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  1. 1
    Image segmentation based on normalised cuts with clustering algorithm

    Image segmentation based on normalised cuts with clustering algorithm by Choong, Mei Yeen

    Published 2013
    “…Evaluation of c -means and fuzzy c-means clustering algorithm with normalised cuts image segmentation on various kinds of images has been carried out. …”
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    Thesis
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  2. 2
    Android Malware classification using static code analysis and Apriori algorithm improved with particle swarm optimization

    Android Malware classification using static code analysis and Apriori algorithm improved with particle swarm optimization by Adebayo, Olawale Surajudeen, Abdul Aziz, Normaziah

    Published 2014
    “…In this method, features were extracted from Android applications byte-code through static code analysis, selected and were used to train supervised classifiers. …”
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    Proceeding Paper
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  3. 3
    Customer segmentation on clustering algorithms

    Customer segmentation on clustering algorithms by Toh, Wei Xuan

    Published 2023
    “…This report presents an analysis of customer segmentation using various clustering algorithms, including k-means, DBSCAN, GMM, and RFM. …”
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    Final Year Project / Dissertation / Thesis
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  4. 4
    Static code analysis of permission-based features for android malware classification using apriori algorithm with particle swarm optimization

    Static code analysis of permission-based features for android malware classification using apriori algorithm with particle swarm optimization by Adebayo, Olawale Surajudeen, Abdul Aziz, Normaziah

    Published 2015
    “…However, supervised learning technique has limitations for malware classification task. This paper presents a classification approach on android malware using candidate detectors generated from an unsupervised association rule of Apriori Algorithm. …”
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  5. 5
    Unsupervised segmentation technique for acute leukemia cells using clustering algorithms

    Unsupervised segmentation technique for acute leukemia cells using clustering algorithms by Harun, Nor Hazlyna, Abdul Nasir, Aimi Salihah, Mashor, Mohd Yusoff, Hassan, Rosline

    Published 2015
    “…Due to the requirement of prompt and accurate diagnosis of leukaemia, the current study has proposed unsupervised pixel segmentation based on clustering algorithm in order to obtain a fully segmented abnormal white blood cell (blast) in acute leukaemia image.In order to obtain the segmented blast, the current study proposed three clustering algorithms which are k-means, fuzzy c-means and moving k-means algorithms have been applied on the saturation component image. …”
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  6. 6
    Development Of Automatic Liver Segmentation Method For Three- Dimensional Computed Tomography Dataset

    Development Of Automatic Liver Segmentation Method For Three- Dimensional Computed Tomography Dataset by Chew, Chin Boon

    Published 2018
    “…The proposed algorithm provided mean VOE of 26.50%, mean RVD of 15.09% and mean DSC of 0.8421. …”
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    Monograph
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  7. 7
    Segmentation of MRI brain images using statistical approaches

    Segmentation of MRI brain images using statistical approaches by Balafar, Mohammad Ali

    Published 2011
    “…Noise is one of the obstacles for brain MRI segmentation. The non-Local means (NL-means) algorithm is a state-of-the art neighbourhood-based noisereduction method which is time-consuming and its accuracy can be improved. …”
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  8. 8
    Cluster approach for auto segmentation of blast in acute leukimia blood slide images

    Cluster approach for auto segmentation of blast in acute leukimia blood slide images by N. H., Harun, Mohd Yusoff, Mashor, Prof. Dr., H., Rosline

    Published 2011
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    Working Paper
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  9. 9
    Liver segmentation on CT images using random walkers and fuzzy c-means for treatment planning and monitoring of tumors in liver cancer patients

    Liver segmentation on CT images using random walkers and fuzzy c-means for treatment planning and monitoring of tumors in liver cancer patients by Moghbel, Mehrdad

    Published 2017
    “…The proposed method is based on a hybrid method integrating random walkers algorithm with integrated priors and particle swarm optimized spatial fuzzy c-means (FCM) algorithm with level set method and AdaBoost classifier. …”
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  10. 10
    A survey: Challenges of image segmentation based fuzzy c-means clustering algorithm

    A survey: Challenges of image segmentation based fuzzy c-means clustering algorithm by Alomoush W., Alrosan A., Norwawi N., Alomari Y., Albashish D., Almomani A., Alqahtani M.

    Published 2024
    journal::journal article
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  11. 11
    An improved artificial bee colony algorithm based on mean best-guided approach for continuous optimization problems and real brain MRI images segmentation

    An improved artificial bee colony algorithm based on mean best-guided approach for continuous optimization problems and real brain MRI images segmentation by Alrosan, Ayat, Alomoush, Waleed, Norwawi, Norita, Alswaitti, Mohammed, Makhadmeh, Sharif Naser

    Published 2024
    Article
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  12. 12
    Image clustering comparison of two color segmentation techniques

    Image clustering comparison of two color segmentation techniques by Subramaniam, Kavitha Pichaiyan

    Published 2010
    “…This project proposed a two color segmentation techniques such as K-means and Fuzzy C-means clustering algorithm that are accurately segment the desired images, which have the same color as the pre-selected pixels with background subtraction. …”
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  13. 13
    Adaptive Hybrid Blood Cell Image Segmentation

    Adaptive Hybrid Blood Cell Image Segmentation by Muda, TZT, Salam, RA, Ismail, S

    Published 2024
    Proceedings Paper
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  14. 14
    A rule-based image segmentation method and neural network model for classifying fruit in natural environment / Hamirul'aini Hambali

    A rule-based image segmentation method and neural network model for classifying fruit in natural environment / Hamirul'aini Hambali by Hambali, Hamirul'aini

    Published 2015
    “…Therefore, the improved thresholding-based segmentation (TsN) is integrated with the Adaptive K-means thus resulting in rule-based segmentation namely TsNKM method. …”
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  15. 15
    A hyper-heuristic based strategy for image segmentation using multilevel thresholding

    A hyper-heuristic based strategy for image segmentation using multilevel thresholding by Luqman, Fakhrud Din, Shah Khalid, Kamal Zuhairi Zamli, Aftab Alam

    Published 2025
    “…EMCQ uses four low-level heuristic sets adopted from the teaching learning-based optimisation (TLBO) algorithm, flower pollination algorithm (FPA), genetic algorithm (GA), and Jaya algorithm. …”
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  16. 16
    A hyper-heuristic based strategy for image segmentation using multilevel thresholding

    A hyper-heuristic based strategy for image segmentation using multilevel thresholding by Luqman, ., Fakhrud, Din, Shah, Khalid, Kamal Z., Zamli, Alam, Aftab

    Published 2025
    “…EMCQ uses four low-level heuristic sets adopted from the teaching learning-based optimisation (TLBO) algorithm, flower pollination algorithm (FPA), genetic algorithm (GA), and Jaya algorithm. …”
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  17. 17
    Development of Acute Stroke Lesion Segmentation Algorithm in Brain MRI using Pseudo-colour with K-means Clustering

    Development of Acute Stroke Lesion Segmentation Algorithm in Brain MRI using Pseudo-colour with K-means Clustering by Abang Mohd Arif Anaqi, Abang Isa

    Published 2021
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  18. 18
    Adaptive Hybrid Blood Cell Image Segmentation

    Adaptive Hybrid Blood Cell Image Segmentation by Tuan Muda, Tuan Zalizam, Abdul Salam, Rosalina, Ismail, Suzilah

    Published 2019
    “…In this paper, we present an adaptive hybrid analysis based on selected segmentation algorithms. Three designates common approaches, that are Fuzzy c-means, K-means and Mean-shift are adapted. …”
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    Conference or Workshop Item
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  19. 19
    Feature identification in a real surface metrology analysis by means of Double Iteration Sobel (DIS) / Ainaa Farhanah Mohd Razali

    Feature identification in a real surface metrology analysis by means of Double Iteration Sobel (DIS) / Ainaa Farhanah Mohd Razali by Mohd Razali, Ainaa Farhanah

    Published 2022
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  20. 20
    Network Traffic Classification Analysis on Differentiated Services Code Point Using Deep Learning Models for Efficient Deep Packet Inspection

    Network Traffic Classification Analysis on Differentiated Services Code Point Using Deep Learning Models for Efficient Deep Packet Inspection by Ahmed Khan, Fazeel, Abubakar, Adamu

    Published 2024
    “…This study develops and analyze using neural network-based models for effective classification of data packets using the DSCP header field. …”
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