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  1. 1
    Algorithm enhancement for host-based intrusion detection system using discriminant analysis

    Algorithm enhancement for host-based intrusion detection system using discriminant analysis by Dahlan, Dahliyusmanto

    Published 2004
    “…Algorithms for building detection models are usually classified into two categories: misuse detection and anomaly detection. …”
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    Thesis
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  2. 2
    Hybrid weight deep belief network algorithm for anomaly-based intrusion detection system

    Hybrid weight deep belief network algorithm for anomaly-based intrusion detection system by Maseer, Ziadoon Kamil

    Published 2022
    “…In future, the HW-DBN algorithm can be proposed as an integrated deep Learning for the classification performance of attack detection models.…”
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  3. 3
    A study on advanced statistical analysis for network anomaly detection

    A study on advanced statistical analysis for network anomaly detection by Ngadi, Md. Asri, Idris, Mohd. Yazid, Abdullah, Abd. Hanan

    Published 2005
    “…Algorithms for building detection models are usually classified into two categories: misuse detection and anomaly detection. …”
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    Monograph
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  4. 4
    Process fault detection and diagnosis using Boolean representation on fatty acid fractionation column

    Process fault detection and diagnosis using Boolean representation on fatty acid fractionation column by Othman, M. R., Ali, Mohamad Wijayanuddin, Kamsah, Mohd. Zaki

    Published 2003
    “…Through the proposed algorithm, various faults could be simulated and detected using the system. …”
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  5. 5
    Using streaming data algorithm for intrusion detection on the vehicular controller area network

    Using streaming data algorithm for intrusion detection on the vehicular controller area network by Sharmin, Shaila, Mansor, Hafizah, Abdul Kadir, Andi Fitriah, Abdul Aziz, Normaziah

    Published 2022
    “…The detection model is trained with only message timing information, making it applicable to all vehicles regardless of make and model. …”
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  6. 6
    Continuous collision detection for virtual environments: A walkthrough of techniques

    Continuous collision detection for virtual environments: A walkthrough of techniques by Sulaiman, Hamzah Asyrani, Bade, Abdullah

    Published 2011
    “…While DCD requires backtracking after collision has been detected, CCD does not suffer the same problems as DCD. …”
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  7. 7
    Detecting High Impedance Fault in Power Distribution Feeder with Fuzzy Subtractive Clustering Model

    Detecting High Impedance Fault in Power Distribution Feeder with Fuzzy Subtractive Clustering Model by Sulaiman , Marizan, Adnan, Tawafan, Ibrahim, Zulkifilie

    Published 2013
    “…This paper proposes an intelligent algorithm using the Takagi Sugeno- Kang (TSK) fuzzy modeling approach based on subtractive clustering to detect the high impedance fault. …”
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  8. 8
    Broken Conductor Detection on Power Distribution Feeder

    Broken Conductor Detection on Power Distribution Feeder by Sulaiman , Marizan, Tawafan, Adnan, Ibrahim, Zulkifilie

    Published 2013
    “…It proposes an intelligent algorithm using the Fuzzy Subtractive Clustering Model (FSCM) to detect the high impedance fault. …”
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  9. 9
    High Impedance Fault Detection on Power Distribution Feeder

    High Impedance Fault Detection on Power Distribution Feeder by Sulaiman , Marizan, Tawafan, Adnan, Ibrahim, Zulkifilie

    Published 2012
    “…This paper presents an intelligent algorithm using a Takagi Sugeno-Kang (TSK) fuzzy modeling approach based on subtractive clustering to detect high impedance fault. …”
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  10. 10
    A Review of Unsupervised Machine Learning Frameworks for Anomaly Detection in Industrial Applications

    A Review of Unsupervised Machine Learning Frameworks for Anomaly Detection in Industrial Applications by Usmani, U.A., Happonen, A., Watada, J.

    Published 2022
    “…Currently, academic literature does not really cover the topic of unsupervised machine learning techniques for anomaly detection. …”
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  11. 11
    Detection of surface defects of aluminium extrudants using artificial intelligence

    Detection of surface defects of aluminium extrudants using artificial intelligence by Poon, Chee Kent

    Published 2024
    “…However, the implementation of attention modules does not increase the performance of the model. The final model achieved a mAP@0.5 of 94.3% and 79.7% of mAP@0.5:0.95 compared to the original YOLOv8n model. …”
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  12. 12
    Integrated combined layer algorithm of jamming detection and classification in manet / Ahmad Yusri Dak

    Integrated combined layer algorithm of jamming detection and classification in manet / Ahmad Yusri Dak by Dak, Ahmad Yusri

    Published 2019
    “…This creates the jamming detection and classification parameters. The second stage is detecting jammers by integrating both lower layers by developing Integrated Combined Layer Algorithm (ICLA). …”
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  13. 13
    Alternate methods for anomaly detection in high-energy physics via semi-supervised learning

    Alternate methods for anomaly detection in high-energy physics via semi-supervised learning by Md. Ali, Mohd. Adli, Badrud’din, Nu’man, Abdullah, Hafidzul, Kemi, Faiz

    Published 2020
    “…The algorithm will flag any sample that does not fit the background feature as an abnormality. …”
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  14. 14
    Wind Turbines: Novel Control Algorithm in Region II

    Wind Turbines: Novel Control Algorithm in Region II by Oudah, Ali A., Izzeldin, I. Mohd, A., Hameed, Zulkifli, Md. Yusof

    Published 2014
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  15. 15
    Extremal region detection and selection with fuzzy encoding for food recognition

    Extremal region detection and selection with fuzzy encoding for food recognition by Razali @ Ghazali, Mohd Norhisham

    Published 2019
    “…A high number of interest regions does not guarantee outstanding classification performance as redundant interest regions as well as interest regions from food images with complex background were detected. …”
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  16. 16
    Adaptive Neural Subtractive Clustering Fuzzy Inference System for the Detection of High Impedance Fault on Distribution Power System

    Adaptive Neural Subtractive Clustering Fuzzy Inference System for the Detection of High Impedance Fault on Distribution Power System by Tawafan, Adnan, Sulaiman , Marizan, Ibrahim, Zulkifilie

    Published 2012
    “…This paper proposes an intelligent algorithm using an adaptive neural- Takagi Sugeno-Kang (TSK) fuzzy modeling approach based on subtractive clustering to detect high impedance fault. …”
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  17. 17
    Dynamic Robust Bootstrap Algorithm for Linear Model Selection Using Least Trimmed Squares

    Dynamic Robust Bootstrap Algorithm for Linear Model Selection Using Least Trimmed Squares by Uraibi, Hassan Sami

    Published 2009
    “…To rectify this problem, we propose a Dynamic Robust Bootstrap-LTS based (DRBLTS) algorithm where the percentage of outliers in each bootstrap sample is detected. …”
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  18. 18
    Skin Colour Detection Based On An Adaptive Multi-Thresholding Technique

    Skin Colour Detection Based On An Adaptive Multi-Thresholding Technique by Mharib, Ahmed M.

    Published 2007
    “…This thesis shows that the Gaussian mixture model alone or the Gaussian single model does not improve the performance of the skin detection model due to the number of false detections for high correct classification. …”
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  19. 19
    An Automated Method For Model-Plant Mismatch Detection And Correction In Process Plants Employing Model Predictive Control (MPC)

    An Automated Method For Model-Plant Mismatch Detection And Correction In Process Plants Employing Model Predictive Control (MPC) by Ahmed Bahakim, Sami Saeed

    Published 2012
    “…Since MPM has significant effect on MPC performance, the model has to be corrected and updated whenever high MPM is detected. …”
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    Final Year Project
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  20. 20
    An Evolutionary Stream Clustering Technique for Outlier Detection

    An Evolutionary Stream Clustering Technique for Outlier Detection by Supardi, N.A., Abdulkadir, S.J., Aziz, N.

    Published 2020
    “…Later, this algorithm will be extended to optimize the model in detecting outlier on data streams. …”
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