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  1. 1
    Adaptive Linear System Identification over Simulated Wireless Environment

    Adaptive Linear System Identification over Simulated Wireless Environment by Elamin, Musab Jabralla Omer Elamin

    Published 2009
    “…The work looks thoroughly on three forms of instantaneous learning algorithms which are: first order algorithms (e.g. least mean square (LMS)), second order algorithms (e.g. …”
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  2. 2
    Developing framework for natphoric computer-aided web-based kansei engineering / Mohammad Bakri Che Haron

    Developing framework for natphoric computer-aided web-based kansei engineering / Mohammad Bakri Che Haron by Che Haron, Mohammad Bakri

    Published 2013
    “…The Natphoric algorithm learns the process done by training with sets of training data from previous KE research works. …”
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  3. 3
    Comparison between Lamarckian Evolution and Baldwin Evolution of neural network

    Comparison between Lamarckian Evolution and Baldwin Evolution of neural network by Taha, Imad, Inazy, Qabas

    Published 2006
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  4. 4
    K-means clustering and visualizing of significant words prototype for crowdsourced relationship comments / Muhammad Syafiq Mastor

    K-means clustering and visualizing of significant words prototype for crowdsourced relationship comments / Muhammad Syafiq Mastor by Mastor, Muhammad Syafiq

    Published 2015
    “…Conflicts occur due to lack of understanding between partners. The most common conflicts are the misunderstanding women‟s feeling from their partners. …”
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  5. 5
    End-to-end DVB-S2X system design with deep learning-based channel estimation over satellite fading channels

    End-to-end DVB-S2X system design with deep learning-based channel estimation over satellite fading channels by Mfarej, Sumaya Dhari Awad

    Published 2021
    “…In the fourth part a deep learning (DL) algorithm of channel estimation for two fad�ing channel models, Tropical and Temperate in the satellite communication system is presented. …”
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  6. 6
    Artificial neural network algorithm for predicting the surface roughness in end milling of Inconel 718 alloy

    Artificial neural network algorithm for predicting the surface roughness in end milling of Inconel 718 alloy by Hossain, Mohammad Ishtiyaq, Amin, A. K. M. Nurul, Patwari, Muhammed Anayet Ullah

    Published 2011
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  7. 7
    Regenerative braking strategy for electric vehicles using improved adaptive genetic algorithm

    Regenerative braking strategy for electric vehicles using improved adaptive genetic algorithm by Taleghani, Hussein

    Published 2017
    “…Firstly, it distributes the brake forces between front and rear axles of the vehicle. …”
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  8. 8
    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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  9. 9
    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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  10. 10
    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. …”
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  11. 11
    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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  12. 12
    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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  13. 13
    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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  14. 14
    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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  15. 15
    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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  16. 16
    Proposed fatigue index for the objective detection of muscle fatigue using surface electromyography and a double-step binary classifier

    Proposed fatigue index for the objective detection of muscle fatigue using surface electromyography and a double-step binary classifier by Qassim, Hassan M., Hasan, Wan Zuha Wan, Ramli, Hafiz R., Harith, Hazreen Haizi, Inche Mat, Liyana Najwa, Ismail, Luthffi Idzhar

    Published 2022
    “…The results show that the proposed algorithm had an accuracy of 94.66% in distinguishing between conditions of muscle fatigue and non-fatigue.…”
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  17. 17
    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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  18. 18
    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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  19. 19
    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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  20. 20
    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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