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  1. 1
    Improved chemotaxis differential evolution optimization algorithm

    Improved chemotaxis differential evolution optimization algorithm by Yıldız, Y. Emre, Altun, Oğuz, Topal, A. Osman

    Published 2015
    “…The social foraging behavior of Escherichia coli has recently received great attention and it has been employed to solve complex search optimization problems.This paper presents a modified bacterial foraging optimization BFO algorithm, ICDEOA (Improved Chemotaxis Differential Evolution Optimization Algorithm), to cope with premature convergence of reproduction operator.In ICDEOA, reproduction operator of BFOA is replaced with probabilistic reposition operator to enhance the intensification and the diversification of the search space.ICDEOA was compared with state-of-the-art DE and non-DE variants on 7 numerical functions of the 2014 Congress on Evolutionary Computation (CEC 2014). …”
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  2. 2
    An FPGA implementation of exp-bet scheduling algorithm in LTE networks / Yusmardiah Yusuf

    An FPGA implementation of exp-bet scheduling algorithm in LTE networks / Yusmardiah Yusuf by Yusuf, Yusmardiah

    Published 2017
    “…It is performed by the scheduling algorithm implemented in the Long Term Evolution (LTE) base station, Evolved Node B. …”
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  3. 3
    Enhanced Quality of Service of Cell-Edge User by Extending Modified Largest Weighted Delay First Algorithm in LTE Networks

    Enhanced Quality of Service of Cell-Edge User by Extending Modified Largest Weighted Delay First Algorithm in LTE Networks by Chayon, H.R., Dimyati, K., Ramiah, H., Reza, A.W.

    Published 2017
    “…It includes the distance of the user from attached eNB, received Signal to Interference plus Noise Ratio (SINR) and error probability into the original algorithm. The simulated results are compared with other well-known algorithms and the comparison shows that the proposed algorithm enhances overall 56.23% of cell-edge user throughput and significantly improves the average user throughput, fairness index, and spectral efficiency.…”
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  4. 4
    Autotuning PID Controllers for Quadplane Hybrid UAV using Differential Evolution Algorithm

    Autotuning PID Controllers for Quadplane Hybrid UAV using Differential Evolution Algorithm by Pairan, Mohammad Fahmi, Shamsudin, Syariful Syafiq

    Published 2024
    “…The paper presents a method for autotuning attitude PID for a quadplane UAV using differential evolution (DE), X-Plane simulation, and neural network (NN)-based system identification. …”
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  5. 5
    Autotuning PID Controllers for Quadplane Hybrid UAV using Differential Evolution Algorithm

    Autotuning PID Controllers for Quadplane Hybrid UAV using Differential Evolution Algorithm by Pairan, Mohammad Fahmi, Shamsudin, Syariful Syafiq, Yaakub, Mohd Fauzi

    Published 2024
    “…The paper presents a method for autotuning attitude PID for a quadplane UAV using differential evolution (DE), X-Plane simulation, and neural network (NN)-based system identification. …”
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  6. 6
    Fair scheduling algorithm in LTE-advanced networks

    Fair scheduling algorithm in LTE-advanced networks by Al-Shibly, Mohammed Abuljawad M, Habaebi, Mohamed Hadi, Islam, Md Rafiqul, Chebil, Jalel, Zyoud, Alhareth, Alfaqawi, Mohammed I M

    Published 2015
    “…The novel algorithm is implemented in a PS module for LTE-Advanced via system level simulations. …”
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  7. 7
    HARQ aware scheduling algorithm for the downlink LTE system

    HARQ aware scheduling algorithm for the downlink LTE system by Mohd. Ramli, Huda Adibah, Sandrasegaran, Kumbesan, Basukala, Riyaj, Afrin, Toyoba Sohana

    Published 2011
    “…Simulation results show that this algorithm outperforms modified-largest weighted delay first (M-LWDF) algorithm by minimizing the number of lost packets, maintaining a low queuing delay and maximizing effective user throughput.…”
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  8. 8
    Development of self-learning algorithm for autonomous system utilizing reinforcement learning and unsupervised weightless neural network / Yusman Yusof

    Development of self-learning algorithm for autonomous system utilizing reinforcement learning and unsupervised weightless neural network / Yusman Yusof by Yusof, Yusman

    Published 2019
    “…In the algorithm development a step-by-step example of the algorithm implementation is presented and then successfully implemented in Lego Mindstorm obstacle avoiding mobile robot as a proof of concept implementation of the hybrid AI algorithm. …”
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  9. 9
    Virtual reality in algorithm programming course: practicality and implications for college students

    Virtual reality in algorithm programming course: practicality and implications for college students by Dewi, Ika Parma, Ambiyar, Mursyida, Lativa, Effendi, Hansi, Giatman, Muhammad, Efrizon, Hanafi, Hafizul Fahri, Ali, Siti Khadijah

    Published 2024
    “…The analysis of learning problems shows the unavailability of interactive learning media that can support various learning styles of students in programming algorithm materials. …”
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  10. 10
    Self-configured link adaptation using channel quality indicator-modulation and coding scheme mapping with partial feedback for green long-term evolution cellular systems

    Self-configured link adaptation using channel quality indicator-modulation and coding scheme mapping with partial feedback for green long-term evolution cellular systems by Salman, Mustafa Ismael

    Published 2015
    “…Based on simulation results, it is concluded that the proposed energy-efficient scheduling has led the eNodeB to transmit with low power through modified MCS. …”
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  11. 11
    Using GA and KMP algorithm to implement an approach to learning through intelligent framework documentation

    Using GA and KMP algorithm to implement an approach to learning through intelligent framework documentation by Mat Jani H., Lee S.P.

    Published 2023
    Conference paper
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  12. 12
    Accurate numerical treatment on a stochastic SIR epidemic model with optimal control strategy

    Accurate numerical treatment on a stochastic SIR epidemic model with optimal control strategy by Ghosh, Indranil, Rashid, Muhammad Mahbubur, Ghosh, Pallabi, Mawa, Shukranul, Roy, Rupal, Ahsan, Md Manjurul, Gupta, Kishor Datta

    Published 2022
    “…We furnish numerical and graphical simulation combined with explicit series solutions of the proposed model using the New Iterative Method (NIM) and Modified New Iterative Method (MNIM). …”
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  13. 13
    Development of an islanding detection scheme based on combination of slantlet transform and ridgelet probabilistic neural network in distributed generation

    Development of an islanding detection scheme based on combination of slantlet transform and ridgelet probabilistic neural network in distributed generation by Ahmadipour, Masoud

    Published 2019
    “…Furthermore, to evaluate the efficiency of the proposed modified differential evolution for the training of ridgelet probabilistic neural network, four statistical search techniques, namely, particle swarm optimization, genetic algorithm, simulated angling, and classical differential evolution are used and their results are compared. …”
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  14. 14
    Design And Development Of Modified-Proportional Fair Scheduler For LTE/LTE-Advanced

    Design And Development Of Modified-Proportional Fair Scheduler For LTE/LTE-Advanced by Mohd Khairy, Ismail, Azmi, Awang Md Isa, Mohd Nor, Husain, Muhammad Syahrir, Johal, Mohd Riduan, Ahmad

    Published 2016
    “…Simulation results show that the Modified-PF scheduler provides the best performance in terms of throughput and spectral efficiency with comparale fairness as compared to RR and PF schedulers. …”
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  15. 15
    A new domain specific scripting language for automated machine learning pipeline

    A new domain specific scripting language for automated machine learning pipeline by Masrom, S., Rahman, A.S.A., Omar, N., Baharun, N.

    Published 2019
    “…However, in respond to the implementation difficulty, there exists a limited software tool that support easy implementation for automated machine learning based on Genetic Programming. …”
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  16. 16
    Rapid software framework for the implementation of machine learning classification models

    Rapid software framework for the implementation of machine learning classification models by Rahman, A.S.A., Masrom, S., Rahman, R.A., Ibrahim, R.

    Published 2021
    “…However, to implement a complete machine learning model involves some technical hurdles such as the steep learning curve, the abundance of the programming skills, the complexities of hyper-parameters, and the lack of user friendly platform to be used for the implementation. …”
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  17. 17
    Optimization Of Two-Dimensional Dual Beam Scanning System Using Genetic Algorithms

    Optimization Of Two-Dimensional Dual Beam Scanning System Using Genetic Algorithms by Koh, Johnny Siaw Paw

    Published 2008
    “…Also, this research involves in developing a machine-learning system and program via genetic algorithm that is capable of performing independent learning capability and optimization for scanning sequence using novel GA operators. …”
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  18. 18
    Quality of service and scheduling performance optimization in LTE networks / Abubakar Auwal Idris

    Quality of service and scheduling performance optimization in LTE networks / Abubakar Auwal Idris by Abubakar Auwal , Idris

    Published 2017
    “…The developed algorithm is simulated in LTE-Sim simulator released 5 in terms of three QoS metrics (1) Packet Loss Ratio (2) Delay (3) Throughput. …”
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  19. 19
    OPTIMIZATION OF HYBRID-FUZZY CONTROLLER FOR SERVOMOTOR CONTROL USING A MODIFIED GENETIC ALGORITHM

    OPTIMIZATION OF HYBRID-FUZZY CONTROLLER FOR SERVOMOTOR CONTROL USING A MODIFIED GENETIC ALGORITHM by WAHYUNGGORO, OYAS WAHYUNGGORO

    Published 2011
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  20. 20
    A novel LTE scheduling algorithm for green technology in smart grid

    A novel LTE scheduling algorithm for green technology in smart grid by Hindia, M.N., Reza, A.W., Noordin, K.A., Chayon, M.H.R.

    Published 2015
    “…In terms of fairness, the proposed algorithm shows 3, 7 and 9 better performance compared to exponential rule (EXP-Rule), modified-largest weighted delay first (M-LWDF) and exponential/PF (EXP/PF), respectively.…”
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