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  1. 1
    Enhancing teaching and learning through data-driven optimization of servicing code demand and lecturer allocation using WEKA analysis

    Enhancing teaching and learning through data-driven optimization of servicing code demand and lecturer allocation using WEKA analysis by Rochin Demong, Nur Atiqah, Mohamed Razali, Murni Zarina, Kamaruddin, Juliana Noor, Shamsuddin, Sazwan, Awang, Nor Ain, Kamarudin, Norjuliatie, Wan Othman, Noor Faradilla

    Published 2025
    “…Complementary K-Means clustering grouped the data into two major clusters, indicating that a clear differentiation between economic-based and entrepreneurship-based courses in terms of student enrolment volume and approval distribution. …”
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  2. 2
    Mapping the distribution of oil palm using Landsat 8 data by comparing machine learning and non-machine learning algorithms

    Mapping the distribution of oil palm using Landsat 8 data by comparing machine learning and non-machine learning algorithms by Shaharum, Nur Shafira Nisa, Mohd Shafri, Helmi Zulhaidi, Wan Ab. Karim Ghani, Wan Azlina, Samsatli, Sheila, Yusuf, Badronnisa

    Published 2019
    “…Hence, the mapping of oil palm distributions via machine learning algorithm was better than that via non-machine learning algorithm.…”
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  3. 3
    Comparative performance of deep learning and machine learning algorithms on imbalanced handwritten data

    Comparative performance of deep learning and machine learning algorithms on imbalanced handwritten data by Amri, A’inur A’fifah, Ismail, Amelia Ritahani, Zarir, Abdullah Ahmad

    Published 2018
    “…The experiment shows that although the algorithm is stable and suitable for multiple domains, the imbalanced data distribution still manages to affect the outcome of the conventional machine learning algorithms.…”
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  4. 4
    State-Aware re-configuration model for multi-radio wireless Mesh Networks

    State-Aware re-configuration model for multi-radio wireless Mesh Networks by Zakaria, Omar, Hassan Abdalla Hashim, Aisha, Hassan, Wan Haslina, Khalifa, Othman Omran, Azram, Mohammad, Goudarzi, Shidrokh, Jivanadham, Lalitha Bhavani, Zareei, Mahdi

    Published 2017
    “…The proposed algorithm re-assigns channels to radios and re-configures flows’ routes with aim of achieving a tradeoff between maximizing the network throughput and minimizing the re-configuration overhead. …”
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  5. 5
    Classification of diabetic patients with imbalanced class distribution by using a Cost-Sensitive forest algorithm / Ummi Asyiqin Che Muhammad and Muhammad Hasbullah Mohd Razali

    Classification of diabetic patients with imbalanced class distribution by using a Cost-Sensitive forest algorithm / Ummi Asyiqin Che Muhammad and Muhammad Hasbullah Mohd Razali by Che Muhammad, Ummi Asyiqin, Mohd Razali, Muhammad Hasbullah

    Published 2023
    “…Although many machine learning algorithms have been developed by researchers, the class imbalanced distribution still makes it challenging for classifiers to properly learn and differentiate between the minority and majority classes. …”
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  6. 6
    Machine learning in botda fibre sensor for distributed temperature measurement

    Machine learning in botda fibre sensor for distributed temperature measurement by Nur Dalilla binti Nordin

    Published 2023
    “…An alternative method is proposed, utilizing machine learning algorithms. Therefore, this thesis explores the comparative analysis for BOTDA data processing using the six most suited machine learning algorithms. …”
    text::Thesis
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  7. 7
    Removal of heavy metals from water by functionalized carbon nanotubes with deep eutectic solvents: An artificial neural network approach / Seef Saadi Fiyadh

    Removal of heavy metals from water by functionalized carbon nanotubes with deep eutectic solvents: An artificial neural network approach / Seef Saadi Fiyadh by Seef Saadi , Fiyadh

    Published 2019
    “…The best result achieved for Pb2+ removal using ANFIS algorithm is with RE 7.078%. For As3+ removal using different adsorbents, two algorithms were applied for the modelling, the feed-forward back-propagation maximum RE achieved is 5.97% while, the NARX algorithm achieved better accuracy with maximum RE of 5.79%. …”
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  8. 8
    Inference Algorithms in Latent Dirichlet Allocation for Semantic Classification

    Inference Algorithms in Latent Dirichlet Allocation for Semantic Classification by Mohammad Zubir, W.M.A., Abdul Aziz, I., Jaafar, J., Hasan, M.H.

    Published 2018
    “…However, the problem of learning or inferencing the posterior distribution of the algorithm is trivial. …”
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  9. 9
    Inference Algorithms in Latent Dirichlet Allocation for Semantic Classification

    Inference Algorithms in Latent Dirichlet Allocation for Semantic Classification by Mohammad Zubir, W.M.A., Abdul Aziz, I., Jaafar, J., Hasan, M.H.

    Published 2018
    “…However, the problem of learning or inferencing the posterior distribution of the algorithm is trivial. …”
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  10. 10
    Enhanced Q-Learning algorithm for potential actions selection in automated graphical user interface testing

    Enhanced Q-Learning algorithm for potential actions selection in automated graphical user interface testing by Goh, Kwang Yi

    Published 2023
    “…To overcome this limitation, the Q-Learning algorithm was proposed by several researchers to minimise randomness. …”
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  11. 11
    Supervised deep learning algorithms for process fault detection and diagnosis under different temporal subsequence length of process data

    Supervised deep learning algorithms for process fault detection and diagnosis under different temporal subsequence length of process data by Terence Chia Yi Kai, Agus Saptoro, Zulfan Adi Putra, King Hann Lim, Wan Sieng Yeo, Jaka Sunarso

    Published 2025
    “…Deep learning algorithms were widely used among all the data-driven algorithms. …”
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  12. 12
    Minimizing Classification Errors in Imbalanced Dataset Using Means of Sampling

    Minimizing Classification Errors in Imbalanced Dataset Using Means of Sampling by Khan I., Ahmad A.R., Jabeur N., Mahdi M.N.

    Published 2023
    Conference Paper
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  13. 13
    Topological Clustering via Adaptive Resonance Theory With Information Theoretic Learning

    Topological Clustering via Adaptive Resonance Theory With Information Theoretic Learning by Masuyama, Naoki, Loo, Chu Kiong, Ishibuchi, Hisao, Kubota, Naoyuki, Nojima, Yusuke, Liu, Yiping

    Published 2019
    “…Moreover, generated topological networks cannot represent the distribution of data. In contrast, the proposed algorithm realizes a stable computation and reduces the number of parameters compared to existing algorithms. …”
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  14. 14
    Segmentation of MRI brain images using statistical approaches

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

    Published 2011
    “…Moreover, three improvements of EM for brain MRI segmentation are proposed, which incorporate neighbourhood information in a new manner in the clustering process. In addition, two algorithms for the post-processing of clustering results using user-interaction and the re-evaluation of boundary data in each cluster are presented. …”
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  15. 15
    Distributed Online Averaged One Dependence Estimator (DOAODE) Algorithm for Multi-class Classification of Network Anomaly Detection System

    Distributed Online Averaged One Dependence Estimator (DOAODE) Algorithm for Multi-class Classification of Network Anomaly Detection System by R.Badlishah, Ahmad, Nawir, M., Amir, A, Yaakob, N, Mat Safar, A, Mohd Warip, M.N, Zunaidi, I

    Published 2019
    “…Therefore, this paper aims to develop an effective and efficient network anomaly detection system by using distributed online averaged one dependence estimator (DOAODE) classification algorithm for multi-class network data to overcome these issues. …”
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  16. 16
    Distributed Online Averaged One Dependence Estimator (DOAODE) Algorithm for Multi-class Classification of Network Anomaly Detection System

    Distributed Online Averaged One Dependence Estimator (DOAODE) Algorithm for Multi-class Classification of Network Anomaly Detection System by Badlishah, Ahmad, Nawir, M., Amir, A, Yaakob, N, Mat Safar, A, Mohd Warip, M.N, Zunaidi, I

    Published 2019
    “…Therefore, this paper aims to develop an effective and efficient network anomaly detection system by using distributed online averaged one dependence estimator (DOAODE) classification algorithm for multi-class network data to overcome these issues. …”
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  17. 17
    A Divide-and-Distribute Approach to Single-Cycle Learning HGN Network for Pattern Recognition

    A Divide-and-Distribute Approach to Single-Cycle Learning HGN Network for Pattern Recognition by Muhamad Amin , Anang Hudaya, Khan, Asad I.

    Published 2010
    “…Distributed Hierarchical Graph Neuron (DHGN) is a single-cycle learning distributed pattern recognition algorithm, which reduces the computational complexity of existing pattern recognition algorithms by distributing the recognition process into smaller clusters. …”
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  18. 18
    Technical job distribution at BSD SHARP service center using combination of naïve Bayes and K-Nearest neighbour

    Technical job distribution at BSD SHARP service center using combination of naïve Bayes and K-Nearest neighbour by Pebrianti, Dwi, Ariawan, Angga, Bayuaji, Luhur, Mahdiana, Deni, ,, Rusdah

    Published 2022
    “…Meanwhile, k-NN algorithm is used to classify the experimental data. …”
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    Proceeding Paper
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  19. 19
    Predicting Customer Buying Decisions for Online Shopping with Unbalanced Data Set

    Predicting Customer Buying Decisions for Online Shopping with Unbalanced Data Set by Yap, Chau Tean

    Published 2022
    “…Weka, a data mining tool, provides the facility to classify the data set with different machine learning algorithms. …”
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  20. 20
    Efficient Machine Learning Model for DDoS Detection System Based on Dimensionality Reduction

    Efficient Machine Learning Model for DDoS Detection System Based on Dimensionality Reduction by Dheyab, Saad Ahmed, Mohammed Abdulameer, Shaymaa, Mostafa, Salama A

    Published 2022
    “…This model relies mainly on dimensionality reduction and machine learning algorithms. The principal component analysis (PCA) and the linear discriminant analysis (LDA) techniques perform the dimensionality reduction in individual and hybrid modes to process and improve the data. …”
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