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  1. 1
    Detection of acute leukaemia cells using variety of features and neural networks

    Detection of acute leukaemia cells using variety of features and neural networks by A. S., Abdul Nasir, Mohd Yusoff, Mashor, Prof. Dr., H., Rosline

    Published 2011
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  2. 2
    Immune Multiagent System for Network Intrusion Detection using Non-linear Classification Algorithm

    Immune Multiagent System for Network Intrusion Detection using Non-linear Classification Algorithm by Mohamed, M. E, Samir, B. B., Azween, Abdullah

    Published 2010
    “…A new non classification algorithm was developed based on the danger theory model of human immune system (HIS).The abstract model of system algorithm is inspired from HIS cell mechanism mainly, the Dendritic cell behavior and T-cell mechanisms. …”
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  3. 3
    HEP-2 CELL IMAGES CLASSIFICATION BASED ON STATISTICAL TEXTURE ANALYSIS AND FUZZY LOGIC

    HEP-2 CELL IMAGES CLASSIFICATION BASED ON STATISTICAL TEXTURE ANALYSIS AND FUZZY LOGIC by Jamil, Nur Farahim

    Published 2014
    “…A working classification algorithm is developed by using MATLAB and the Fuzzy Logic Toolbox to differentiate and classify the staining pattern of HEp-2 cell images. …”
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  4. 4
    FLUORESCENCE INTENSITY POSITIVITY CLASSIFICATION OF HEP-2 CELLS IMAGES USING FUZZY LOGIC

    FLUORESCENCE INTENSITY POSITIVITY CLASSIFICATION OF HEP-2 CELLS IMAGES USING FUZZY LOGIC by ABANG SAZALI, DAYANG FARZANAI

    Published 2013
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  5. 5
    Object-Oriented Programming semantics representation utilizing agents

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

    Published 2011
    “…Novices tend to refer to source codes examples and adapt the source codes to the problem given in their assignments. …”
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  6. 6
    Scene classification for aerial images based on CNN using sparse coding technique

    Scene classification for aerial images based on CNN using sparse coding technique by Qayyum, A., Malik, A.S., Saad, N.M., Iqbal, M., Faris Abdullah, M., Rasheed, W., Rashid Abdullah, T.A., Bin Jafaar, M.Y.

    Published 2017
    “…Recent developments include several approaches and numerous algorithms address the task. This article proposes a convolutional neural network (CNN) approach that utilizes sparse coding for scene classification applicable for HRRS unmanned aerial vehicle (UAV) and satellite imagery. …”
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  7. 7
    Breast cancer disease classification using fuzzy-ID3 algorithm with FUZZYDBD method: automatic fuzzy database definition

    Breast cancer disease classification using fuzzy-ID3 algorithm with FUZZYDBD method: automatic fuzzy database definition by Nur Farahaina, Idris, Mohd Arfian, Ismail

    Published 2021
    “…This paper proposed the fuzzy-ID3 (FID3) algorithm, a fuzzy decision tree as the classification method in breast cancer detection. …”
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  8. 8
    Maldroid- attribute selection analysis for malware classification

    Maldroid- attribute selection analysis for malware classification by Rahiwan Nazar, Romli, Mohamad Fadli, Zolkipli, Mohd Zamri, Osman

    Published 2019
    “…Hence, the objective of this paper is to find the most effective and efficient attribute selection and classification algorithm in malware detection. Moreover, in order to get the best combination between attribute selection and classification algorithm, eight attributes selection and seven categories machine learning algorithm are applied in this study. …”
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  9. 9
    Scene classification for aerial images based on CNN using sparse coding technique

    Scene classification for aerial images based on CNN using sparse coding technique by Qayyum A., Malik A.S., Saad N.M., Iqbal M., Faris Abdullah M., Rasheed W., Rashid Abdullah T.A., Bin Jafaar M.Y.

    Published 2023
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  10. 10
    Geometric Feature Extraction for Identification and Classification of Overlapping Cells for Leukaemia

    Geometric Feature Extraction for Identification and Classification of Overlapping Cells for Leukaemia by Siew Ming, Kiu, Yin Chai, Wang

    Published 2022
    “…This paper describes the study of overlapping leukaemia cells based on geometric features for identification and classification. …”
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  11. 11
    HEp-2 cell images classification based on statistical texture analysis and fuzzy logic

    HEp-2 cell images classification based on statistical texture analysis and fuzzy logic by Jamil, N.F.B., Faye, I., May, Z.

    Published 2014
    “…This paper proposes a pattern recognition algorithm consisting of statistical methods to extract seven textural features from the HEp-2 cell images followed by classification of staining patterns by using fuzzy logic. …”
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  12. 12
    Classification of acute leukemia using image processing and machine learning techniques / Hayan Tareq Abdul Wahhab

    Classification of acute leukemia using image processing and machine learning techniques / Hayan Tareq Abdul Wahhab by Wahhab, Hayan Tareq Abdul

    Published 2015
    “…In total, 601 features were extracted from each blast cell, and its nucleus: 31of these were shape-based features, while 534 were texture-based features and 36 were color-based features. …”
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  13. 13
    Geometric Feature Extraction for Identification and Classification of the Overlapping Cells for Leukaemia

    Geometric Feature Extraction for Identification and Classification of the Overlapping Cells for Leukaemia by Kiu, Siew Ming

    Published 2018
    “…Overlapping cell identification and classification of microscopic blood cell image is proposed to increase the accuracy of the number of automated counting and the percentage of the overall accuracy. …”
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  14. 14
    New Algorithm of Location Model based on Robust Estimators and Smoothing Approach

    New Algorithm of Location Model based on Robust Estimators and Smoothing Approach by Hamid, Hashibah

    Published 2017
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  15. 15
    Classification of Diabetes Mellitus (DM) using Machine Learning Algorithms

    Classification of Diabetes Mellitus (DM) using Machine Learning Algorithms by Sirajun Noor, Noor Azmiya

    Published 2021
    “…The objective of this study is to perform DM classification using various machine learning algorithms using Weka as a tool. …”
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  16. 16
    Whale Optimisation Freeman Chain Code (WO-FCC) extraction algorithm for handwritten character recognition

    Whale Optimisation Freeman Chain Code (WO-FCC) extraction algorithm for handwritten character recognition by Muhammad Arif, Mohamad, Jamaludin, Sallim, Kohbalan, Moorthy

    Published 2021
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  17. 17
    Utilisation of Exponential-Based Resource Allocation and Competition in Artificial Immune Recognition System

    Utilisation of Exponential-Based Resource Allocation and Competition in Artificial Immune Recognition System by Hormozi, Shahram Golzari

    Published 2011
    “…Artificial Immune Recognition System is one of the several immune inspired algorithms that can be used to perform classification, a data mining task. …”
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  18. 18
    Classification of Diabetes Mellitus using Ensemble Algorithms

    Classification of Diabetes Mellitus using Ensemble Algorithms by Noor, N.A.B.S., Elamvazuthi, I., Yahya, N.

    Published 2021
    “…The objective of this study is to perform DM classification using various machine learning algorithms. …”
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  19. 19
    An Adaptive Fuzzy Contrast Enhancement Algorithm with Details Preserving

    An Adaptive Fuzzy Contrast Enhancement Algorithm with Details Preserving by Jing, Rui Tang, Mat Isa, Nor Ashidi

    Published 2014
    “…In addition, the experiments using cervical cell images and HEp-2 cell images showed great potential of the AFCEDP technique as a technique for enhancing medical microscopic images.…”
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  20. 20
    Validation on an enhanced dendrite cell algorithm using statistical analysis

    Validation on an enhanced dendrite cell algorithm using statistical analysis by Mohamad Mohsin, Mohamad Farhan, Hamdan, Abdul Razak, Abu Bakar, Azuraliza, Abd Wahab, Mohd Helmy

    Published 2017
    “…In practice, most of studies apply a straightforward approach for evaluation where appropriate performance metrics such as classification accuracy is selected, computes the mean and its variance over several repetitive experiments, and then compares it with the base algorithm or other comparative approach. …”
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