, Norhafiza, Islam, Md. Rajibul

    Published 2009
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  3. 3
    Development of a scheduling tool for constructing a Malaysian school timetable using genetic algorithms / Salihin Farag Shoeeb

    Development of a scheduling tool for constructing a Malaysian school timetable using genetic algorithms / Salihin Farag Shoeeb by Salihin Farag , Shoeeb

    Published 2003
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    Thesis
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  4. 4
    A conceptual multi-agent framework using ant colony optimization and fuzzy algorithms for learning style detection

    A conceptual multi-agent framework using ant colony optimization and fuzzy algorithms for learning style detection by Basheer G.S., Ahmad M.S., Tang A.Y.C.

    Published 2023
    “…The multi-agent system applies ant colony optimization and fuzzy logic search algorithms as tools to detecting learning styles. …”
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  5. 5
    Mobile application for real time baby sign language recognition using YOLOv8 / Siti Aishah Idris and Ahmad Firdaus Ahmad Fadzil

    Mobile application for real time baby sign language recognition using YOLOv8 / Siti Aishah Idris and Ahmad Firdaus Ahmad Fadzil by Idris, Siti Aishah, Ahmad Fadzil, Ahmad Firdaus

    Published 2024
    “…The model will be designed and developed using a deep learning algorithm, which is YOLOv8, the latest version of YOLO. …”
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  6. 6
    Form four chemistry teachers’ conceptualization of pedagogical content knowledge / Chien Lee Shing

    Form four chemistry teachers’ conceptualization of pedagogical content knowledge / Chien Lee Shing by Chien, Lee Shing

    Published 2016
    “…As a whole, the teaching of the four Form Four chemistry teachers was teacher-oriented, algorithmic, using chalk-and-talk approach which did not support thoughtful and meaningful and optimal learning among the students. …”
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  7. 7
    Improving Class Timetabling using Genetic Algorithm

    Improving Class Timetabling using Genetic Algorithm by Qutishat, Ahmed Mohammed Ali

    Published 2006
    “…We have targeted the research on class timetabling problem. Hence, Genetic Algorithm (GA) is used as one of the most popular optimization solutions. …”
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  8. 8
    New model combination meta-learner to improve accuracy prediction P2P lending with stacking ensemble learning

    New model combination meta-learner to improve accuracy prediction P2P lending with stacking ensemble learning by Muslim, Much Aziz, Nikmah, Tiara Lailatul, Agustina Pertiwi, Dwika Ananda, Subhan, Subhan, Jumanto, Jumanto, Dasril, Yosza, swanto, Iswanto

    Published 2023
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  9. 9
    Analyzing enrolment patterns: modified stacked ensemble statistical learning based approach to educational decision-making

    Analyzing enrolment patterns: modified stacked ensemble statistical learning based approach to educational decision-making by Zun, Liang Chuan, Nursultan Japashov, Soon, Kien Yuan, Tan, Wei Qing, Noriszura Ismail

    Published 2024
    “…Moreover, the introduction of the novel modified stacked ensemble statistical learning-based algorithm had improved predictive accuracy compared to traditional dichotomous logistic regression algorithms on average, particularly at optimal training-to-test ratios of 70:30, 80:20, and 90:10. …”
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  10. 10
    Computational intelligence approach for classification and risk quantification of metabolic syndrome / Habeebah Adamu Kakudi

    Computational intelligence approach for classification and risk quantification of metabolic syndrome / Habeebah Adamu Kakudi by Habeebah Adamu , Kakudi

    Published 2019
    “…Genetic Algorithm(GA) is used to optimize the order of sequence of the input sample and the parameters of the Bayesian ARTMAP (BAM). …”
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  11. 11
    Analyzing enrolment patterns: Modified stacked ensemble statistical learning-based approach to educational decision-making

    Analyzing enrolment patterns: Modified stacked ensemble statistical learning-based approach to educational decision-making by Chuan, Zun Liang, Japashov, Nursultan, Yuan, Soon Kien, Tan, Wei Qing, Noriszura, Ismail

    Published 2024
    “…Moreover, the introduction of the novel modified stacked ensemble statistical learning-based algorithm had improved predictive accuracy compared to traditional dichotomous logistic regression algorithms on average, particularly at optimal training-to-test ratios of 70:30, 80:20, and 90:10. …”
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  12. 12
    Analyzing enrolment patterns: Stacked ensemble statistical learning-based approach to educational decision making

    Analyzing enrolment patterns: Stacked ensemble statistical learning-based approach to educational decision making by Chuan, Zun Liang, Chong, Teak Wei, Japashov, Nursultan, Soon, Kien Yuan, Tan, Wei Qing, Noriszura, Ismail, Liong, Choong-Yeun, Tan, Ee Hiae

    Published 2023
    “…Moreover, the introduction of the novel stacked ensemble machine learning algorithm had improved predictive accuracy compared to traditional dichotomous logistic regression algorithms on average, particularly at optimal training-to-test ratios of 70:30, 80:20, and 90:10. …”
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  13. 13
    Personalized one-shot local adaptation federated learning for mortality prediction in multi-center Intensive Care Unit

    Personalized one-shot local adaptation federated learning for mortality prediction in multi-center Intensive Care Unit by Deng, Ting

    Published 2024
    “…Step 2 enables highly personalized models as students to rebalance global and local data knowledge through knowledge distillation optimizations. Step 3 automatically evolves the best-fitting parameters for the highly personalized model at each center using an adapted genetic algorithm. …”
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