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  1. 1
    Improving Classification of Remotely Sensed Data Using Best Band Selection Index and Cluster Labelling Algorithms

    Improving Classification of Remotely Sensed Data Using Best Band Selection Index and Cluster Labelling Algorithms by Teoh, Chin Chuang

    Published 2005
    “…In addition, the best band selected for image classification is not necessarily the best for classification.A Best Band Selection Index (BBSI) algorithm was developed which is capable of selecting the best band combination for image visualization and supervised classification. …”
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  2. 2
    Pattern Classification of Human Epithelial Images

    Pattern Classification of Human Epithelial Images by Mohd Isa, Mohd Fazlie

    Published 2016
    “…In this project, there are four stages will be used to analyze pattern classification in human epithelial (HEp-2) images. First of all, image enhancement will take part in order to boost efficiency of algorithm by implementing some of the adjustment and filtering technique to increase the visibility of image. …”
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    Final Year Project
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  3. 3
    Efficient classifying and indexing for large iris database based on enhanced clustering method

    Efficient classifying and indexing for large iris database based on enhanced clustering method by Khalaf, Emad Taha, Mohammed, Muamer N., Kohbalan, Moorthy, Khalaf, Ahmad Taha

    Published 2018
    “…The proposed method can be used to perform global search and exhibits quick convergence rate while optimizing the initial clustering centers of the K-means algorithm. From the experimental results, the proposed method was indeed more effective for clustering and classification and outperformed the traditional k-mean algorithm. …”
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  4. 4
    Improved Fast Fuzzy C-Means Algorithm for Medical MR Images Segmentation

    Improved Fast Fuzzy C-Means Algorithm for Medical MR Images Segmentation by Li, Min, Huang, Tinglei, Zhu, Gangqiang

    Published 2008
    “…Fuzzy c-means (FCM) clustering algorithm has been widely used in automated image segmentation. …”
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  5. 5
    Hybrid 2D phase unwrapping methodology using spatial fuzzy clustering and neurofuzzy classification with application in sar images

    Hybrid 2D phase unwrapping methodology using spatial fuzzy clustering and neurofuzzy classification with application in sar images by Then, Patrick Hang Hui.

    Published 2001
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  6. 6
    Extreme learning machine classification of file clusters for evaluating content-based feature vectors

    Extreme learning machine classification of file clusters for evaluating content-based feature vectors by Ali, Rabei Raad, Mohamad, Kamaruddin Malik, Jamel, Sapiee, Ahmad Khalid, Shamsul Kamal

    Published 2018
    “…The files are allocated in a continuous series of clusters. The ELM algorithm is applied to the DFRWS (2006) dataset and the results show that the combination of the three methods produces 93.46% classification accuracy.…”
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  7. 7
    Plant identification using combination of fuzzy c-means spatial pyramid matching, gist, multi-texton histogram and multiview dictionary learning

    Plant identification using combination of fuzzy c-means spatial pyramid matching, gist, multi-texton histogram and multiview dictionary learning by Safa, Soodabeh

    Published 2016
    “…For next, instead of k-means clustring, Fuzzy cmeans clustering is combined with Spatial Pyramid Matching image representation to improve the accuracy of classification results. …”
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  8. 8
    Classification of JPEG files by using extreme learning machine

    Classification of JPEG files by using extreme learning machine by Ali, Rabei Raad, Mohamad, Kamaruddin Malik, Jamel, Sapiee, Ahmad Khalid, Shamsul Kamal

    Published 2018
    “…This paper proposes an Extreme Learning Machine (ELM) algorithm to assign a class label of JPEG or Non-JPEG image for files in a continuous series of data clusters. …”
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  9. 9
    Image Processing Of Digital Mammograms For Breast Cancer Detection And Classification

    Image Processing Of Digital Mammograms For Breast Cancer Detection And Classification by Mohd Nizom, Ahmad Nabil

    Published 2018
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    Monograph
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  10. 10
    Detecting lung cancer region from CT image using meta-heuristic optimized segmentation approach

    Detecting lung cancer region from CT image using meta-heuristic optimized segmentation approach by Shakeel, Pethuraj Mohamed, Mohd Aboobaider, Burhanuddin, Salahuddin, Lizawati

    Published 2022
    “…An optimal tumor detection requires noise reduced computed tomography (CT) images for pixel classification. In this paper, the butterfly optimization algorithm-based K-means clustering (BOAKMC) method is introduced for reducing CT image segmentation uncertainty. …”
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  11. 11
    Dense-cluster based voting approach for license plate identification

    Dense-cluster based voting approach for license plate identification by Asadzadehkaljahi, Maryam, Shivakumara, Palaiahnakote, Roy, Sangheeta, Olatunde, Mojeed Salmon, Anisi, Mohammad Hossein, Lu, Tong, Pal, Umapada

    Published 2018
    “…This process gives four clusters for the input image. The number of pixels in clusters (dense cluster) and the standard deviation are computed for deriving new hypotheses. …”
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  12. 12
    Blood cell image segmentation using unsupervised clustering techniques

    Blood cell image segmentation using unsupervised clustering techniques by Tuan Muda, Tuan Zalizam, Abdul Salam, Rosalina

    Published 2009
    “…The aim of our research is to develop an effective algorithm for segmentation of the blood cell images. …”
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    Conference or Workshop Item
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  13. 13
    The classification of hunger behaviour of Lates Calcarifer through the integration of image processing technique and k-Nearest Neighbour learning algorithm

    The classification of hunger behaviour of Lates Calcarifer through the integration of image processing technique and k-Nearest Neighbour learning algorithm by Taha, Zahari, Razman, Mohd Azraan, Abdul Ghani, Ahmad Shahrizan, P.P. Abdul Majeed, Anwar, Musa, Rabiu Muazu, A. Adnan, Fatihah, Sallehudin, Muhammad Firdaus, Mukai, Yukinori

    Published 2018
    “…The clustered fish behaviour is then classified through k-Nearest Neighbour (k-NN) learning algorithm. …”
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    Proceeding Paper
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  14. 14
    Intelligent Color Vision System For Ripeness Classification Of Oil Palm Fresh Fruit Bunch

    Intelligent Color Vision System For Ripeness Classification Of Oil Palm Fresh Fruit Bunch by Fadilah, Norasyikin

    Published 2015
    “…The images are collected and analyzed using digital image processing techniques. k-means clustering algorithm is used to segment the image into two separate regions which are fruit and spike regions. …”
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  15. 15
    A new hybrid technique for nosologic segmentation of primary brain tumors / Shafaf Ibrahim

    A new hybrid technique for nosologic segmentation of primary brain tumors / Shafaf Ibrahim by Ibrahim, Shafaf

    Published 2015
    “…This study presents an algorithm for nosologic segmentation of primary brain tumors on Magnetic Resonance Imaging (MRI) brain images. …”
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  16. 16
    Cluster validity of Xie and Beni and the partition coefficient indexes for fuzzy c-means clustering / Nor Azrin Ahmad Mustaffa

    Cluster validity of Xie and Beni and the partition coefficient indexes for fuzzy c-means clustering / Nor Azrin Ahmad Mustaffa by Ahmad Mustaffa, Nor Azrin

    Published 2010
    “…Therefore, segmentation of mammographic images is an important phase in image analysis that can be further applied to other algorithms for specific tasks such as the detection and classification of breast anomalies. …”
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  17. 17
    Unsupervised classification of multi-class chart images: A comparison of customized CNNs and transfer learning techniques

    Unsupervised classification of multi-class chart images: A comparison of customized CNNs and transfer learning techniques by Hassan Zaidi, Syed Muhammad, Jamil Alsayaydeh, Jamil Abedalrahim, Khan, Abdul Hafeez, Khan, Abdullah Ayub, AlZubi, Ahmad Ali, Ogunshola, Benny, Herawan, Safarudin Gazali

    Published 2025
    “…This study investigates the unsupervised classification of chart images using a combination of deep learning and clustering techniques. …”
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  18. 18
    The classification of hunger behaviour of Lates Calcarifer through the integration of image processing technique and k-Nearest Neighbour learning algorithm

    The classification of hunger behaviour of Lates Calcarifer through the integration of image processing technique and k-Nearest Neighbour learning algorithm by Zahari, Taha, M. A. M., Razman, Ahmad Shahrizan, Abdul Ghani, A. P. P., Abdul Majeed, R. M., Musa, F. A., Adnan, M. F., Sallehudin, Y., Mukai

    Published 2018
    “…The clustered fish behaviour is then classified through k-Nearest Neighbour (k-NN) learning algorithm. …”
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  19. 19
    Adaptive Feature Selection and Image Classification Using Manifold Learning Techniques

    Adaptive Feature Selection and Image Classification Using Manifold Learning Techniques by ASHRAF, AMNA, MOHD NAWI, NAZRI, MUHAMMAD AAMIR, MUHAMMAD AAMIR

    Published 2024
    “…In the first phase, the manifold learning approach is used to improve the ‘feature selection by clustering’. Clustering algorithms such as K-means, spectral clustering, and the Gaussian Mixer Model have been tested with manifold learning approaches for adaptive feature selection. …”
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  20. 20
    FACE CLASSIFICATION FOR AUTHENTICATION APPROACH BY USING WAVELET TRANSFORM AND STATISTICAL FEATURES SELECTION

    FACE CLASSIFICATION FOR AUTHENTICATION APPROACH BY USING WAVELET TRANSFORM AND STATISTICAL FEATURES SELECTION by DAWOUD JADALAH, NADIR NOURAIN

    Published 2011
    “…In the last method, the Modified K-Means Algorithm was used to remove the non-face regions in the input image. …”
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