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  1. 1
    Hybrid ant colony system algorithm for static and dynamic job scheduling in grid computing

    Hybrid ant colony system algorithm for static and dynamic job scheduling in grid computing by Alobaedy, Mustafa Muwafak Theab

    Published 2015
    “…The algorithms are ACS(GA), ACS+GA, ACS(TS), and ACS+TS. …”
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  2. 2
    The application of voltage sags pattern to locate a faulted section in distribution network

    The application of voltage sags pattern to locate a faulted section in distribution network by Mokhlis, Hazlie, Li, H.Y., Khalid, A.R.

    Published 2010
    “…A pattern matching approach between simulated voltage sag and measurement is proposed to identify the faulted section. …”
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  3. 3
    Survival versus non-survival prediction after acute coronary syndrome in Malaysian population using machine learning technique / Nanyonga Aziida

    Survival versus non-survival prediction after acute coronary syndrome in Malaysian population using machine learning technique / Nanyonga Aziida by Nanyonga , Aziida

    Published 2019
    “…Combinations of feature selection and classification algorithms were used for mortality prediction post ACS. …”
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  4. 4
    An improved ACS algorithm for data clustering

    An improved ACS algorithm for data clustering by Mohammed Jabbar, Ayad, Ku-Mahamud, Ku Ruhana, Sagban, Rafid

    Published 2020
    “…Data clustering is a data mining technique that discovers hidden patterns by creating groups (clusters) of objects. …”
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  5. 5
    VLSI Design of a neurohardware processor implementing the Kohonen Neural Network algorithm

    VLSI Design of a neurohardware processor implementing the Kohonen Neural Network algorithm by Rajah, Avinash

    Published 2005
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  6. 6
    Xilinx (FPGA) design for single phase DC-AC matrix converter with sinusoidal pulse width modulation (SPWM) / Mohd Raduan Md Adam

    Xilinx (FPGA) design for single phase DC-AC matrix converter with sinusoidal pulse width modulation (SPWM) / Mohd Raduan Md Adam by Md Adam, Mohd Raduan

    Published 2003
    “…One sine waveform with one carrier waveforms is used to generate the PWM. The PWM pattern proposes occupies less FPGA block cell hence more space could be used for other control algorithm. …”
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  7. 7
    An efficient fuzzy clustering algorithm for mining user session clusters on web log data

    An efficient fuzzy clustering algorithm for mining user session clusters on web log data by Mallik, M. A., Zulkurnain, Nurul Fariza

    Published 2021
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  8. 8
    Ensemble Classifier for Recognition of Small Variation in X-Bar Control Chart Patterns

    Ensemble Classifier for Recognition of Small Variation in X-Bar Control Chart Patterns by Alwan, Waseem, Ngadiman, Nor Hasrul Akhmal, Hassan, Adnan, Saufi, Syahril Ramadhan, Mahmood, Salwa

    Published 2023
    “…The ensemble classifier can distinguish unstable pattern types with a classification accuracy of 99.55% and an ARL1 of 11.94.…”
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  9. 9
    An artificial neural network model for multi dimension reduction and data structure exploration, Proceedings of International Conference of Soft Computing and Pattern Recognition (SoCPaR 2009), December 4-7, 2009

    An artificial neural network model for multi dimension reduction and data structure exploration, Proceedings of International Conference of Soft Computing and Pattern Recognition (... by Teh, Chee Siong, Ming Leong, Yii, Chen, Chwen Jen

    Published 2009
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  10. 10
    Ensemble Classifier for Recognition of Small Variation in X-Bar Control Chart Patterns

    Ensemble Classifier for Recognition of Small Variation in X-Bar Control Chart Patterns by Waseem Alwan, Waseem Alwan, Ngadiman, Nor Hasrul Akhmal, Hassan, Adnan, Syahril Ramadhan Saufi, Syahril Ramadhan Saufi, Mahmood, Salwa

    Published 2023
    “…The ensemble classifier can distinguish unstable pattern types with a classification accuracy of 99.55% and an ARL1 of 11.94.…”
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  11. 11
    Ensemble Classifier for Recognition of Small Variation in X-Bar Control Chart Patterns

    Ensemble Classifier for Recognition of Small Variation in X-Bar Control Chart Patterns by Alwan, Waseem, Ngadiman, Nor Hasrul Akhmal, Hassan, Adnan, Ramadhan Saufi, Syahril, Mahmood, Salwa

    Published 2023
    “…The ensemble classifier can distinguish unstable pattern types with a classification accuracy of 99.55% and an ARL1 of 11.94.…”
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  12. 12
    Ensemble Classifier for Recognition of Small Variation in X-Bar Control Chart Patterns

    Ensemble Classifier for Recognition of Small Variation in X-Bar Control Chart Patterns by Alwan, Waseem, Ngadiman, Nor Hasrul Akhmal, Hassan, Adnan, Saufi, Syahril Ramadhan, Mahmood, Salwa

    Published 2023
    “…The ensemble classifier can distinguish unstable pattern types with a classification accuracy of 99.55% and an ARL1 of 11.94.…”
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  13. 13
    Ensemble Classifier for Recognition of Small Variation in X-Bar Control Chart Patterns

    Ensemble Classifier for Recognition of Small Variation in X-Bar Control Chart Patterns by Waseem Alwan, Waseem Alwan, Ngadiman, Nor Hasrul Akhmal, Adnan Hassan, Adnan Hassan, Syahril Ramadhan Saufi, Syahril Ramadhan Saufi, Mahmood, Salwa

    Published 2023
    “…The ensemble classifier can distinguish unstable pattern types with a classification accuracy of 99.55% and an ARL1 of 11.94.…”
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  14. 14
    Ensemble Classifier for Recognition of Small Variation in X-Bar Control Chart Patterns

    Ensemble Classifier for Recognition of Small Variation in X-Bar Control Chart Patterns by Alwan, Waseem, Ngadiman, Nor Hasrul Akhmal, Hassan, Adnan, Ramadhan Saufi, Syahril, Mahmood, Salwa

    Published 2023
    “…The ensemble classifier can distinguish unstable pattern types with a classification accuracy of 99.55% and an ARL1 of 11.94.…”
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  15. 15
    Ensemble Classifier for Recognition of Small Variation in X-Bar Control Chart Patterns

    Ensemble Classifier for Recognition of Small Variation in X-Bar Control Chart Patterns by Alwan, Waseem, Ngadiman, Nor Hasrul Akhmal, Hassan, Adnan, Saufi, Syahril Ramadhan, Mahmood, Salwa

    Published 2023
    “…The ensemble classifier can distinguish unstable pattern types with a classification accuracy of 99.55% and an ARL1 of 11.94.…”
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  16. 16
    Ensemble Classifier for Recognition of Small Variation in X-Bar Control Chart Patterns

    Ensemble Classifier for Recognition of Small Variation in X-Bar Control Chart Patterns by Waseem Alwan, Waseem Alwan, Nor Hasrul Akhmal Ngadiman, Nor Hasrul Akhmal Ngadiman, Adnan Hassan, Adnan Hassan, Syahril Ramadhan Saufi, Syahril Ramadhan Saufi, Salwa Mahmood, Salwa Mahmood

    Published 2023
    “…The ensemble classifier can distinguish unstable pattern types with a classification accuracy of 99.55% and an ARL1 of 11.94.…”
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  17. 17
    Ensemble Classifier for Recognition of Small Variation in X-Bar Control Chart Patterns

    Ensemble Classifier for Recognition of Small Variation in X-Bar Control Chart Patterns by Waseem Alwan, Waseem Alwan, Nor Hasrul Akhmal Ngadiman, Nor Hasrul Akhmal Ngadiman, Adnan Hassan, Adnan Hassan, Syahril Ramadhan Saufi, Syahril Ramadhan Saufi, Salwa Mahmood, Salwa Mahmood

    Published 2023
    “…The ensemble classifier can distinguish unstable pattern types with a classification accuracy of 99.55% and an ARL1 of 11.94.…”
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  18. 18
    Facial image retrieval on semantic features using adaptive mean genetic algorithm

    Facial image retrieval on semantic features using adaptive mean genetic algorithm by Shnan, Marwan Ali, Rassem, Taha H., Nor Saradatul Akmar, Zulkifli

    Published 2019
    “…The performance matrix of the AMGA was compared to those of particle swarm optimization algorithm (PSO) and genetic algorithm (GA) and found to perform better; thus, proving its efficiency.…”
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  19. 19
    Extraction and Optimization of Fuzzy Protein Sequences Classification Rules Using GRBF Neural Networks

    Extraction and Optimization of Fuzzy Protein Sequences Classification Rules Using GRBF Neural Networks by Wang, Dianhui, Lee, Nung Kion, Dillon, Tharam S.

    Published 2003
    “…Traditionally, two protein sequences are classified into the same class if their feature patterns have high homology. These feature patterns were originally extracted by sequence alignment algorithms, which measure similarity between an unseen protein sequence and identified protein sequences. …”
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  20. 20
    A simple model-free butterfly shape-based detection (BSD) method integrated with deep learning CNN for valve stiction detection and quantification

    A simple model-free butterfly shape-based detection (BSD) method integrated with deep learning CNN for valve stiction detection and quantification by Kamaruddin, B., Zabiri, H., Mohd Amiruddin, A.A.A., Teh, W.K., Ramasamy, M., Jeremiah, S.S.

    Published 2020
    “…This simple model-free butterfly shape-based detection (BSD) method uses Stenman's one parameter stiction model, which results in a distinctive â��butterflyâ�� pattern in the presence of stiction. …”
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