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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
    “…Anomaly detection algorithms model normal behavior. Anomaly detection models compare sensor data to normal patterns learned from the training data by using statistical method and try to detect activity that deviates from normal activity. …”
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  2. 2
    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
    “…Anomaly detection algorithms model normal behavior. Anomaly detection models compare sensor data to normal patterns learned from the training data by using statistical method and try to detect activity that deviates from normal activity. …”
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  3. 3
    Features selection for intrusion detection system using hybridize PSO-SVM

    Features selection for intrusion detection system using hybridize PSO-SVM by Tabaan, Alaa Abdulrahman

    Published 2016
    “…Features selection process can be considered a problem of global combinatorial optimization in machine learning. Genetic algorithm GA had been adopted to perform features selection method; however, this method could not deliver an acceptable detection rate, lower accuracy, and higher false alarm rates. …”
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  4. 4
    Artificial immune system based on real valued negative selection algorithms for anomaly detection

    Artificial immune system based on real valued negative selection algorithms for anomaly detection by Khairi, Rihab Salah

    Published 2015
    “…Experimental results illustrate that RNSA and V-Detector algorithms are suitable for the detection of anomalies, with SVM and KNN producing significant efficiency rates and increase in execution time. …”
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  5. 5
    Cabbage disease detection system using k-NN algorithm

    Cabbage disease detection system using k-NN algorithm by Mohamad Ainuddin Sahimat

    Published 2022
    “…Then, the segmented cabbage sample will use the GLCM method for feature extraction. It is a method of extracting second-order statistical texture features to detect diseases more efficiently. …”
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    Academic Exercise
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  6. 6
    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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  7. 7
    Region duplication forgery detection technique based on keypoint matching / Diaa Mohammed Hassan Uliyan

    Region duplication forgery detection technique based on keypoint matching / Diaa Mohammed Hassan Uliyan by Diaa , Mohammed Hassan Uliyan

    Published 2016
    “…The average detection rate of our algorithm maintained 96 % true positive rate and 7 % false positive rate which outperform several current detection methods. …”
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  8. 8
    Automated Face Detection Using Skin Color Segmentation and Viola-Jones Algorithm

    Automated Face Detection Using Skin Color Segmentation and Viola-Jones Algorithm by Ahmad Fakhri, Ab. Nasir, Ahmad Shahrizan, Abdul Ghani, Muhammad Aizzat, Zakaria, Anwar, P. P. Abdul Majeed, Ahmad Najmuddin, Ibrahim

    Published 2019
    “…Viola-Jones algorithm can be categorized as one of an established and effective method (feature-based approach) for detecting face. …”
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  9. 9
    Outbreak detection model based on danger theory

    Outbreak detection model based on danger theory by Mohamad Mohsin, Mohamad Farhan, Abu Bakar, Azuraliza, Hamdan, Abdul Razak

    Published 2014
    “…Two outbreak diseases, dengue and SARS, are subjected to a danger theory algorithm; namely the dendritic cell algorithm.To evaluate the model, four measurement metrics are applied: detection rate, specificity, false alarm rate, and accuracy. …”
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  10. 10
    A Systematic Review of Anomaly Detection within High Dimensional and Multivariate Data

    A Systematic Review of Anomaly Detection within High Dimensional and Multivariate Data by Suboh, Syahirah, Abdul Aziz, Izzatdin, Shaharudin, Shazlyn Milleana, Ismail, Saidatul Akmar, Mahdin, Hairulnizam

    Published 2023
    “…It has been found that PCA, DOBIN, Stray algorithm, and DAE-KNN have a high learning rate compared to Random projection, ROBEM, and OCP methods. …”
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  11. 11
    A Systematic Review of Anomaly Detection within High Dimensional and Multivariate Data

    A Systematic Review of Anomaly Detection within High Dimensional and Multivariate Data by Suboh, Syahirah, Abdul Aziz, Izzatdin, Shaharudin, Shazlyn Milleana, Ismail, Saidatul Akmar, Mahdin, Hairulnizam

    “…It has been found that PCA, DOBIN, Stray algorithm, and DAE-KNN have a high learning rate compared to Random projection, ROBEM, and OCP methods. …”
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  12. 12
    A Systematic Review of Anomaly Detection within High Dimensional and Multivariate Data

    A Systematic Review of Anomaly Detection within High Dimensional and Multivariate Data by Suboh, Syahirah, Abdul Aziz, Izzatdin, Shaharudin, Shazlyn Milleana, Ismail, Saidatul Akmar, Mahdin, Hairulnizam

    Published 2023
    “…It has been found that PCA, DOBIN, Stray algorithm, and DAE-KNN have a high learning rate compared to Random projection, ROBEM, and OCP methods. …”
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  13. 13
    A Systematic Review of Anomaly Detection within High Dimensional and Multivariate Data

    A Systematic Review of Anomaly Detection within High Dimensional and Multivariate Data by Suboh, Syahirah, Abdul Aziz, Izzatdin, Shaharudin, Shazlyn Milleana, Ismail, Saidatul Akmar, Mahdin, Hairulnizam

    Published 2023
    “…It has been found that PCA, DOBIN, Stray algorithm, and DAE-KNN have a high learning rate compared to Random projection, ROBEM, and OCP methods. …”
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  14. 14
    A Systematic Review of Anomaly Detection within High Dimensional and Multivariate Data

    A Systematic Review of Anomaly Detection within High Dimensional and Multivariate Data by Suboh, Syahirah, Abdul Aziz, Izzatdin, Shaharudin, Shazlyn Milleana, Ismail, Saidatul Akmar, Mahdin, Hairulnizam

    Published 2023
    “…It has been found that PCA, DOBIN, Stray algorithm, and DAE-KNN have a high learning rate compared to Random projection, ROBEM, and OCP methods. …”
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  15. 15
    A Systematic Review of Anomaly Detection within High Dimensional and Multivariate Data

    A Systematic Review of Anomaly Detection within High Dimensional and Multivariate Data by Suboh, Syahirah, Abdul Aziz, Izzatdin, Shaharudin, Shazlyn Milleana, Akmar Ismail, Saidatul, Mahdin, Hairulnizam

    Published 2023
    “…It has been found that PCA, DOBIN, Stray algorithm, and DAE-KNN have a high learning rate compared to Random projection, ROBEM, and OCP methods. …”
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  16. 16
    A Systematic Review of Anomaly Detection within High Dimensional and Multivariate Data

    A Systematic Review of Anomaly Detection within High Dimensional and Multivariate Data by Suboh, Syahirah, Abdul Aziz, Izzatdin, Shaharudin, Shazlyn Milleana, Akmar Ismail, Saidatul, Mahdin, Hairulnizam

    Published 2023
    “…It has been found that PCA, DOBIN, Stray algorithm, and DAE-KNN have a high learning rate compared to Random projection, ROBEM, and OCP methods. …”
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  17. 17
    A COMBINED HISTOGRAM OF ORIENTED GRADIENTS AND COMPLETED LOCAL BINARY PATTERN METHODS FOR PEOPLE COUNTING IN A DENSE CROWD SCENARIO

    A COMBINED HISTOGRAM OF ORIENTED GRADIENTS AND COMPLETED LOCAL BINARY PATTERN METHODS FOR PEOPLE COUNTING IN A DENSE CROWD SCENARIO by PARDIANSYAH, INDRATNO

    Published 2016
    “…This method used a collaborative Histogram of Oriented Gradients (HOG) and Completed Local Binary Pattern (CLBP) based on people detection algorithm to detect headshoulder region. …”
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  18. 18
    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
    “…In this paper, a â��butterflyâ�� shape derived from the manipulation of the standard PV and OP data, which is more robust towards different loop dynamics, is developed for stiction detection. 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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  19. 19
    Pairwise clusters optimization and cluster most significant feature methods for anomaly-based network intrusion detection system (POC2MSF) / Gervais Hatungimana

    Pairwise clusters optimization and cluster most significant feature methods for anomaly-based network intrusion detection system (POC2MSF) / Gervais Hatungimana by Hatungimana, Gervais

    Published 2018
    “…Anomaly-based Intrusion Detection System (IDS) uses known baseline to detect patterns which have deviated from normal behaviour. …”
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  20. 20
    COMPUTER AIDED SYSTEM FOR BREAST CANCER DIAGNOSIS USING CURVELET TRANSFORM

    COMPUTER AIDED SYSTEM FOR BREAST CANCER DIAGNOSIS USING CURVELET TRANSFORM by MOHAMED ELTOUKHY, MOHAMED MESELHY

    Published 2011
    “…Experimental results show that the introduced methods have very high detection accuracies. The effectiveness of the proposed methods has been tested with Mammographic Image Analysis Society (MIAS) dataset. …”
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