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  1. 1
    Detection of corneal arcus using rubber sheet and machine learning methods

    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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  2. 2
    VGG16-based deep learning architectures for classification of lung sounds into normal, crackles, and wheezes using Gammatonegrams

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

    Published 2023
    “…In this study, we conducted a comparison of two versions of the VGG16-based deep learning model for breathing sound classification using Gammatonegrams as input. …”
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  3. 3
    Classification of Rheumatoid Arthritis using Machine Learning Algorithms

    Classification of Rheumatoid Arthritis using Machine Learning Algorithms by Sharon, H., Elamvazuthi, I., Lu, C.K., Parasuraman, S., Natarajan, E.

    Published 2019
    “…Furthermore, this database which consists of 8 attributes and 32 instances, are used to determine the performance in terms of accuracy for the classification of different algorithms. …”
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  4. 4
    Classification of Rheumatoid Arthritis using Machine Learning Algorithms

    Classification of Rheumatoid Arthritis using Machine Learning Algorithms by Sharon, H., Elamvazuthi, I., Lu, C.K., Parasuraman, S., Natarajan, E.

    Published 2019
    “…Furthermore, this database which consists of 8 attributes and 32 instances, are used to determine the performance in terms of accuracy for the classification of different algorithms. …”
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  5. 5
    Cardiotocogram Data Classification using Random Forest based Machine Learning Algorithm

    Cardiotocogram Data Classification using Random Forest based Machine Learning Algorithm by Molla, M. M. Imran, Jui, Julakha Jahan, Bari, Bifta Sama, Rashid, Mamunur, Hasan, Md Jahid

    Published 2020
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  6. 6
    Deep learning-based item classification for retail automation

    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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  7. 7
    Deep learning-based breast cancer detection and classification using histopathology images / Ghulam Murtaza

    Deep learning-based breast cancer detection and classification using histopathology images / Ghulam Murtaza by Ghulam , Murtaza

    Published 2021
    “…The proposed EBrC-Net model is based on deep learning (DL) based approach. …”
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  8. 8
    Deep learning-based classification of breast tumors in ultrasound images / Ayub Ahmed Omar

    Deep learning-based classification of breast tumors in ultrasound images / Ayub Ahmed Omar by Ayub Ahmed, Omar

    Published 2022
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  9. 9
    Heart sound diagnosis using nonlinear ARX model / Noraishah Shamsuddin

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

    Published 2011
    “…This thesis presents a research work on a diagnosis system for heart sound based on nonlinear ARX (NARX) model. The system uses neural network for model estimation and classification of several heart diseases. …”
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  10. 10
    Converged Classification Network For Matching Cost Computation

    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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  11. 11
    Handgrip strength evaluation using neuro fuzzy approach

    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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  12. 12
    Image-based air quality estimation using convolutional neural network optimized by genetic algorithms: A multi-dataset approach

    Image-based air quality estimation using convolutional neural network optimized by genetic algorithms: A multi-dataset approach by Khan, Arshad Ali, Mazlina, Abdul Majid, Dandoush, Abdulhalim

    Published 2025
    “…These improvements underscore the effectiveness of Genetic Algorithms in optimizing the model.…”
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  13. 13
    Semi-Supervised Learning for limited medical data using Generative Adversarial Network and Transfer Learning

    Semi-Supervised Learning for limited medical data using Generative Adversarial Network and Transfer Learning by Amin, I., Hassan, S., Jaafar, J.

    Published 2020
    “…This paper presents a semi-supervised learning based model that combines the capabilities of generative adversarial network (GAN) and transfer learning. …”
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  14. 14
    Analysis of hyperspectral reflectance for disease classification of soybean frogeye leaf spot using Knime analytics

    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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  15. 15
    Data‑driven audiogram classifer using data normalization and multi‑stage feature selection

    Data‑driven audiogram classifer using data normalization and multi‑stage feature selection by Abeer Elkhouly, Allan MelvinAndrew, HaslizaA Rahim, NidhalAbdulaziz, Mohd FareqAbd Malek, Shafquzzaman Siddique

    Published 2023
    “…In this work, a machine learning solution is introduced to classify the audiograms according to the shapes based on unsupervised spectral clustering. The features used to build the ML model are peculiar and describe the audiograms better. …”
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  16. 16
    Classification of Mental Health Level of Students Using SMOTE and Soft Voting Ensemble Classifier and the DASS-21 Profile

    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

    “…Feature importance-based feature selection using Random Forest is utilized to eliminate less relevant attributes. …”
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  17. 17
    Prediction of breast cancer diagnosis using machine learning in Malaysian women

    Prediction of breast cancer diagnosis using machine learning in Malaysian women by Mokhtar, Tengku Muhammad Hanis Tengku

    Published 2024
    “…This project found that neural network, deep learning, tree-based models, and SVM performed well on mammographic data for breast cancer detection. …”
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  18. 18
    The effect of pre-processing techniques and optimal parameters on BPNN for data classification

    The effect of pre-processing techniques and optimal parameters on BPNN for data classification by HUSSEIN, AMEER SALEH

    Published 2015
    “…In this research, a performance analysis based on different activation functions; gradient descent and gradient descent with momentum, for training the BP algorithm with pre-processing techniques was executed. …”
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  19. 19
    Classification of SNPs for obesity analysis using FARNeM modelling

    Classification of SNPs for obesity analysis using FARNeM modelling by Ong, Phaik Ling, Choo, Yun Huoy, Emran, Nurul Akmar

    Published 2013
    “…In this paper, we investigated feature selection process in obesity related SPNs analysis using Forward attribute reduction based on neighbourhood rough set model (FARNeM). …”
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  20. 20
    Enhanced emotion recognition in videos: a convolutional neural network strategy for human facial expression detection and classification

    Enhanced emotion recognition in videos: a convolutional neural network strategy for human facial expression detection and classification by Ashraf, Arselan, Gunawan, Teddy Surya, Arifin, Fatchul, Kartiwi, Mira, Sophian, Ali, Habaebi, Mohamed Hadi

    Published 2023
    “…To address these issues, we propose a comprehensive CNN-based model for real-time detection and classification of five primary emotions: anger, happiness, neutrality, sadness, and surprise. …”
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