Search Results - (( learning programme e algorithm ) OR ( java application customization algorithm ))

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    Predicting usage for a marketable e-learning portal by Yaacob, Aizan, Yusof, Yuhanis, Sheik Osman, Wan Rozaini, Derashid, Chek, Omar Khan, Zainizad

    Published 2014
    “…To date, existing e-learning portals focuses on providing various learning materials via online.Such an approach may provide huge benefit to the learners; nevertheless, less value can be obtained by the developers or owners.The knowledge transfer programme provides an insight on how existing e-learning portal can be upgraded.The academia has introduced the industry to a computational modelling that is built upon the behaviour of nature community (i.e bees)The utilization of Artificial Bee Colony algorithm in predicting learners' usage of an e-learning portal provides an indicator to the developers on the portals effectiveness.Such information is then useful in producing a marketable and valuable e-learning portal…”
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    CT-eKit: computational thinking interactive learning / Ong Sing Ling, Jill Ling and Fetylyana Nor Pazilah by Ong, Sing Ling, Jill, Ling, Pazilah, Fetylyana Nor

    Published 2023
    “…A mixed method was employed to investigate the students' learning experience with CT-eKit. This study involved 120 students from the Foundation in Science programme of the University of Technology Sarawak (UTS). …”
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    Book Section
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    Enhancing obfuscation technique for protecting source code against software reverse engineering by Mahfoudh, Asma

    Published 2019
    “…The proposed technique can be enhanced in the future to protect games applications and mobile applications that are developed by java; it can improve the software development industry. …”
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    Thesis
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    A machine learning approach of predicting high potential archers by means of physical fitness indicators by Musa, Rabiu Muazu, Anwar, P. P. Abdul Majeed, Zahari, Taha

    Published 2019
    “…k-nearest neighbour (k-NN) has been shown to be an effective learning algorithm for classification and prediction. …”
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    Article
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    Using web questionnaire for web-based course evaluation: advantages and disadvantages by Fariborzi, Elham, Abu Bakar, Kamariah, Kasa, Zakaria, Abu Samah, Bahaman, Abdullah, Muhammad Taufik

    Published 2017
    “…Creating and conducting a Web survey is a task requiring familiarity with Web programmes and database. This article examines some advantages and disadvantages of conducting Web survey research especially for Web-based course evaluation at Iranian university E-learning centers. …”
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    Ensemble learning for multidimensional poverty classification by Azuraliza Abu Bakar, Rusnita Hamdan, Nor Samsiah Sani

    Published 2020
    “…The goal of this study was to determine whether ensemble learning method (random forest) can classify poverty and hence produce multidimensional poverty indicator compared to based learner method using eKasih dataset. …”
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    Article
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    Design & Development of a Robotic System Using LEGO Mindstorm by Abd Manap, Nurulfajar, Md Salim, Sani Irwan, Haron, Nor Zaidi

    Published 2006
    “…Since the model is built using LEGO bricks, the model is fully customized, in term of its applications, to perform any relevant tasks. …”
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    The identification of high potential archers based on relative psychological coping skills variables: a support vector machine approach by Taha, Z., Musa, R.M., Majeed, A.P.P.A, Abdullah, M.R., Zakaria, M.A., Alim, M.M., Jizat, J.A.M., Ibrahim, M.F.

    Published 2018
    “…Support Vector Machine (SVM) has been revealed to be a powerful learning algorithm for classification and prediction. …”
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    The employment of support vector machine to classify high and low performance archers based on bio-physiological variables by Taha, Z., Musa, R.M., Majeed, A.P.P.A, Abdullah, M.R., Abdullah, M.A., Hassan, M.H.A., Khalil, Z.

    Published 2018
    “…The present study employs a machine learning algorithm namely support vector machine (SVM) to classify high and low potential archers from a collection of biophysiological variables trained on different SVMs. 50 youth archers with the average age and standard deviation of (17.0 +/-.056) gathered from various archery programmes completed a one end shooting score test. …”
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    The employment of Support Vector Machine to classify high and low performance archers based on bio-physiological variables by Zahari, Taha, Musa, Rabiu Muazu, Anwar, P. P. Abdul Majeed, Mohamad Razali, Abdullah, Muhammad Amirul, Abdullah, M. H. A., Hassan, Zubair, Khalil

    Published 2018
    “…The present study employs a machine learning algorithm namely support vector machine (SVM) to classify high and low potential archers from a collection of bio-physiological variables trained on different SVMs. 50 youth archers with the average age and standard deviation of (17.0 ±.056) gathered from various archery programmes completed a one end shooting score test. …”
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    Conference or Workshop Item