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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
    Inversion Of Surface Wave Phase Velocity Using New Genetic Algorithm Technique For Geotechnical Site Investigation

    Inversion Of Surface Wave Phase Velocity Using New Genetic Algorithm Technique For Geotechnical Site Investigation by Hamimu, La

    Published 2011
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  3. 3
    Design and implementation of cordic algorithm with sinusoidal pulse width modulation switching strategy

    Design and implementation of cordic algorithm with sinusoidal pulse width modulation switching strategy by Madzzaini, Nur Sofea Eleena

    Published 2017
    “…Usually, one sinusoidal wave is used for one inverter. In this design, SPWM is used for multilevel inverter application in photovoltaic (PV) system. …”
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  4. 4
    A WAVELET BASED SIGNAL DENOISING AND HYDROCARBON PREDICTION ALGORITHM FOR A NEW MARINE CSEM ANTENNA DESIGN

    A WAVELET BASED SIGNAL DENOISING AND HYDROCARBON PREDICTION ALGORITHM FOR A NEW MARINE CSEM ANTENNA DESIGN by NYAMASVISVA, TADIWA ELISHA

    Published 2017
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  5. 5
    Ant colony optimization based subset feature selection in speech processing: Constructing graphs with degree sequences

    Ant colony optimization based subset feature selection in speech processing: Constructing graphs with degree sequences by Rajesvary Rajoo R., Salam R.A.

    Published 2024
    journal::journal article
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  6. 6
    Improved Salp Swarm Algorithm based on opposition based learning and novel local search algorithm for feature selection

    Improved Salp Swarm Algorithm based on opposition based learning and novel local search algorithm for feature selection by Tubishat, Mohammad, Idris, Norisma, Shuib, Liyana, Abushariah, Mohammad A.M., Mirjalili, Seyedali

    Published 2020
    “…Two main improvements were included into the original SSA algorithm to alleviate its drawbacks and adapt it for feature selection problems. …”
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  7. 7
    Dynamic Probability Selection for Flower Pollination Algorithm based on Metropolis-hastings Criteria

    Dynamic Probability Selection for Flower Pollination Algorithm based on Metropolis-hastings Criteria by Zamli, Kamal Zuhairi, Din, Fakhrud, Nasser, Abdullah, Ramli, Nazirah, Mohamed, Noraini

    Published 2021
    “…This paper proposed flower pollination algorithm metropolis-hastings (FPA-MH) based on the adoption of Metropolis-Hastings criteria adopted from the Simulated Annealing (SA) algorithm to enable dynamic selection of the pa probability. …”
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  8. 8
    Modelling of tsunami wave spectra propagation run-up in Sri-Lanka and Indonesia

    Modelling of tsunami wave spectra propagation run-up in Sri-Lanka and Indonesia by Marghany, Maged, Thamayendhi, Thamayendhi, Mazlan, Hashim

    Published 2006
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    Conference or Workshop Item
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  9. 9
    Simulation of Tsunami Wave Spectra on Sri Lanka and Indonesia

    Simulation of Tsunami Wave Spectra on Sri Lanka and Indonesia by Marghany , Maged, Hashim, Mazlan

    Published 2006
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  10. 10
    Simulation of Tsunami Wave Spectra on Sri-Lanka and Indonesia

    Simulation of Tsunami Wave Spectra on Sri-Lanka and Indonesia by Marghany , Maged, Hashim, Mazlan

    Published 2006
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  11. 11
    Dynamic probability selection for flower pollination algorithm based on metropolis-hastings criteria

    Dynamic probability selection for flower pollination algorithm based on metropolis-hastings criteria by Zamli, Kamal Zuhairi, Din, Fakhrud, Nasser, Abdullah, Ramli, Nazirah, Mohamed, Noraini

    Published 2021
    “…This paper proposed flower pollination algorithm metropolis-hastings (FPA-MH) based on the adoption of Metropolis-Hastings criteria adopted from the Simulated Annealing (SA) algorithm to enable dynamic selection of the pa probability. …”
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  12. 12
    On the spectra efficiency of low-complexity and resolution hybrid precoding and combining transceivers for mmWave MIMO systems

    On the spectra efficiency of low-complexity and resolution hybrid precoding and combining transceivers for mmWave MIMO systems by Uwaechia, Anthony Ngozichukwuka, Mahyuddin, Nor Muzlifah, Mohd Fadzil, Ain, Abdul) Latiff, Nurul Muazzah, Za'bah, Nor Farahidah

    Published 2019
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  13. 13
    Selective chaotic maps Tiki-Taka algorithm for the S-box generation and optimization

    Selective chaotic maps Tiki-Taka algorithm for the S-box generation and optimization by Kamal Z., Zamli, Abdul Kader, ., Fakhrud Din, ., Alhadawi, Hussam S.

    Published 2021
    “…This paper introduces a new variant of a metaheuristic algorithm based on Tiki-Taka algorithm, called selective chaotic maps Tiki-Taka algorithm (SCMTTA). …”
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  14. 14
    Quasi linear algorithm for modelling shoreline change from AIRSAR/POLSAR polarized data

    Quasi linear algorithm for modelling shoreline change from AIRSAR/POLSAR polarized data by Marghany, Maged, Hashim, Mazlan, Cracknell, Arthur P.

    Published 2007
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  15. 15
    Adaptive differential evolution algorithm with fitness based selection of parameters and mutation strategies / Rawaa Dawoud Hassan Al-Dabbagh

    Adaptive differential evolution algorithm with fitness based selection of parameters and mutation strategies / Rawaa Dawoud Hassan Al-Dabbagh by Rawaa Dawoud Hassan, Al-Dabbagh

    Published 2015
    “…The performance of DE algorithm depends heavily on the selected mutation strategy and its associated control parameters. …”
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  16. 16
    The Bacterial Foraging Optimisation Algorithm using Prototype Selection and Prototype Generation for Data Classification

    The Bacterial Foraging Optimisation Algorithm using Prototype Selection and Prototype Generation for Data Classification by Faizol, Bin Mohd Suria

    Published 2020
    “…However, none of the available works had proposed BFOA as a classification algorithm despite of its good performance. Thus, this study aims to adopt and modify the BFOA into Instance Selection (IS) classifier by manipulating its global search capability and high convergence rate for data classification problem. …”
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  17. 17
    Random traveling wave pulse coupled oscillator (RTWPCO) algorithm of energy-efficient wireless sensor networks

    Random traveling wave pulse coupled oscillator (RTWPCO) algorithm of energy-efficient wireless sensor networks by Al-Mekhlafi, Zeyad Ghaleb Aqlan, Mohd Hanapi, Zurina, Othman, Mohamed, Ahmad Zukarnain, Zuriati, Shamsan Saleh, Ahmed M.

    Published 2018
    “…To avert this problem, this study proposes a new mechanism called random traveling wave pulse-coupled oscillator algorithm, which is a self-organizing technique for energy-efficient wireless sensor networks using the phase-locking traveling wave pulse-coupled oscillator and random method on anti-phase of the pulse-coupled oscillator model. …”
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  18. 18
    An adaptive opposition-based learning selection: The case for jaya algorithm

    An adaptive opposition-based learning selection: The case for jaya algorithm by Nasser, Abdullah B., Kamal Z., Zamli, Hujainah, Fadhl, Ghanem, Waheed Ali H. M., Saad, Abdul-Malik H. Y., Mohammed Alduais, Nayef Abdulwahab

    Published 2021
    “…The results also show that OBL-JA performs better than standard Jaya Algorithm in most of the tested cases due to its ability to adapt its behaviour based on the current performance feedback of the search process.…”
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  19. 19
    Aco-based feature selection algorithm for classification

    Aco-based feature selection algorithm for classification by Al-mazini, Hassan Fouad Abbas

    Published 2022
    “…The adaptive technique for ant selection enables the parameter to adaptively change based on the feedback of the search space. …”
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  20. 20
    Hybrid indoor positioning utilizing multipath- assisted fingerprint and geometric estimation for single base station systems

    Hybrid indoor positioning utilizing multipath- assisted fingerprint and geometric estimation for single base station systems by Manap, Zahariah

    Published 2025
    “…Despite the reduced robustness of the Geometric-RANSAC algorithm when applied to scenarios with a limited number of VBSs, its exceptional accuracy compensates for this limitation. …”
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