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  1. 1
    An online density-based clustering algorithm for data stream based on local optimal radius and cluster pruning

    An online density-based clustering algorithm for data stream based on local optimal radius and cluster pruning by Islam, Md Kamrul

    Published 2019
    “…Experimental results are compared with other alternative online/offline hybrid density-based clustering algorithms. The average processing time for data point in the data stream is about 2 milliseconds which is much lower than the aligned clustering algorithms in literature. …”
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  2. 2
    A study of fluctuations and confidence of implementation in genetic algorithm optimized network in data centre

    A study of fluctuations and confidence of implementation in genetic algorithm optimized network in data centre by Nurika, O., Hassan, M.F., Zakaria, N., Jung, L.T.

    Published 2018
    “…The purpose of this study is to observe the reliability of genetic algorithm in our previously simulated network optimization in a data centre. …”
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  3. 3
    A study of fluctuations and confidence of implementation in genetic algorithm optimized network in data centre

    A study of fluctuations and confidence of implementation in genetic algorithm optimized network in data centre by Nurika, O., Hassan, M.F., Zakaria, N., Jung, L.T.

    Published 2018
    “…The purpose of this study is to observe the reliability of genetic algorithm in our previously simulated network optimization in a data centre. …”
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  4. 4
    Improving the Muskingum flood routing method using a hybrid of particle swarm optimization and bat algorithm

    Improving the Muskingum flood routing method using a hybrid of particle swarm optimization and bat algorithm by Ehteram M., Othman F.B., Yaseen Z.M., Afan H.A., Allawi M.F., Malek M.B.A., Ahmed A.N., Shahid S., Singh V.P., El-Shafie A.

    Published 2023
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  5. 5
    An improved data classification framework based on fractional particle swarm optimization

    An improved data classification framework based on fractional particle swarm optimization by Sherwani, Fahad

    Published 2019
    “…The proposed algorithm is tested and verified for optimization performance comparison on ten benchmark functions against six existing established algorithms in terms of Mean of Error and Standard Deviation values. …”
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  6. 6
    Improving the Muskingum flood routing method using a hybrid of particle swarm optimization and bat algorithm

    Improving the Muskingum flood routing method using a hybrid of particle swarm optimization and bat algorithm by Ehteram, Mohammad, Othman, Faridah, Yaseen, Zaher Mundher, Afan, Haitham Abdulmohsin, Allawi, Mohammed Falah, Malek, Marlinda Abdul, Ahmed, Ali Najah, Shahid, Shamsuddin, Singh, Vijay P., El-Shafie, Ahmed

    Published 2018
    “…In this study, a hybrid of the bat algorithm (BA) and the particle swarm optimization (PSO) algorithm, i.e., the hybrid bat-swarm algorithm (HBSA), was developed for the optimal determination of these four parameters. …”
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  7. 7
    Balancing exploration and exploitation in ACS algorithms for data clustering

    Balancing exploration and exploitation in ACS algorithms for data clustering by Jabbar, Ayad Mohammed, Sagban, Rafid, Ku-Mahamud, Ku Ruhana

    Published 2019
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  8. 8
    Logistic regression methods for classification of imbalanced data sets

    Logistic regression methods for classification of imbalanced data sets by Santi Puteri Rahayu, -

    Published 2012
    “…Hence, it is required to develop effective imbalanced LR-based methods to be widely used in data mining applications. …”
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  9. 9
    Identification of continuous-time hammerstein model using improved archimedes optimization algorithm

    Identification of continuous-time hammerstein model using improved archimedes optimization algorithm by Islam, Muhammad Shafiqul, Mohd Ashraf, Ahmad, Cho, Bo Wen

    Published 2024
    “…Therefore, this article identified various continuous-time Hammerstein models based on an improved Archimedes optimization algorithm (IAOA) to address these concerns. …”
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  10. 10
    Fractional chaotic ensemble particle swarm optimizer for identifying the single, double, and three diode photovoltaic models� parameters

    Fractional chaotic ensemble particle swarm optimizer for identifying the single, double, and three diode photovoltaic models� parameters by Yousri D., Thanikanti S.B., Allam D., Ramachandaramurthy V.K., Eteiba M.B.

    Published 2023
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  11. 11
    Accuracy enhancement for zone mapping of a solar radiation forecasting based multi-objective model for better management of the generation of renewable energy

    Accuracy enhancement for zone mapping of a solar radiation forecasting based multi-objective model for better management of the generation of renewable energy by Ehteram M., Ahmed A.N., Fai C.M., Afan H.A., El-Shafie A.

    Published 2023
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  12. 12
    Multi-Objective Hybrid Algorithm For The Classification Of Imbalanced Datasets

    Multi-Objective Hybrid Algorithm For The Classification Of Imbalanced Datasets by Saeed, Sana

    Published 2019
    “…Classification of imbalanced datasets remained a significant issue in data mining and machine learning (ML) fields. This research work proposed a new idea based on the optimization for handling the imbalanced datasets. …”
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  13. 13
    Data driven neuroendocrine pid controller for mimo plants based adaptive safe experimentation dynamics algorithm

    Data driven neuroendocrine pid controller for mimo plants based adaptive safe experimentation dynamics algorithm by Mohd Riduwan, Ghazali

    Published 2020
    “…Data-driven tools are an optimization method to find the optimal controller parameters using the system’s input and output data. …”
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  14. 14
    An enhanced sequential exception technique for semantic-based text anomaly detection

    An enhanced sequential exception technique for semantic-based text anomaly detection by Taiye, Mohammed Ahmed

    Published 2019
    “…The detection of semantic-based text anomaly is an interesting research area which has gained considerable attention from the data mining community. …”
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  15. 15
    Investigating dam reservoir operation optimization using metaheuristic algorithms

    Investigating dam reservoir operation optimization using metaheuristic algorithms by Lai V., Essam Y., Huang Y.F., Ahmed A.N., El-Shafie A.

    Published 2023
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  16. 16
    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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  17. 17
    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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  18. 18
    Enhanced long short-term memory with fireworks algorithm and mutation operator

    Enhanced long short-term memory with fireworks algorithm and mutation operator by Changqing Gong, Xinyao Wang, Abdullah Gani, Han Qi

    Published 2021
    “…Prediction models are used to prevent and prepare for corresponding events according to various types of data generated in production. Aiming at the problems of lower predictive accuracy and slower convergent speed of the existing prediction models, a prediction model based on fireworks algorithm (FWA) and long short-term memory (LSTM) is proposed to predict time-related data. …”
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  19. 19
    Statistical data preprocessing methods in distance functions to enhance k-means clustering algorithm

    Statistical data preprocessing methods in distance functions to enhance k-means clustering algorithm by Dalatu, Paul Inuwa

    Published 2018
    “…The suggested approaches are called new approach to min-max (NAMM) and decimal scaling (NADS). The Hybrid mean algorithms which are based on spherical clusters is also proposed to remedy the most significant limitation of the K-Means and K-Midranges algorithms. …”
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  20. 20
    An improved hybrid method combined with a cloud-based supervisory control to facilitate smooth coordination under low-inertia grids

    An improved hybrid method combined with a cloud-based supervisory control to facilitate smooth coordination under low-inertia grids by Yiizzan, Suffian, Ahmed Mohamed, Ahmed Haidar, Wan Azlan, Wan Zainal Abidin, Hazrul, Mohamed Basri

    Published 2025
    “…Specifically, it limits frequency deviations to 0.01 %, significantly outperforming the traditional droop control algorithm, which exhibits deviations of 0.8 %. …”
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