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  1. 1
    Dorsal Hand Vein Pattern Recognition: A Comparison between Manual and Automatic Segmentation Methods

    Dorsal Hand Vein Pattern Recognition: A Comparison between Manual and Automatic Segmentation Methods by Waheed Ali Laghari, Waheed Ali Laghari, Audrey Huong, Audrey Huong, Kim Gaik Tay, Kim Gaik Tay, Chang Choon Chew, Chang Choon Chew

    Published 2023
    “…This work compared manual segmentation with our proposed hybrid automatic segmentation method (HHM) for this classification problem. …”
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  2. 2
    Dorsal Hand Vein Pattern Recognition: A Comparison between Manual and Automatic Segmentation Methods

    Dorsal Hand Vein Pattern Recognition: A Comparison between Manual and Automatic Segmentation Methods by Waheed Ali Laghari, Waheed Ali Laghari, Audrey Huong, Audrey Huong, Kim Gaik Tay, Kim Gaik Tay, Chang Choon Chew, Chang Choon Chew

    Published 2023
    “…This work compared manual segmentation with our proposed hybrid automatic segmentation method (HHM) for this classification problem. …”
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  3. 3
    Dorsal Hand Vein Pattern Recognition: A Comparison between Manual and Automatic Segmentation Methods

    Dorsal Hand Vein Pattern Recognition: A Comparison between Manual and Automatic Segmentation Methods by Waheed Ali Laghari, Waheed Ali Laghari, Audrey Huong, Kim Gaik Tay, Audrey Huong, Kim Gaik Tay, Chang Choon Chew, Chang Choon Chew

    Published 2023
    “…This work compared manual segmentation with our proposed hybrid automatic segmentation method (HHM) for this classification problem. …”
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  4. 4
    Dorsal Hand Vein Pattern Recognition: A Comparison between Manual and Automatic Segmentation Methods

    Dorsal Hand Vein Pattern Recognition: A Comparison between Manual and Automatic Segmentation Methods by Waheed Ali Laghari, Waheed Ali Laghari, Audrey Huong, Audrey Huong, Kim Gaik Tay, Kim Gaik Tay, Chang Choon Chew, Chang Choon Chew

    Published 2023
    “…This work compared manual segmentation with our proposed hybrid automatic segmentation method (HHM) for this classification problem. …”
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  5. 5
    Dorsal Hand Vein Pattern Recognition: A Comparison between Manual and Automatic Segmentation Methods

    Dorsal Hand Vein Pattern Recognition: A Comparison between Manual and Automatic Segmentation Methods by Waheed Ali Laghari, Waheed Ali Laghari, Audrey Huong, Kim Gaik Tay, Audrey Huong, Kim Gaik Tay, Chang Choon Chew, Chang Choon Chew

    Published 2023
    “…This work compared manual segmentation with our proposed hybrid automatic segmentation method (HHM) for this classification problem. …”
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  6. 6
    Comprehensive review of retinal blood vessel segmentation and classification techniques: intelligent solutions for green computing in medical images, current challenges, open issues, and knowledge gaps in fundus medical images

    Comprehensive review of retinal blood vessel segmentation and classification techniques: intelligent solutions for green computing in medical images, current challenges, open issue... by Abdulsahib A.A., Mahmoud M.A., Mohammed M.A., Rasheed H.H., Mostafa S.A., Maashi M.S.

    Published 2023
    Review
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  7. 7
    Improved Genetic Algorithm Multilayer Perceptron Network For Data Classification

    Improved Genetic Algorithm Multilayer Perceptron Network For Data Classification by Ahmad, Fadzil

    Published 2017
    “…Based on the occurrences of the best result obtained by an algorithm across different test functions; it is proven that the proposed method outperforms standard GA. …”
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  8. 8
    APPLICATION OF LINK GRAMMAR IN SEMI-SUPERVISED NAMED ENTITY RECOGNITION FOR ACCIDENT DOMAIN

    APPLICATION OF LINK GRAMMAR IN SEMI-SUPERVISED NAMED ENTITY RECOGNITION FOR ACCIDENT DOMAIN by SARI, YUNITA SARI

    Published 2011
    “…The Self-Training algorithm greatly benefits semi-supervised learning which allows classification of entities given only a small-size of labelled data. …”
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  9. 9
    Integration Of Unsupervised Clustering Algorithm And Supervised Classifier For Pattern Recognition

    Integration Of Unsupervised Clustering Algorithm And Supervised Classifier For Pattern Recognition by Leong, Shi Xiang

    Published 2017
    “…In pattern recognition system, achieving high accuracy in pattern classification is crucial. …”
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  10. 10
    FEATURES EXTRACTION OF HEP-2 IMMUNOFLUORESCENCE PATTERNS BASED ON TEXTURE AND REGION OF INTEREST TECHNIQUES

    FEATURES EXTRACTION OF HEP-2 IMMUNOFLUORESCENCE PATTERNS BASED ON TEXTURE AND REGION OF INTEREST TECHNIQUES by MD HASIM, SITI MASTURA

    Published 2013
    “…Next, to design an algorithm that can automatically identify the 2 patterns of the HEp-2 cell tested using ANA. …”
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    Final Year Project
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  11. 11
    Pattern Recognition for Human Diseases Classification in Spectral Analysis

    Pattern Recognition for Human Diseases Classification in Spectral Analysis by Nur Hasshima Hasbi, Abdullah Bade, Fuei, Pien Chee, Muhammad Izzuddin Rumaling

    Published 2022
    “…The use of UV/Vis, IR, and Raman spectroscopy for disease classification is also highlighted. To conclude, many pattern recognition algorithms have the potential to overcome each of their distinct limits, and there is also the option of combining all of these algorithms to create an ensemble of methods.…”
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  12. 12
    Hybrid ACO and SVM algorithm for pattern classification

    Hybrid ACO and SVM algorithm for pattern classification by Alwan, Hiba Basim

    Published 2013
    “…Support Vector Machine (SVM) is a pattern classification approach originated from statistical approaches. …”
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  13. 13
    Formulating new enhanced pattern classification algorithms based on ACO-SVM

    Formulating new enhanced pattern classification algorithms based on ACO-SVM by Alwan, Hiba Basim, Ku-Mahamud, Ku Ruhana

    Published 2013
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  14. 14
    Grid-Based Classifier as a Replacement for Multiclass Classifier in a Supervised Non-Parametric Approach

    Grid-Based Classifier as a Replacement for Multiclass Classifier in a Supervised Non-Parametric Approach by Moheb Pour, Majid Reza

    Published 2009
    “…Pattern recognition/classification has received a considerable attention in engineering fields. …”
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  15. 15
    A modified fuzzy min-max neural network with a genetic-algorithm-based rule extractor for pattern classification

    A modified fuzzy min-max neural network with a genetic-algorithm-based rule extractor for pattern classification by Quteishat, A., Lim, C.P., Tan, K.S.

    Published 2010
    “…In this paper, a two-stage pattern classification and rule extraction system is proposed. …”
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  16. 16
    Classification for large number of variables with two imbalanced groups

    Classification for large number of variables with two imbalanced groups by Ahmad Hakiim, Jamaluddin

    Published 2020
    “…This study proposed two algorithms of classification namely Algorithm 1 and Algorithm 2 which combine resampling, variable extraction, and classification procedure. …”
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  17. 17
    Multilevel learning in Kohonen SOM network for classification problems

    Multilevel learning in Kohonen SOM network for classification problems by Mohd. Yusof, Norfadzila

    Published 2006
    “…Self-organizing map (SOM) is a feed-forward neural network approach that uses an unsupervised learning algorithm has shown a particular ability for solving the problem of classification in pattern recognition. …”
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  18. 18
    A derivative-free optimization method for solving classification problem

    A derivative-free optimization method for solving classification problem by Shabanzadeh, Parvaneh, Abu Hassan, Malik, Leong, Wah June

    Published 2010
    “…For optimization generalized pattern search method has been applied. The results of numerical experiments allowed us to say the proposed algorithms are effective for solving classification problems at least for databases considered in this study.…”
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  19. 19
    Analytical framework for predicting online purchasing behavior in Malaysia using a machine learning approach

    Analytical framework for predicting online purchasing behavior in Malaysia using a machine learning approach by Mustakim, Nurul Ain

    Published 2025
    “…The framework uses machine learning methods, including classification, clustering, feature selection, and parameter tuning, to improve accuracy and reliability. …”
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  20. 20
    Mussels wandering optimization algorithmn based training of artificial neural networks for pattern classification

    Mussels wandering optimization algorithmn based training of artificial neural networks for pattern classification by Abusnaina, Ahmed A., Abdullah, Rosni

    Published 2013
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