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  1. 1
    An ensemble learning method for spam email detection system based on metaheuristic algorithms

    An ensemble learning method for spam email detection system based on metaheuristic algorithms by Behjat, Amir Rajabi

    Published 2015
    “…A well known task is classification that predicts the class of new instances using known features or attributes automatically. …”
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    Thesis
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  2. 2
    A multi-filter feature selection in detecting distributed denial-of-service attack

    A multi-filter feature selection in detecting distributed denial-of-service attack by Yon, Yi Jun, Leau, Yu-Beng, Suraya Alias, Park, Yong Jin

    Published 2019
    “…It consists of 3-stage procedures: feature ranking, feature selection and classification. Subsequently, an experimental evaluation of the proposed Multi-Filter Feature Selection (M2FS) method is performed by using the benchmark dataset, NSL-KDD and employed the J48 classification algorithm. …”
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  3. 3
    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 b, Dwika Ananda, Subhan, Subhan, Jumanto, Jumanto, Yosza Dasril, Yosza Dasril, Iswanto, Iswanto

    Published 2023
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  4. 4
    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, Dwika Ananda Agustina Pertiwi, Dwika Ananda Agustina Pertiwi, Subhan, Subhan, Jumanto, Jumanto, Yosza Dasril, Yosza Dasril, Iswanto, Iswanto

    Published 2023
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  5. 5
    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 Much Aziz Muslim, Much Aziz Muslim, Tiara Lailatul Nikmah, Tiara Lailatul Nikmah, Dwika Ananda Agustina Pertiwi, Dwika Ananda Agustina Pertiwi, Subhan, Subhan, Jumanto, Jumanto, Yosza Dasril, Yosza Dasril, Iswanto, Iswanto

    Published 2023
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  6. 6
    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, Tiara Lailatul Nikmah, Tiara Lailatul Nikmah, Dwika Ananda Agustina Pertiwi b, Dwika Ananda Agustina Pertiwi b, Subhan, Subhan, Jumanto, Jumanto, Yosza Dasril, Yosza Dasril, Iswanto, Iswanto

    Published 2023
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  7. 7
    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, Dwika Ananda Agustina Pertiwi b, Dwika Ananda Agustina Pertiwi b, Subhan b, Subhan b, Jumanto, Jumanto, Nikmah, Tiara Lailatul, Agustina Pertiwi, Dwika Ananda, Iswanto, Iswanto

    Published 2023
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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, Tiara Lailatul Nikmah, Tiara Lailatul Nikmah, Dwika Ananda Agustina Pertiwi, Dwika Ananda Agustina Pertiwi, Subhan, Subhan, Jumanto, Jumanto, Yosza Dasril, Yosza Dasril, Iswanto, Iswanto

    Published 2023
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  9. 9
    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, Tiara Lailatul Nikmah, Tiara Lailatul Nikmah, Dwika Ananda Agustina Pertiwi, Dwika Ananda Agustina Pertiwi, Subhan, Subhan, Jumanto, Jumanto, Yosza Dasril, Yosza Dasril, Iswanto, Iswanto

    Published 2023
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  10. 10
    My little learner: E-learning wonderland

    My little learner: E-learning wonderland by Teoh, Wei En

    Published 2025
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    Final Year Project / Dissertation / Thesis
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  11. 11
    Developing an intelligent system to acquire meeting knowledge in problem-based learning environments

    Developing an intelligent system to acquire meeting knowledge in problem-based learning environments by Chiang, A., Baba, M.S.

    Published 2006
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  12. 12
    Predicting usage for a marketable e-learning portal

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

    Published 2014
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  13. 13
    My little learner : E-learning wonderland

    My little learner : E-learning wonderland by Teoh, Wei En

    Published 2025
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  14. 14
    An automated learner for extracting new ontology relations

    An automated learner for extracting new ontology relations by Amaal Saleh Hassan, Al Hashimy, Narayanan, Kulathuramaiyer

    Published 2013
    “…In this research we developed a buffered system that handle the whole process of extracting causation relations in general domain ontologies. …”
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  15. 15
    Art-Integration in Computational Thinking as an Unplugged Pedagogical Approach at A Rural Sarawak Primary School

    Art-Integration in Computational Thinking as an Unplugged Pedagogical Approach at A Rural Sarawak Primary School by Nur Hasheena, Anuar, Fitri Suraya, Mohamad, Jacey Lynn, Minoi

    Published 2020
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  16. 16
    Ethical Considerations in the Use of AI in Learning and Teaching for Special Education.

    Ethical Considerations in the Use of AI in Learning and Teaching for Special Education. by Noor Aida, Md Noor, Siti Noor Aneeis, Hashim, Juereanor, Mat Jusoh, Leha, Saliman, Shazali, Johari, NorHamidah, Ibrahim, Zaim Azizi, Abu Bakar, Mohd Norazmi, Nordin

    Published 2026
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  17. 17
    Mapping Computational Thinking Skills Through Digital Games Co-Creation Activity Amongst Malaysian Sub-urban Children Sub-urban Children

    Mapping Computational Thinking Skills Through Digital Games Co-Creation Activity Amongst Malaysian Sub-urban Children Sub-urban Children by Mohd Kamal, Othman, Syazni, Jazlan, Fatin Afiqah, Yamin, Shaziti, Aman, Fitri Suraya, Mohamad, Nurfarahani, Norman Anuar, Abdulrazak Yahya, Saleh, Ahmad Azaini, Abdul Manaf

    Published 2022
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  18. 18
    Ensemble learning for multidimensional poverty classification

    Ensemble learning for multidimensional poverty classification by Azuraliza Abu Bakar, Rusnita Hamdan, Nor Samsiah Sani

    Published 2020
    “…Beside Random Forest, we also examined decision tree and general linear methods to benchmark their performance and determine the method with the highest accuracy. …”
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  19. 19
    Applying (ACTFL) Standards to Arabic Language Education and the Challenges of Artificial Intelligence An Analytical Study

    Applying (ACTFL) Standards to Arabic Language Education and the Challenges of Artificial Intelligence An Analytical Study by Abdallah, Abdallah Saleh, Al-Haddad, Abdulwahab Abdulaziz Kassem, Talib, Noor Husna, Ibrahim Youssef, Mohamed Abdelrahman, Zolmaily, Muhammad Akmal

    Published 2025
    “…The study concludes that for AI tools to be effectively deployed in Arabic language education, they must be tailored to the linguistic and cultural realities of Arabic-speaking learners. Furthermore, alignment with ACTFL standards should not only guide instructional content but also inform the development of AI algorithms and feedback systems. …”
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    Proceeding Paper
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  20. 20
    Developing students' mathematical thinking: how far have we came?

    Developing students' mathematical thinking: how far have we came? by Md. Yunus, Aida Suraya

    Published 2015
    “…Several findings from studies that focused on students' ability to provide reasoning and give meanings to concepts and algorithms are highlighted. Students' development in geometric thinking based van Hiele's levels of geometric thinking in learning, shapes and spaces is also discussed. van Hiele's levels of geometric thinking includes higher order thinking and decision making skills and acquisition of mathematical concepts to enable learners to operate at higher levels in van Hiele's theory. …”
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