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  1. 1
    Cognitive knowledge-based model for adaptive feedback: A case in physics / Andrew Thomas Bimba

    Cognitive knowledge-based model for adaptive feedback: A case in physics / Andrew Thomas Bimba by Andrew Thomas, Bimba

    Published 2019
    “…Tailoring feedback according to student’s characteristics and other external parameters is a promising way to implement adaptation in computer-based learning environment. There is an increase in the implementation of adaptive feedback models, which focus on the relationship between adaptive feedback and learning gains. …”
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  2. 2
    A comparative study between deep learning algorithm and bayesian network on Advanced Persistent Threat (APT) attack detection

    A comparative study between deep learning algorithm and bayesian network on Advanced Persistent Threat (APT) attack detection by Ooi, Hui Ni, Ab Rahman, Nurul Hidayah

    Published 2021
    “…Besides, Multilayer Perceptron algorithm has high true positive rate (TPR) in the detection of APT attack compared to Naïve Bayes algorithm. …”
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  3. 3
    A comparative study between deep learning algorithm and bayesian network on Advanced Persistent Threat (APT) attack detection

    A comparative study between deep learning algorithm and bayesian network on Advanced Persistent Threat (APT) attack detection by Ooi, Hui Ni, Ab Rahman, Nurul Hidayah

    Published 2021
    “…Besides, Multilayer Perceptron algorithm has high true positive rate (TPR) in the detection of APT attack compared to Naïve Bayes algorithm. …”
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  4. 4
    Meta-heuristics and deep learning for energy applications: Review and open research challenges (2018?2023)

    Meta-heuristics and deep learning for energy applications: Review and open research challenges (2018?2023) by Hosseini E., Al-Ghaili A.M., Kadir D.H., Gunasekaran S.S., Ahmed A.N., Jamil N., Deveci M., Razali R.A.

    Published 2025
    “…The synergy between deep learning and meta-heuristic algorithms presents a promising avenue for tackling the complexities of energy-related modeling and forecasting tasks. …”
    Review
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  5. 5
    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
    “…The objective of this research is to study the performance/capability of the integration between both unsupervised and supervised learning. …”
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  6. 6
    Automated bilateral negotiation with incomplete information in the e-marketplace.

    Automated bilateral negotiation with incomplete information in the e-marketplace. by Jazayeriy, Hamid

    Published 2011
    “…In one-side incomplete information problem, comparison between MGT algorithm and smart random trade-offs (SRT) algorithm indicates that MGT outperforms SRT. …”
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  7. 7
    A New Unsupervised Validation Index Model Suitable for Energy-Efficient Clustering Techniques in VANET

    A New Unsupervised Validation Index Model Suitable for Energy-Efficient Clustering Techniques in VANET by Abdulrazzak H.N., Hock G.C., Mohamed Radzi N.A., Tan N.M.L.

    Published 2024
    “…Clustering evaluation techniques are important to check the clustering algorithm quality. High cluster similarity help to reduce the distance between a node to node within the cluster, also good separation was more important to avoid overlapping clusters. …”
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  8. 8
    Design and implemtation of a real-time adaptive learning algorithm controller for a 3-DOF parallel manipulator / Mustafa Jabbar Hayyawi

    Design and implemtation of a real-time adaptive learning algorithm controller for a 3-DOF parallel manipulator / Mustafa Jabbar Hayyawi by Jabbar Hayyawi, Mustafa

    Published 2016
    “…An electronic board, transistor relay driver circuit, is designed for the purpose of establishing communication interface between the computer, adaptive learning algorithm and the actuator mechanism. …”
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  9. 9
    Detection and classification of conflict flows in SDN using machine learning algorithms

    Detection and classification of conflict flows in SDN using machine learning algorithms by Mutaz Hamed Hussien Khairi, Sharifah Hafizah Syed Ariffin, Nurul Mu'azzah Abdul Latiff, Kamaludin Mohamad Yusof, Mohamed Khalafalla Hassan, Fahad Taha Al-Dhief, Mosab Hamda, Suleman Khan, Muzaffar Hamzah

    Published 2021
    “…In addition, the proposed EFDT algorithm achieves 95.73% accuracy on the task of classification between conflict flow types. …”
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  10. 10
    Comparison between pixel-based and object based classifications using radar satellite image in extracting massive flood extent at northern region of Peninsular Malaysia

    Comparison between pixel-based and object based classifications using radar satellite image in extracting massive flood extent at northern region of Peninsular Malaysia by Abd Manaf, Syaifulnizam, Mustapha, Norwati, Mohd Shafri, Helmi Zulhaidi, Sulaiman, Md. Nasir, Husin, Nor Azura

    Published 2015
    “…In object-based approach, there were three simple machine learning algorithms such as PP, MD, MH together with NN performed with high accuracy while in pixel based approach, NN was the highest accuracy of all machine learning algorithms. …”
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  11. 11
    Comparison between pixel-based and object-based classifications using radar satellite image in extracting massive flood extent at northern region of Peninsular Malaysia

    Comparison between pixel-based and object-based classifications using radar satellite image in extracting massive flood extent at northern region of Peninsular Malaysia by Abd Manaf, Syaifulnizam, Mustapha, Norwati, Mohd Shafri, Helmi Zulhaidi, Sulaiman, Md Nasir, Husin, Nor Azura

    Published 2015
    “…In object-based approach, there were three simple machine learning algorithms such as PP, MD, MH together with NN performed with high accuracy while in pixel based approach, NN was the highest accuracy of all machine learning algorithms. …”
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  12. 12
    Operating a reservoir system based on the shark machine learning algorithm

    Operating a reservoir system based on the shark machine learning algorithm by Allawi M.F., Jaafar O., Mohamad Hamzah F., Ehteram M., Hossain M.S., El-Shafie A.

    Published 2023
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  13. 13
    Waste management using machine learning and deep learning algorithms

    Waste management using machine learning and deep learning algorithms by Sami, Khan Nasik, Amin, Zian Md Afique, Hassan, Raini

    Published 2020
    “…For our research we did the comparisons between three Machine Learning algorithms, namely Support Vector Machine (SVM), Random Forest, and Decision Tree, and one Deep Learning algorithm called Convolutional Neural Network (CNN), to find the optimal algorithm that best fits for the waste classification solution. …”
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  14. 14
    Prediction of earnings manipulation on Malaysian listed firms: A comparison between linear and tree-based machine learning

    Prediction of earnings manipulation on Malaysian listed firms: A comparison between linear and tree-based machine learning by Rahman, R.A., Masrom, S., Zakaria, N.B., Nurdin, E., Abd Rahman, A.S.

    Published 2021
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  15. 15
    Machine learning modeling for radiofrequency electromagnetic fields (RF-EMF) signals from mmWave 5G signals

    Machine learning modeling for radiofrequency electromagnetic fields (RF-EMF) signals from mmWave 5G signals by Al-Jumaily, Abdulmajeed, Sali, Aduwati, Riyadh, Mohammed, Wali, Sangin Qahtan, Li, Lu, Osman, Anwar Faizd

    Published 2023
    “…However, in 6 minutes and 30 minutes, it can observe more accurate results since the differences between values reach 0.1 in these situations. Additionally, the ICNIRP standard was used and compared with the results and validation values of the algorithms.…”
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  16. 16
    Development of Machine Learning Algorithm for Acquiring Machining Data in Turning Process

    Development of Machine Learning Algorithm for Acquiring Machining Data in Turning Process by Ali Al-Assadi, Hayder M. A.

    Published 2004
    “…Artificial Neural Network (ANN) was selected from Machine Learning Algorithms to be the learning algorithm. …”
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  17. 17
    Extreme learning machine model for state-of-charge estimation of lithium-ion battery using gravitational search algorithm

    Extreme learning machine model for state-of-charge estimation of lithium-ion battery using gravitational search algorithm by Hossain Lipu M.S., Hannan M.A., Hussain A., Saad M.H., Ayob A., Uddin M.N.

    Published 2023
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  18. 18
    Comparison of expectation maximization and K-means clustering algorithms with ensemble classifier model

    Comparison of expectation maximization and K-means clustering algorithms with ensemble classifier model by Sulaiman, Md. Nasir, Mohamed, Raihani, Mustapha, Norwati, Zainudin, Muhammad Noorazlan Shah

    Published 2018
    “…In this article, we present the exploration on the combination of the clustering based algorithm with an ensemble classification learning. …”
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  19. 19
    Micro-Crack Detection Of Solar Cells Featuring Adaptive Anisotropic Diffusion Filter And Semi-Supervised Support Vector Learning

    Micro-Crack Detection Of Solar Cells Featuring Adaptive Anisotropic Diffusion Filter And Semi-Supervised Support Vector Learning by Majid, Said Amirul Anwar B Ab Hamid @ Ab

    Published 2014
    “…The outcomes of this algorithm have demonstrated a highly accurate segmentation results compared to other standard methods. …”
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  20. 20
    Enhancing the QoS performance for mobile station over LTE and WiMAX networks / Mhd Nour Hindia

    Enhancing the QoS performance for mobile station over LTE and WiMAX networks / Mhd Nour Hindia by Hindia, Mhd Nour

    Published 2015
    “…Then, a multi criteria with two-threshold algorithm is proposed to prioritize the selection between the LTE and WiMAX as target technologies. …”
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