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  1. 1

    Detection of corneal arcus using rubber sheet and machine learning methods by Ramlee, Ridza Azri

    Published 2019
    “…In this research, two categories of eye’s images which are the normal, and the abnormal (i.e. CA) are used. The normal eye, dataset are taken from the eye database (i.e. …”
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    Thesis
  2. 2

    VGG16-based deep learning architectures for classification of lung sounds into normal, crackles, and wheezes using Gammatonegrams by Zakaria, Neili, Sundaraj, Kenneth

    Published 2023
    “…Breathing sounds are a rich source of information that can assist doctors in diagnosing pulmonary diseases in a non-invasive manner. Several algorithms can be developed based on these sounds to create an automatic classification system for lung diseases. …”
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    Conference or Workshop Item
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    Sauvola Segmentation and Support Vector Machine-Salp Swarm Algorithm Approach for Identifying Nutrient Deficiencies in Citrus Reticulata Leaves by Lia, Kamelia

    Published 2024
    “…The proposed method integrates colour and texture feature-based image analysis with machine learning algorithms for classification. …”
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    Thesis
  5. 5

    Application Of Neural Network In Malaria Parasites Classification by Lim, Chia Li

    Published 2006
    “…Multilayer Perceptron (MLP) network and Radial Basis Function (RBF) network will be developed using MATLAB in which MLP network is trained with Back Propagation, Bayesian Rule and Levenberg-Marquardt learning algorithm and RBF network is trained with k-means clustering algorithm. …”
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    Monograph
  6. 6

    Deep learning-based item classification for retail automation by Ling, Ji Xiang

    Published 2025
    “…This project focuses on developing a deep learning-based system for retail item classification. …”
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    Final Year Project / Dissertation / Thesis
  7. 7

    Detection on ambiguous software requirements specification written in malay using machine learning by Zahrin, Mohd Firdaus

    Published 2017
    “…Four (4) algorithms have been evaluated to find the suitable classification algorithm for this purpose. …”
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    Thesis
  8. 8

    Pengkelasan Sel Kanser Pangkal Rahim Kepada Sel Normal Dan Tidak Normal Menggunakan Analisis Pembezalayan Dan Rangkaian Neural by Saidin, Mohammad Norrish

    Published 2006
    “…The neural network is trained using two types of learning algorithms, which is Levenberg-Marquardt and Back Propagation. …”
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    Monograph
  9. 9

    Converged Classification Network For Matching Cost Computation by Hamid, Mohd Saad, Abd Manap, Nurulfajar, Hamzah, Rostam Affendi, Kadmin, Ahmad Fauzan

    Published 2020
    “…Overall quantitative results for the proposed work performed competitively compared to other established stereo matching algorithm based on the Middlebury standard benchmark online system.…”
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    Article
  10. 10

    Handgrip strength evaluation using neuro fuzzy approach by Seng, W.C., Chitsaz, M.

    Published 2010
    “…Handgrip assessment is a useful method to monitor patient rehabilitation. The neurofuzzy analysis provides system identification and interpretability of fuzzy models and learning capability of neural networks. …”
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    Article
  11. 11

    Simultaneous fault diagnosis based on multiple kernel support vector machine in nonlinear dynamic distillation column by Taqvi, S.A.A., Zabiri, H., Uddin, F., Naqvi, M., Tufa, L.D., Kazmi, M., Rubab, S., Naqvi, S.R., Maulud, A.S.

    Published 2022
    “…In the developed MK-SVM algorithm, multilabel approach based on various kernel functions has been utilized for the classification of simultaneous faults. …”
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    Article
  12. 12

    Heart sound diagnosis using nonlinear ARX model / Noraishah Shamsuddin by Shamsuddin, Noraishah

    Published 2011
    “…The system uses neural network for model estimation and classification of several heart diseases. …”
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    Thesis
  13. 13

    Analysis of hyperspectral reflectance for disease classification of soybean frogeye leaf spot using Knime analytics by Ang, Yuhao, Mohd Shafri, Helmi Zulhaidi

    Published 2023
    “…In order to select the most significant wavelengths, genetic algorithm (GA) was used as a forward feature selection technique. …”
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    Article
  14. 14

    Metaheuristic-Based Neural Network Training And Feature Selector For Intrusion Detection by Ghanem, Waheed Ali Hussein Mohammed

    Published 2019
    “…However, the training datasets usually compose feature sets of irrelevant or redundant information, which impacts the performance of classification, and traditional learning algorithms such as backpropagation suffer from known issues, including slow convergence and the trap of local minimum. …”
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    Thesis
  15. 15

    A proposed approach for diabetes diagnosis using neuro-fuzzy technique by Alasaady, Maher Talal, Mohd Aris, Teh Noranis, Mohd Sharef, Nurfadhlina, Hamdan, Hazlina

    Published 2022
    “…In the classification stage, ANFIS classifiers were trained using the hybrid learning algorithm of the neural network. …”
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    Article
  16. 16

    Ultrasound-based tissue characterization and classification of fatty liver disease: A screening and diagnostic paradigm by Acharya, U.R., Faust, O., Molinari, F., Sree, S.V., Junnarkar, S.P., Sudarshan, V.

    Published 2015
    “…These classification algorithms are trained using the features extracted from the patient data in order for them to learn the relationship between the features and the end-result (FLD present or absent). …”
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    Article
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    Design and performance analysis of artificial neural network for hand motion detection from EMG signals by Ibrahimy, Muhammad Ibn, Ahsan, Md. Rezwanul, Khalifa, Othman Omran

    Published 2013
    “…The conventional and most effective time and timefrequency based features are extracted and normalized. The neural network has been trained with the normalized feature set with supervised learning method. …”
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    Article
  19. 19

    CNN-LSTM: hybrid deep neural network for network intrusion detection system; a case by Halbouni, Asmaa Hani, Gunawan, Teddy Surya, Habaebi, Mohamed Hadi, Halbouni, Murad, Kartiwi, Mira, Ahmad, Robiah

    Published 2022
    “…Based on the binary and multiclass classification, the model was trained using three datasets: CIC-IDS 2017, UNSW-NB15, and WSN-DS. …”
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    Article
  20. 20

    Classification of Mental Health Level of Students Using SMOTE and Soft Voting Ensemble Classifier and the DASS-21 Profile by Muhammad Imron, Rosadi, Khoirun, Nisa, Nanik, Kholifah

    “…These findings support the use of ensemble learning and SMOTE for developing effective college student mental health screening systems, ultimately enabling timely intervention and support…”
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    Article