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  1. 1
    Performance evaluation of real-time multiprocessor scheduling algorithms

    Performance evaluation of real-time multiprocessor scheduling algorithms by Alhussian, H., Zakaria, N., Abdulkadir, S.J., Fageeri, S.O.

    Published 2016
    “…The CPU profiler of JavaTM VisualVM measures the number of invocations of scheduling event handlers (procedures) in each algorithm as well as the total time spent in all invocations of this handler. …”
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  2. 2
    A Toolkit for Simulation of Desktop Grid Environment

    A Toolkit for Simulation of Desktop Grid Environment by FOROUSHAN, PAYAM CHINI

    Published 2014
    “…In this type of environment it is nearly impossible to prove the effectiveness of a scheduling algorithm. Hence the main objective of this study is to develop a desktop grid simulator toolkit for measuring and modeling scheduler algorithm performance. …”
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  3. 3
    Computer Lab Timetabling Using Genetic Algorithm Case Study - Unit ICT

    Computer Lab Timetabling Using Genetic Algorithm Case Study - Unit ICT by Abdullah, Amran

    Published 2006
    “…Genetic Algorithm is one of the most popular optimization solutions used in various applications such as scheduling. …”
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    Thesis
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  4. 4
    Improving Class Timetabling using Genetic Algorithm

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

    Published 2006
    “…This paper reports the power fill techniques using GA in scheduling. Class timetabling problem is one of the applications in scheduling. …”
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  5. 5
    Examination timetabling using genetic algorithm case study: KUiTTHO

    Examination timetabling using genetic algorithm case study: KUiTTHO by Mohd Salikon, Mohd Zaki

    Published 2005
    “…This paper reports the powerful techniques using GA in scheduling. Examination timetabling problem is one of the applications in scheduling. …”
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  6. 6
    Examination Timetabling Using Genetic Algorithm Case Study : KUiTTHO

    Examination Timetabling Using Genetic Algorithm Case Study : KUiTTHO by Mohd. Zaki, Mohd. Salikon

    Published 2005
    “…This paper reports the powerful techniques using GA in scheduling. Examination timetabling problem is one of the applications in scheduling. …”
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  7. 7
    Attribute reduction based scheduling algorithm with enhanced hybrid genetic algorithm and particle swarm optimization for optimal device selection

    Attribute reduction based scheduling algorithm with enhanced hybrid genetic algorithm and particle swarm optimization for optimal device selection by Nwogbaga, Nweso Emmanuel, Latip, Rohaya, Affendey, Lilly Suriani, Abdul Rahiman, Amir Rizaan

    Published 2022
    “…Therefore, in this paper, we proposed Dynamic tasks scheduling algorithm based on attribute reduction with an enhanced hybrid Genetic Algorithm and Particle Swarm Optimization for optimal device selection. …”
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  8. 8
    Enhancement of Ant Colony Optimization for Grid Job Scheduling and Load Balancing

    Enhancement of Ant Colony Optimization for Grid Job Scheduling and Load Balancing by Husna, Jamal Abdul Nasir

    Published 2011
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  9. 9
    Batch mode heuristic approaches for efficient task scheduling in grid computing system

    Batch mode heuristic approaches for efficient task scheduling in grid computing system by Maipan-Uku, Jamilu Yahaya

    Published 2016
    “…Many algorithms have been implemented to solve the grid scheduling problem. …”
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  10. 10
    Missing tags detection algorithm for radio frequency identification (RFID) data stream

    Missing tags detection algorithm for radio frequency identification (RFID) data stream by Zainudin, Nur 'Aifaa

    Published 2019
    “…Thus in this research, an AC complement algorithm with hashing algorithm and Detect False Negative Read algorithm (DFR) is used to developed the Missing Tags Detection Algorithm (MTDA). …”
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  11. 11
    An Evaluation of Machine Learning Algorithms for Missing Values Imputation

    An Evaluation of Machine Learning Algorithms for Missing Values Imputation by Kohbalan, Moorthy, Ali, Mohammed Hasan, Mohd Arfian, Ismail, Chan, Weng Howe, Mohd Saberi, Mohamad, Safaai, Deris

    Published 2019
    “…We considered the algorithms in a global, hybrid, local, and knowledge-based technique. …”
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  12. 12
    Detection Of Misplaced And Missing Regions In Image Using Neural Network

    Detection Of Misplaced And Missing Regions In Image Using Neural Network by Tan , Jin Siang

    Published 2017
    “…Therefore, it is necessary to develop an algorithm that is able to detect both misplaced and missing jigsaw puzzles. …”
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  13. 13
    Missing-values imputation algorithms for microarray gene expression data

    Missing-values imputation algorithms for microarray gene expression data by Moorthy, Kohbalan, Jaber, Aws Naser, Mohd Arfian, Ismail, Ernawan, Ferda, Mohd Saberi, Mohamad, Safaai, Deris

    Published 2019
    “…The purpose of this review is to focus on developments in the current techniques for scientists rather than applying different or newly developed algorithms with identical functional goals. …”
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  14. 14
    Auto-feed hyperparameter support vector regression prediction algorithm in handling missing values in oil and gas dataset

    Auto-feed hyperparameter support vector regression prediction algorithm in handling missing values in oil and gas dataset by Amirruddin, A., Aziz, I.A., Hasan, M.H.

    Published 2020
    “…This problem inspires the idea to develop a prediction algorithm to predict the missing values in the dataset, where Support vector regression (SVR) has been proposed as a prediction method to predict missing values in several academic types of researches. …”
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  15. 15
    Enhanced mechanism to handle missing data of Hadith classifier

    Enhanced mechanism to handle missing data of Hadith classifier by Aldhlan, Kawther A., Zeki, Ahmed M., Zeki, Akram M.

    Published 2011
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  16. 16
    Development of an imputation technique - INI for software metric database with incomplete data

    Development of an imputation technique - INI for software metric database with incomplete data by Wasito, Ito, Olanrewaju, Rashidah F.

    Published 2007
    “…Missing data causes significant problem. With inaccurate data or missing data, it is very difficult to know how much a project will cost or worth. …”
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  17. 17
    Classroom finder system with student availability, space and time constraint

    Classroom finder system with student availability, space and time constraint by Loh, Yong Bin

    Published 2024
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    Final Year Project / Dissertation / Thesis
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  18. 18
    MISSING DAILY RAINFALL PREDICTION USING GREY WOLF OPTIMIZER-BASED NEURAL NETWORK

    MISSING DAILY RAINFALL PREDICTION USING GREY WOLF OPTIMIZER-BASED NEURAL NETWORK by Lai, Wai Yan, Kuok, King Kuok, Chiu, Po Chan, Md. Rezaur, Rahman, Muhammad Khusairy, Bakri

    Published 2024
    “…This research chapter presents the integration of the Grey Wolf Optimizer (GWO) algorithm for training a Feedforward Neural Network (FNN) to address the issue of missing daily rainfall records. …”
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  19. 19
    Data-driven continuous-time Hammerstein modeling with missing data using improved Archimedes optimization algorithm

    Data-driven continuous-time Hammerstein modeling with missing data using improved Archimedes optimization algorithm by Islam, Muhammad Shafiqul, Mohd Ashraf, Ahmad

    Published 2024
    “…The primary focus is on developing a novel data-driven approach for modeling continuous-time Hammerstein models, particularly in the presence of missing output data. …”
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  20. 20
    Smart student timetable planner

    Smart student timetable planner by Wong, Xin Tong

    Published 2025
    “…Course data is managed in CSV format, parsed into JSON for fast processing, while sessionStorage and localStorage handle user data within active sessions. A Genetic Algorithm forms the core scheduling engine, generating optimized timetables that respect both hard constraints, such as avoiding clashes, and soft constraints, such as personal preferences.The final output of this project is a functional web-based timetable planner that successfully enhances scheduling efficiency, reduces the likelihood of errors, and improves the overall academic planning experience. …”
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