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  1. 1
    Deep Learning-Driven Mobility And Utility-Based Resource Management In Mm-Wave Enable Ultradense Heterogeneous Networks

    Deep Learning-Driven Mobility And Utility-Based Resource Management In Mm-Wave Enable Ultradense Heterogeneous Networks by Ukasyah bin Mahamod

    Published 2025
    thesis::doctoral thesis
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  2. 2
    State of charge estimation for lithium-ion battery based on random forests technique with gravitational search algorithm

    State of charge estimation for lithium-ion battery based on random forests technique with gravitational search algorithm by Lipu M.S.H., Ayob A., Saad M.H.M., Hussain A., Hannan M.A., Faisal M.

    Published 2023
    Conference Paper
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  3. 3
    Greedy-assisted teaching-learning-based optimization algorithm for cost-based hybrid flow shop scheduling

    Greedy-assisted teaching-learning-based optimization algorithm for cost-based hybrid flow shop scheduling by Ullah, Wasif, Mohd Fadzil Faisae, Ab Rashid, Muhammad Ammar, Nik Mu’tasim

    Published 2025
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  4. 4
    Comparative study on job scheduling using priority rule and machine learning

    Comparative study on job scheduling using priority rule and machine learning by Murad, Saydul Akbar, Zafril Rizal, M Azmi, Abu Jafar, Md Muzahid, Al-Imran, Md.

    Published 2021
    “…Cloud computing is a potential technique for running resource-intensive applications on a wide scale. Implementation of a suitable scheduling algorithm is critical in order to properly use cloud resources. …”
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  5. 5
    Enhancing project completion date prediction using a hybrid model: rule-based algorithm and machine learning algorithm

    Enhancing project completion date prediction using a hybrid model: rule-based algorithm and machine learning algorithm by Abd Rahman, Mohd Shahrizan, Jamaludin, Nor Azliana Akmal, Zainol, Zuraini, Tengku Sembok, Tengku Mohd

    Published 2025
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  6. 6
    Enhancing the QoS performance for mobile station over LTE and WiMAX networks / Mhd Nour Hindia

    Enhancing the QoS performance for mobile station over LTE and WiMAX networks / Mhd Nour Hindia by Hindia, Mhd Nour

    Published 2015
    “…The proposed algorithms provide an effective scheduling technique for smart grid applications. …”
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    Thesis
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  7. 7
    Fog-cloud scheduling simulator for reinforcement learning algorithms

    Fog-cloud scheduling simulator for reinforcement learning algorithms by Al-Hashimi, Mustafa Ahmed Adnan, Rahiman, Amir Rizaan, Muhammed, Abdullah, Hamid, Nor Asilah Wati

    Published 2023
    “…Therefore, supplying optimized scheduling algorithms to provide satisfactory quality service for the node’s task execution and processing becomes demanding. …”
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  8. 8
    Modeling and optimization of cost-based hybrid flow shop scheduling problem using metaheuristics

    Modeling and optimization of cost-based hybrid flow shop scheduling problem using metaheuristics by Ullah, Wasif, Mohd Fadzil Faisae, Ab Rashid, Muhammad Ammar, Nik Mu’tasim

    Published 2023
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  9. 9
    Fuzzy Mixed Assembly Line Sequencing and Scheduling Optimization Model Using Multiobjective Dynamic Fuzzy GA

    Fuzzy Mixed Assembly Line Sequencing and Scheduling Optimization Model Using Multiobjective Dynamic Fuzzy GA by Zahari, Taha, Farzad, Tahriri, Siti Zawiah, Md Dawal

    Published 2014
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  10. 10
    Reinforcement Learning Algorithm for Optimising Durian Irrigation Systems: Maximising Growth and Water Efficiency

    Reinforcement Learning Algorithm for Optimising Durian Irrigation Systems: Maximising Growth and Water Efficiency by Ramli, Muhammad Shahrul Azwan, Zainal Abidin, Mohamad Shukri, Hasan, Nor Shahida, Md Reba, Mohd Nadzri, Kolawole, Keshinro Kazeem, Ardiansyah, Rizqi Andry, Mpuhus, Sikudhan Lucas

    Published 2024
    “…This study presents a Reinforcement Learning-based algorithm designed to optimise irrigation for Durio Zibethinus (i.e., durian) trees, aiming to maximise tree growth and reduce water usage. …”
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  11. 11
    A predictive approach to improve a fault tolerance confidence level on grid resources scheduling

    A predictive approach to improve a fault tolerance confidence level on grid resources scheduling by Bouyer, Asgarali, Md. Sap, Mohd. Noor

    Published 2008
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  12. 12
    A block cipher based on genetic algorithm

    A block cipher based on genetic algorithm by Zakaria, Nur Hafiza

    Published 2016
    “…In many algorithms which are based on the genetic algorithm approach, diffusion properties using crossover and mutation function are being generated to produce a secure data transmission. …”
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  13. 13
    Efficient multi-UAV coordination via rl: joint path planning and task scheduling for post-disaster UAV-assisted MEC systems

    Efficient multi-UAV coordination via rl: joint path planning and task scheduling for post-disaster UAV-assisted MEC systems by Adnan, Mohd Hirzi, Ahmad Zukarnain, Zuriati, Subramaniam, Shamala K.

    Published 2025
    “…In this paper, we propose the Geometric Reinforcement Learning Algorithm (GRLA), a unified framework for joint path planning and task scheduling in multi-UAV MEC systems. …”
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  14. 14
    Semiparametric inference procedure for the accelarated failure time model with interval-censored data

    Semiparametric inference procedure for the accelarated failure time model with interval-censored data by Karimi, Mostafa

    Published 2019
    “…The rank-based methods, estimating algorithms, and resampling techniques that are developed do not involve the difficulties of the existing estimating procedures. …”
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  15. 15
    A genetic algorithm based approach for economic dispatch in power system / Mohd Rozely Kalil

    A genetic algorithm based approach for economic dispatch in power system / Mohd Rozely Kalil by Kalil, Mohd Rozely

    Published 1998
    “…The Genetic Algorithm developed in the project was tested on two objective functions for optimizing the economic dispatch problem, which are the total generation cost and the incremental cost function. …”
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  16. 16
    Computation of cryptosystem based on Lucas functions using addition chain

    Computation of cryptosystem based on Lucas functions using addition chain by Md Ali, Zulkarnain, Othman, Mohamed, Md. Said, Mohamad Rushdan, Sulaiman, Md. Nasir

    Published 2010
    “…In this paper, an efficient computation algorithm for LUC Cryptosystem is developed. It is based on Addition Chain. …”
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  17. 17
    A study on regional GDP forecasting analysis based on radial basis function neural network with genetic algorithm (RBFNN-GA) for Shandong economy

    A study on regional GDP forecasting analysis based on radial basis function neural network with genetic algorithm (RBFNN-GA) for Shandong economy by Qing, Zhang, Abdullah, Abdul Rashid, Choo, Wei Chong, Ali, Mass Hareeza

    Published 2022
    “…These basic indicators can comprehensively and effectively reflect a country’s or region’s future economic development. The center of radial basis function neural network and smoothing factor to take a uniform distribution of the random radial basis function artificial neural network will be the focus of this study. …”
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  18. 18
    Software project estimation with machine learning

    Software project estimation with machine learning by Zakaria, Noor Azura, Ismail, Amelia Ritahani, Yakath Ali, Afrujaan, Mohd Khalid, Nur Hidayah, Zainal Abidin, Nadzurah

    Published 2021
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  19. 19
    Prediction of Remaining Useful Life (RUL) in Refinery using Deep Learning

    Prediction of Remaining Useful Life (RUL) in Refinery using Deep Learning by Baharadin, Hazirah

    Published 2019
    “…The project is developed using Deep Learning algorithm which the functionality can be found in KNIME Analytic application. …”
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    Final Year Project
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  20. 20
    A Reference Based Surface Defect Segmentation Algorithm For Automatic Optical Inspection System

    A Reference Based Surface Defect Segmentation Algorithm For Automatic Optical Inspection System by Wong, Ze-Hao

    Published 2020
    “…Finally, surface defects are segmented from an anomaly heat map which is generated based on histogram distance functions. Results show that the proposed algorithm required a learning dataset size as small as 5 samples and was resistant to learning labelling error up to 50%.…”
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