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  1. 1
    Experimental study of urban growth pattern classification using moving window algorithm

    Experimental study of urban growth pattern classification using moving window algorithm by Ghani N.L.A., Abidin S.Z.Z.

    Published 2023
    “…Urban growth pattern can be generally categorized as either infill, expansion or outlying growth. Moving window algorithm determines urban growth pattern based on moving window analysis and a set of classification rules. …”
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  2. 2
    Overview of metaheuristic: classification of population and trajectory

    Overview of metaheuristic: classification of population and trajectory by Zainul Rashid, Zarina

    Published 2010
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    Monograph
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  3. 3
    An improved pixel-based and region-based approach for urban growth classification algorithms / Nur Laila Ab Ghani

    An improved pixel-based and region-based approach for urban growth classification algorithms / Nur Laila Ab Ghani by Ab Ghani, Nur Laila

    Published 2015
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  4. 4
    Fair bandwidth distribution marking and scheduling algorithm in network traffic classification

    Fair bandwidth distribution marking and scheduling algorithm in network traffic classification by Al-Kharasani, Ameen Mohammed Abdulkarem

    Published 2019
    “…Second, propose an Optimized Time Sliding Window based Three Colour Marker. Finally, propose a new method of obtaining optimal parameters dropping functions for Random Early Detection (RED) algorithm. …”
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  5. 5
    Non-fiducial based electrocardiogram biometrics with kernel methods

    Non-fiducial based electrocardiogram biometrics with kernel methods by Hejazi, Maryamsadat

    Published 2017
    “…The effectiveness of this algorithm is investigated by comparing with other AC based feature extraction algorithms involving AC/LDA (Linear Discriminant Analysis) and AC/PCA (Principal Component Analysis). …”
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  6. 6
    Random subspace K-NN based ensemble classifier for driver fatigue detection utilizing selected EEG channels

    Random subspace K-NN based ensemble classifier for driver fatigue detection utilizing selected EEG channels by Rashid, Mamunur, Mahfuzah, Mustafa, Norizam, Sulaiman, Nor Rul Hasma, Abdullah, Rosdiyana, Samad

    Published 2021
    “…In the present framework, a new channel selection algorithm based on correlation coefficients and an ensemble classifier based on random subspace k-nearest neighbour (k-NN) has been presented to enhance the classification performance of EEG data for driver fatigue detection. …”
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  7. 7
    Block Motion Estimation Using Directional Adaptive Search Window

    Block Motion Estimation Using Directional Adaptive Search Window by Fadly Chew, Nor Farhana, Badruddin, Nasreen

    Published 2006
    “…This classification and the direction of the motion vectors, are used to dynamically adjust the search window size and general orientation. …”
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  8. 8
    Arabic Speaker Identification System for Forensic Authentication Using K-NN Algorithm

    Arabic Speaker Identification System for Forensic Authentication Using K-NN Algorithm by Abdulwahid S., Mahmoud M.A., Abdulwahid N.

    Published 2023
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  9. 9
    UAV-based RGB/NIR aerial imaging for the detection of Ganoderma disease in oil palm plantation / Ezzati Bahrom

    UAV-based RGB/NIR aerial imaging for the detection of Ganoderma disease in oil palm plantation / Ezzati Bahrom by Ezzati , Bahrom

    Published 2018
    “…This study demonstrated classification from UAV-based imagery can be improved using filters for Ganoderma disease detection mapping in oil palm plantation. …”
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  10. 10
    Deep learning object detector using a combination of Convolutional Neural Network (CNN) architecture (MiniVGGNet) and classic object detection algorithm

    Deep learning object detector using a combination of Convolutional Neural Network (CNN) architecture (MiniVGGNet) and classic object detection algorithm by Ismail, Asmida, Ahmad, Siti Anom, Che Soh, Azura, Hassan, Mohd Khair, Harith, Hazreen Haizi

    Published 2020
    “…Based on the experiment result, the percentage of classification accuracy of the network is 80% to 90% and the time for the system to detect the object is less than 15sec/frame. …”
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  11. 11
    A voting-based hybrid machine learning approach for fraudulent financial data classification / Kuldeep Kaur Ragbir Singh

    A voting-based hybrid machine learning approach for fraudulent financial data classification / Kuldeep Kaur Ragbir Singh by Kuldeep Kaur , Ragbir Singh

    Published 2019
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  12. 12
    Direct least squares fitting of ellipses segmentation and prioritized rules classification for curve-shaped chart patterns

    Direct least squares fitting of ellipses segmentation and prioritized rules classification for curve-shaped chart patterns by Lei, I. L., Teh, Phoey Lee *, Si, Yain-Whar

    Published 2021
    “…These methods are implemented based on the principles of sliding windows, turning points, and bottom-up piece wise linear approximation. …”
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  13. 13
    Block motion estimation using directional adaptive search window

    Block motion estimation using directional adaptive search window by Chew , N.F.F., Badruddin ., Nasreen

    Published 2007
    “…This classification and the direction of the motion vectors, are used to dynamically adjust the search window size and general orientation. …”
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  14. 14
    Automatic microsleep detection based on KNN classifier utilizing selected and effective EEG channels

    Automatic microsleep detection based on KNN classifier utilizing selected and effective EEG channels by Hasan, Md Mahmudul, Hossain, Mirza Mahfuj, Norizam, Sulaiman, Islam, Md Nahidul, Khandaker, Sayma

    Published 2024
    “…The selection of an optimum channel and the use of a competent classification algorithm are crucial for effective microsleep detection. …”
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  15. 15
    Radar - Acoustic Vehicle Classification System based on shallow Convolutional Neural Network

    Radar - Acoustic Vehicle Classification System based on shallow Convolutional Neural Network by Yasmin, Aida

    Published 2025
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  16. 16
    A hybrid scheme for AEP based hearing deficiency diagnosis: CWT and convoluted k-nearest neighbour (CKNN) pipeline

    A hybrid scheme for AEP based hearing deficiency diagnosis: CWT and convoluted k-nearest neighbour (CKNN) pipeline by Islam, Md Nahidul, Norizam, Sulaiman, Bari, Bifta Sama, Rashid, Mamunur, Mahfuzah, Mustafa

    Published 2022
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  17. 17
    A hybrid scheme for AEP based hearing deficiency diagnosis: CWT and convoluted k-nearest neighbour (CKNN) pipeline

    A hybrid scheme for AEP based hearing deficiency diagnosis: CWT and convoluted k-nearest neighbour (CKNN) pipeline by Islam, Md Nahidul, Norizam, Sulaiman, Bari, Bifta Sama, Rashid, Mamunur, Mahfuzah, Mustafa

    Published 2022
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  18. 18
    Epileptic seizure detection using singular values and classical features of EEG signals

    Epileptic seizure detection using singular values and classical features of EEG signals by Elmahdy, A.E., Yahya, N., Kamel, N.S., Shahid, A.

    Published 2015
    “…Using CHB-MIT Scalp EEG Database, the calculations of the features are performed over a sliding window of one second. The algorithm was evaluated in terms of accuracy, sensitivity, specificity and failure rate. …”
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  19. 19
    Glass break detection system using deep auto encoders with fuzzy rules induction algorithm

    Glass break detection system using deep auto encoders with fuzzy rules induction algorithm by Nyein Naing, Wai Yan, Htike, Zaw Zaw

    Published 2019
    “…This paper proposes a new design of a glass break detection algorithm based on Fuzzy Deep Auto-encoder Neural Network. …”
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  20. 20
    Comparison of supervised machine learning algorithms for malware detection / Mohd Faris Mohd Fuzi ... [et al.]

    Comparison of supervised machine learning algorithms for malware detection / Mohd Faris Mohd Fuzi ... [et al.] by Mohd Fuzi, Mohd Faris, Mohd Shahirudin, Syamir, Abd Halim, Iman Hazwam, Jamaluddin, Muhammad Nabil Fikri

    Published 2023
    “…This study was solely concerned with the Windows malware dataset. The malware classification was determined by testing and training the supervised ML algorithms using the extracted features from the malware dataset. …”
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