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  1. 1
    The effect of different distance measures in detecting outliers using clustering-based algorithm for circular regression model

    The effect of different distance measures in detecting outliers using clustering-based algorithm for circular regression model by Nur Faraidah, Muhammad Di, Siti Zanariah, Satari

    Published 2017
    “…Then, a stopping rule for the cluster tree based on the mean direction and circular standard deviation of the tree height is proposed. …”
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  2. 2
    Comparative study of clustering-based outliers detection methods in circular-circular regression model

    Comparative study of clustering-based outliers detection methods in circular-circular regression model by Siti Zanariah Satari, Nur Faraidah Muhammad Di, Yong Zulina Zubairi, Abdul Ghapor Hussin

    Published 2021
    “…A stopping rule for the cluster tree based on the mean direction and circular standard deviation of the tree height was used as the cutoff point and classifier to the cluster group that exceeded the stopping rule as potential outliers. …”
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  3. 3
    Comparative study of clustering-based outliers detection methods in circularcircular regression model

    Comparative study of clustering-based outliers detection methods in circularcircular regression model by Siti Zanariah, Satari, Nur Faraidah, Muhammad Di, Yong Zulina, Zubairi, Abdul Ghapor, Hussin

    Published 2021
    “…A stopping rule for the cluster tree based on the mean direction and circular standard deviation of the tree height was used as the cutoff point and classifier to the cluster group that exceeded the stopping rule as potential outliers. …”
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  4. 4
    Comparative study of clustering-based outliers detection methods in circular-circular regression model

    Comparative study of clustering-based outliers detection methods in circular-circular regression model by Siti Zanariah, Satari, Nur Faraidah, Muhammad Di, Yong Zulina, Zubairi, Abdul Ghapor, Hussin

    Published 2021
    “…A stopping rule for the cluster tree based on the mean direction and circular standard deviation of the tree height was used as the cutoff point and classifier to the cluster group that exceeded the stopping rule as potential outliers. …”
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  5. 5
    Three-dimension coronary artery tree curvature confirmation

    Three-dimension coronary artery tree curvature confirmation by Khaleel, Hasan Hadi, O. K. Rahmat, Rahmita Wirza, Dimon, Mohd Zamrin, Mahmod, Ramlan, Mustapha, Norwati

    Published 2010
    “…We propose to use the standard deviation technique to compare the output features of the 3D coronary artery trees. …”
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  6. 6
    Footwear quality evaluation using decision tree and logistic regression models

    Footwear quality evaluation using decision tree and logistic regression models by Tan, Swee Choon

    Published 2022
    “…The analysis showed that Decision Tree with Gini algorithm (three branches) in the first method prevails against the other methods with misclassification rate of 0.1307. …”
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  7. 7
    Analysis of short term load forecasting techniques / Tan Vy Luoh

    Analysis of short term load forecasting techniques / Tan Vy Luoh by Tan, Vy Luoh

    Published 2019
    “…In this report, three common numerical STLF techniques including Multiple Linear Regression (MLR), Curve Fitting and Bagged Tree Regression are proposed to forecast one-day ahead load profile with a yearly historical load data. …”
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  8. 8
    Parameter estimation and outlier detection for some types of circular model / Siti Zanariah binti Satari

    Parameter estimation and outlier detection for some types of circular model / Siti Zanariah binti Satari by Satari, Siti Zanariah

    Published 2015
    “…Then, a stopping rule for the cluster tree based on the mean direction and circular standard deviation of the tree height is proposed. …”
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  9. 9
    A hybrid interpretable deep structure based on adaptive neuro‑fuzzy inference system, decision tree, and K‑means for intrusion detection

    A hybrid interpretable deep structure based on adaptive neuro‑fuzzy inference system, decision tree, and K‑means for intrusion detection by Jia, Lu, Yin Chai, Wang, Chee Siong, Teh, Xinjin, Li, Liping, Zhao, Fengrui, Wei

    Published 2022
    “…The proposed algorithm was trained, validated, and tested on the NSL-KDD (National security lab–knowledge discovery and data mining) dataset. …”
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  10. 10
    SUDOKU HELPER

    SUDOKU HELPER by Abdul Razak, Muhammad Asyraf

    Published 2015
    “…In this paper research, author presents an algorithm to provide a tutorial for any Sudoku player who got stuck during the solving process. …”
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  11. 11
    Implementation of machine learning algorithms for streamflow prediction of Dokan dam

    Implementation of machine learning algorithms for streamflow prediction of Dokan dam by Sarmad Dashti Latif, Mr.

    Published 2023
    “…This study aims at comparing the application of deep learning algorithms and conventional machine learning algorithms for predicting reservoir inflow. …”
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  12. 12
    Developing machine learning algorithms for meteorological temperature and humidity forecasting at Terengganu state in Malaysia

    Developing machine learning algorithms for meteorological temperature and humidity forecasting at Terengganu state in Malaysia by Hanoon M.S., Ahmed A.N., Zaini N., Razzaq A., Kumar P., Sherif M., Sefelnasr A., El-Shafie A.

    Published 2023
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  13. 13
    Identifying multiple outliers in linear functional relationship model using a robust clustering method

    Identifying multiple outliers in linear functional relationship model using a robust clustering method by Adilah Abdul Ghapor, Yong Zulina Zubairi, Al Mamun, Sayed Md., Siti Fatimah Hassan, Elayaraja Aruchunan, Nurkhairany Amyra Mokhtar

    Published 2023
    “…A new robust cut-off point using the median and median absolute deviation for the tree heights to classify the potential outliers are proposed in this study. …”
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  14. 14
    Enhanced Adaptive Neuro-Fuzzy Inference System Classification Method for Intrusion Detection

    Enhanced Adaptive Neuro-Fuzzy Inference System Classification Method for Intrusion Detection by Jia, Liu

    Published 2024
    “…On the KDDTest+ dataset, the proposed method also outperforms single CART and ANFIS in terms of various metrics other than precision. Since the CART tree is a binary tree, it can only represent the relationship between data through a split based on a single attribute at a single tree node. …”
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  15. 15
    Talkout : Protecting mental health application with a lightweight message encryption

    Talkout : Protecting mental health application with a lightweight message encryption by Gavin Teo Juen

    Published 2022
    “…The investigation of lightweight message encryption algorithms is conducted with systematic quantitative literature and experiment implementation in Java and Android running environment. …”
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    Academic Exercise
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  16. 16
    A comparative analysis of classical machine learning and deep learning techniques for predicting lung cancer survivability

    A comparative analysis of classical machine learning and deep learning techniques for predicting lung cancer survivability by Huang S., Arpaci I., Al-Emran M., K?l?�arslan S., Al-Sharafi M.A.

    Published 2024
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  17. 17
    Object-Oriented Programming semantics representation utilizing agents

    Object-Oriented Programming semantics representation utilizing agents by Mohd Aris, Teh Noranis

    Published 2011
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  18. 18
    Multi-floor indoor location estimation system based on wireless local area network

    Multi-floor indoor location estimation system based on wireless local area network by Chua, Tien Han

    Published 2007
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  19. 19
    An improved hybrid learning approach for better anomaly detection

    An improved hybrid learning approach for better anomaly detection by Mohamed Yassin, Warusia

    Published 2011
    “…Therefore, anomaly detection is often associated with high false alarm with only moderate accuracy of detection rates. In recent years, data mining approach for intrusion detection have been proposed and used such as neural networks, clustering, genetic algorithms, decision trees, and support vector machines. …”
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  20. 20
    A malware analysis and detection system for mobile devices / Ali Feizollah

    A malware analysis and detection system for mobile devices / Ali Feizollah by Ali, Feizollah

    Published 2017
    “…We then used feature selection algorithms and deep learning algorithms to build a detection model. …”
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