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  1. 1
    An Empirical Study on the Construction of A Non-Convex Risk Parity Portfolio using a Genetic Algorithm

    An Empirical Study on the Construction of A Non-Convex Risk Parity Portfolio using a Genetic Algorithm by Kusumawati, Rosita, Rosadi, Dedi, Abdurakhman, Abdurakhman

    Published 2025
    “…Risk-based portfolio optimization has become increasingly crucial due to the limitations and underperformance of traditional Mean-Variance (MV) portfolios. …”
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  2. 2
    A comparison of watermarking image quality based on dual intermediate significant bit with genetic algorithm

    A comparison of watermarking image quality based on dual intermediate significant bit with genetic algorithm by Yasin, Azman, M. Zeki, Akram, Mohammed, Ghassan N.

    Published 2013
    “…In this case, when the two hidden bits are equal to the original bits, there will be no change to the other remaining bits.However, if the original value is not equal to the embedded one, the nearest pixel to the original one will be chosen as the watermarked image.The second method, GA method is used to embed two bits of watermarking data within every pixel of the original image and to find the optimal value based on the existing DISB.On the other hand, if the two embedded bits are equal to the original bits then this means the watermarked image is still the same as the original one without any changes, while in the other case GA is used in determining the minimum fitness value in which the fittest is the absolute value between the pixel and chromosome and the value of chromosome between 0-255.The results indicate that the two methods produce a high quality watermarked image, but there is a big difference in the processing time, so the DISB method is faster than the GA method.…”
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  3. 3
    High image quality watermarking model by using genetic algorithm

    High image quality watermarking model by using genetic algorithm by Mohammed, Ghassan N., Yasin, Azman, Zeki, Akram M.

    Published 2012
    “…In this study, Genetic Algorithm (GA) method is used to embed two bits of watermarking data within every pixel of original image and to find the optimal value based on the existing Dual Intermediate Significant Bit (DISB). …”
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  4. 4
    A modified weighted support vector machine (WSVM) to reduce noise data in classification problem

    A modified weighted support vector machine (WSVM) to reduce noise data in classification problem by Mohd Dzulkifli, Syarizul Amri

    Published 2021
    “…To overcome SVM drawback for noise data problem, WSVM using KPCM algorithm was used but WSVM using kernel-based learning algorithm such as KPCM algorithm suffer from training complexity, expensive computation time and storage memory when noise data contaminate training data. …”
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  5. 5
    A modified weighted support vector machine (WSVM) to reduce noise data in classification problem

    A modified weighted support vector machine (WSVM) to reduce noise data in classification problem by Mohd Dzulkifli, Syarizul Amri

    Published 2021
    “…To overcome SVM drawback for noise data problem, WSVM using KPCM algorithm was used but WSVM using kernel-based learning algorithm such as KPCM algorithm suffer from training complexity, expensive computation time and storage memory when noise data contaminate training data. …”
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  6. 6
    Mitigation of atmospheric turbulences using mode division multiplexing based on decision feedback equalizer for free space optics

    Mitigation of atmospheric turbulences using mode division multiplexing based on decision feedback equalizer for free space optics by Almogahed, Abdullah, Amphawan, Angela, Fazea, Yousef

    Published 2017
    “…This paper investigates the mitigation of atmospheric turbulences of FSO using MDM and decision feedback equalizer (DFE) with minimum mean square error (MMSE) algorithm. The implementation of the MMSE algorithm is used to optimize both the feedforward and the feedback filter coefficients of DFE. …”
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  7. 7
    High image quality watermarking model by using genetic algorithm

    High image quality watermarking model by using genetic algorithm by Mohammed, Ghassan N., Yasin, Azman, Zeki, Akram M.

    Published 2012
    “…Many studies try to enhance the quality by using different techniques and methods.In this study, Genetic Algorithm (GA) method is used to embed two bits of watermarking data within every pixel of original image and to find the optimal value based on the existing Dual Intermediate Significant Bit (DISB).However, if the two embedded bits is equal to the original bits then this means the watermarked image is still the same as the original one without any changing, while in the other case GA is used getting the minimum fitness value in which the fitness is the absolute value between the pixel and chromosome and the value of chromosome between 0-255.The results show that the new method improves the image quality and get the optimal value for the two embedded bits.…”
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  8. 8
    Development of prediction model for phosphate in reservoir water system based machine learning algorithms

    Development of prediction model for phosphate in reservoir water system based machine learning algorithms by Latif S.D., Birima A.H., Ahmed A.N., Hatem D.M., Al-Ansari N., Fai C.M., El-Shafie A.

    Published 2023
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  9. 9
    Technical report: genetic algorithm for vehicle routing problem / Mohammad Izwan Jamaluddin and Muhamad Syahmie Adeeb Mohd Shukri

    Technical report: genetic algorithm for vehicle routing problem / Mohammad Izwan Jamaluddin and Muhamad Syahmie Adeeb Mohd Shukri by Jamaluddin, Mohammad Izwan, Mohd Shukri, Muhamad Syahmie Adeeb

    Published 2016
    “…Based on the study that have been conducted the minimum routes is equal to 3990. …”
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  10. 10
    New signed-digit {0,1,3}-NAF scalar multiplication algorithm for elliptic curve over binary field

    New signed-digit {0,1,3}-NAF scalar multiplication algorithm for elliptic curve over binary field by Md Yasin, Sharifah

    Published 2011
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  11. 11
    Prediction of shear strength of concrete using the artificial neural network / R. Rohim, S.F. Senin and N.F. Azman

    Prediction of shear strength of concrete using the artificial neural network / R. Rohim, S.F. Senin and N.F. Azman by Rohim, Rohamezan, Senin, Syahrul Fithry, Azman, N.F.

    Published 2022
    “…The remaining 18 (35%) mixes data were divided equally into testing and validation data sets. …”
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  12. 12
    Acquisition of new subscribers using analytical models in the telecommunication industry / Nik Muhammad Naim Nik Ghazali

    Acquisition of new subscribers using analytical models in the telecommunication industry / Nik Muhammad Naim Nik Ghazali by Nik Ghazali, Nik Muhammad Naim

    Published 2020
    “…The proposed algorithm for building the analytical acquisition model benefits the Data Science community to explore the use of optimization model in their work domain. …”
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  13. 13
    Optimized clustering with modified K-means algorithm

    Optimized clustering with modified K-means algorithm by Alibuhtto, Mohamed Cassim

    Published 2021
    “…Among the techniques, the k-means algorithm is the most commonly used technique for determining optimal number of clusters (k). …”
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  14. 14
    Meta-heuristic approaches for reservoir optimisation operation and investigation of climate change impact at Klang gate dam

    Meta-heuristic approaches for reservoir optimisation operation and investigation of climate change impact at Klang gate dam by Lai, Vivien Mei Yen

    Published 2023
    “…The Whale Optimisation Algorithm (WOA), Harris Hawks Optimisation (HHO) Algorithm, Lévy Flight WOA (LFWOA) and the Opposition-Based Learning of HHO (OBL-HHO) were proposed to simulate the initial model’s response and optimise the Klang Gate Dam (KGD) release operation with observed inflow, water level (storage), release, and evaporation rate (loss). …”
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  15. 15
    A near-optimal centroids initialization in K-means algorithm using bees algorithm

    A near-optimal centroids initialization in K-means algorithm using bees algorithm by Mahmuddin, Massudi, Yusof, Yuhanis

    Published 2009
    “…This creates problem for novice users especially to those who have no or little knowledge on the data.Trial-error attempt might be one of the possible preference to deal with this issue.In this paper, an optimization algorithm inspired from the bees foraging activities is used to locate near-optimal centroid of a given data set.Result shows that propose approached prove it robustness and competence in finding a near optimal centroid on both synthetic and real data sets.…”
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  16. 16
    Application of Bat Algorithm and Its Modified Form Trained with ANN in Channel Equalization

    Application of Bat Algorithm and Its Modified Form Trained with ANN in Channel Equalization by Pradyumna Kumar Mohapatra, Saroja Kumar Rout, Sukant Kishoro Bisoy, Sandeep Kautish, Muzaffar Hamzah, Muhammed Basheer Jasser, Ali Wagdy Mohamed

    Published 2022
    “…An alternative approach to training neural network-based equalizers is to use metaheuristic algorithms. …”
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  17. 17
    Data clustering using the bees algorithm

    Data clustering using the bees algorithm by Pham, D.T, Otri, S., Afify, A., Mahmuddin, Massudi, Al-Jabbouli, H.

    Published 2007
    “…K-means clustering involves search and optimization. …”
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  18. 18
    An improvement of stochastic gradient descent approach for mean-variance portfolio optimization problem

    An improvement of stochastic gradient descent approach for mean-variance portfolio optimization problem by S. W. Su, Stephanie, Kek, Sie Long

    Published 2021
    “…Furthermore, the applicability of SGD, Adam, AdaMax, Nadam, AMSGrad, and AdamSE algorithms in solving the mean-variance portfolio optimization problem is validated.…”
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  19. 19
    Optimization of the hidden layer of a multilayer perceptron with backpropagation (bp) network using hybrid k-means-greedy algorithm (kga) for time series prediction

    Optimization of the hidden layer of a multilayer perceptron with backpropagation (bp) network using hybrid k-means-greedy algorithm (kga) for time series prediction by Tan, James Yiaw Beng

    Published 2012
    “…The proposed KGA model combines greedy algorithm withk-means++ clustering in this research to assist users in automating the finding of the optimal number of new-ons inside the hidden layer of the BP network. …”
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  20. 20
    Improved stochastic gradient descent algorithm with mean-gradient adaptive stepsize for solving large-scale optimization problems

    Improved stochastic gradient descent algorithm with mean-gradient adaptive stepsize for solving large-scale optimization problems by Zulkifli, Munierah, Abd Rahmin, Nor Aliza, Wah, June Leong

    Published 2023
    “…SGD uses random or batch data sets to compute gradient in solving optimization problems. …”
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