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  1. 1
    Intelligent Examination Timetabling System Using Hybrid Intelligent Water Drops Algorithm

    Intelligent Examination Timetabling System Using Hybrid Intelligent Water Drops Algorithm by AlDeeb, BA, Norwawi, NM, Al-Betar, MA, Jali, MZ

    Published 2024
    “…Intelligent Water Drops algorithm (IWD) is a population-based algorithm where each drop represents a solution and the sharing between the drops during the search lead to better drops. …”
    Proceedings Paper
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  2. 2
    Intelligent examination timetabling system using hybrid intelligent water drops algorithm

    Intelligent examination timetabling system using hybrid intelligent water drops algorithm by AlDeeb, Bashar A., Md Norwawi, Norita, Al-Betar, Mohammed A., Jali, Mohd Z.

    Published 2015
    “…This paper proposes Hybrid Intelligent Water Drops (HIWD) algorithm to solve Tamhidi programs uncapacitated examination timetabling problem in Universiti Sains Islamic Malaysia (USIM).Intelligent Water Drops algorithm (IWD) is a population-based algorithm where each drop represents a solution and the sharing between the drops during the search lead to better drops.The results of this study prove that the proposed algorithm can produce a high quality examination timetable in shorter time in comparison with the manual timetable.…”
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  3. 3
    Solving university examination timetabling problem using intelligent water drops algorithm

    Solving university examination timetabling problem using intelligent water drops algorithm by Aldeeb B.A., Norwawi N.M., Al-Betar M.A., Jali M.Z.B.

    Published 2024
    Subjects: “…Intelligent water drops algorithm…”
    Conference Paper
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  4. 4
    Hybrid Intelligent Water Drops Algorithm For Examination Timetabling Problem

    Hybrid Intelligent Water Drops Algorithm For Examination Timetabling Problem by Bashar A.Aldeeb, Mohammed Azmi Al-Betar, Norita Md Norwawi, Khalid A.Alissa, Mutasem K.Alsmadi, Ayman A.Hazaymeh, Malek Alzaqebah

    Published 2024
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  5. 5
    Modified And Ensemble Intelligent Water Drop Algorithms And Their Applications

    Modified And Ensemble Intelligent Water Drop Algorithms And Their Applications by O. F. Alijla, Basem

    Published 2015
    “…Pertama, algoritma TAC yang diubahsuai, diperkenalkan. The Intelligent Water Drop (IWD) algorithm is a swarm-based model that is useful for undertaking optimization problems. …”
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  6. 6
    Solving University Examination Timetabling Problem Using Intelligent Water Drops Algorithm

    Solving University Examination Timetabling Problem Using Intelligent Water Drops Algorithm by Aldeeb, BA, Norwawi, NM, Al-Betar, MA, Bin Jali, MZ

    Published 2024
    “…IWD is a recent metaheuristic population-based algorithm belonging to swarm intelligent category which simulate river system. …”
    Proceedings Paper
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  7. 7
    Enhanced Intelligent Water Drops Algorithm for University Examination Timetabling Problems

    Enhanced Intelligent Water Drops Algorithm for University Examination Timetabling Problems by Bashar AbedAl Mohdi Talal AlDeeb

    Published 2024
    “…This thesis presents an investigation of using the Intelligent Water Drops (IWD) algorithm to construct and produce good quality solutions for the UETP. …”
    thesis::doctoral thesis
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  8. 8
    Hybrid model to improve the river streamflow forecasting utilizing multi-layer perceptron-based intelligent water drop optimization algorithm

    Hybrid model to improve the river streamflow forecasting utilizing multi-layer perceptron-based intelligent water drop optimization algorithm by Pham Q.B., Afan H.A., Mohammadi B., Ahmed A.N., Linh N.T.T., Vo N.D., Moazenzadeh R., Yu P.-S., El-Shafie A.

    Published 2023
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  9. 9
    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
    “…This project adapts the original MALESAbrain definitions and algorithm to create an intelligent learning tool; then, testing the tool in a students' meeting to discuss "To build up programming skills for computer science students, do you agree JAVA is a proper language in the first year foundation, course for computer science students"? …”
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  10. 10
    Evaluate the performance of university timetabling problem with various artificial intelligence techniques

    Evaluate the performance of university timetabling problem with various artificial intelligence techniques by Hooi, Charmaine Wai Yee

    Published 2025
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    Final Year Project / Dissertation / Thesis
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  11. 11
    Design and Implementation of Intelligent Interoperability Framework for Heterogeneous Subsystems in Smart Home Environment

    Design and Implementation of Intelligent Interoperability Framework for Heterogeneous Subsystems in Smart Home Environment by Perumal, Thinagaran

    Published 2011
    “…The third algorithm, named as Pro-Active Intelligence algorithm has inspired autonomous action triggering for each event interoperation, using a control action statement that is generated by SOAP packets required for joint execution of tasks among heterogeneous subsystems. …”
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  12. 12
    An Intelligent System Approach to the Dynamic Hybrid Robot Control

    An Intelligent System Approach to the Dynamic Hybrid Robot Control by Md. Yeasin, Md. Mahmud Hasan

    Published 1996
    “…A one-pass smoothing algorithm was used to generate a practical smooth trajectory path in near real time. …”
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  13. 13
    Design and Development of Artificial Intelligence (Al)-Based Desicion Support System For Manufacturing Applications

    Design and Development of Artificial Intelligence (Al)-Based Desicion Support System For Manufacturing Applications by Lim , Chee Peng

    Published 2016
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    Monograph
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  14. 14
    CSC728 - Machine Learning / College of Computing, Informatics and Media

    CSC728 - Machine Learning / College of Computing, Informatics and Media by UiTM, College of Computing, Informatics and Media

    Published 2022
    “…The ability to learn is a fundamental characteristic of intelligent behavior. …”
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    Teaching Resource
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  15. 15
    CSC728: Machine Learning / College of Computing, Informatics and Mathematics

    CSC728: Machine Learning / College of Computing, Informatics and Mathematics by UiTM, College of Computing, Informatics and Mathematics

    Published 2017
    “…The ability to learn is a fundamental characteristic of intelligent behavior. …”
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  16. 16
    CSC779: Special Topic (Intelligent Systems) / College of Computing, Informatics and Media

    CSC779: Special Topic (Intelligent Systems) / College of Computing, Informatics and Media by UiTM, College of Computing, Informatics and Media

    Published 2015
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  17. 17
    CSC779: Special Topic (Intelligent Systems) / College of Computing, Informatics and Mathematics

    CSC779: Special Topic (Intelligent Systems) / College of Computing, Informatics and Mathematics by UiTM, College of Computing, Informatics and Mathematics

    Published 2012
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  18. 18
    A PSO inspired asynchronous cooperative distributed hyper-heuristic for course timetabling problems

    A PSO inspired asynchronous cooperative distributed hyper-heuristic for course timetabling problems by Joe Henry Obit, Rayner Alfred, Mansour Hassani Abdalla

    Published 2017
    “…The proposed hyper-heuristic algorithm starts with a complete solution and tries to improve the soft constraints, whilst always remaining in the feasible region of the search space. …”
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  19. 19
    A comparative study and simulation of object tracking algorithms

    A comparative study and simulation of object tracking algorithms by Ji, Yuanfa, Yin, Pan, Sun, Xiyan, Kamarul Hawari, Ghazali, Guo, Ning

    Published 2020
    “…This article introduces the popular object tracking algorithms, from common problems in object tracking to the classification of algorithms: Early classic trackingalgorithms, tracking algorithms based on kernel correlation filtering, and tracking algorithms based on deep learning. …”
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  20. 20
    Fast sequential learning methods on RBF-network using decomposed training algorithms

    Fast sequential learning methods on RBF-network using decomposed training algorithms by Asirvadam , Vijanth Sagayan, McLoone, Sean, Irwin, George W

    Published 2004
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