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  1. 1
    Study and Implementation of Data Mining in Urban Gardening

    Study and Implementation of Data Mining in Urban Gardening by Mohana, Muniandy, Lee, Eu Vern

    Published 2019
    “…Using the J48 tree algorithm implemented through WEKA API on a Java Servlet, data provided is processed to derive a health index of the plant, with the possible outcomes set to “Good,” “Okay”, or “Bad”. …”
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  2. 2
    Adoption of machine learning algorithm for analysing supporters and non supporters feedback on political posts / Ogunfolajin Maruff Tunde

    Adoption of machine learning algorithm for analysing supporters and non supporters feedback on political posts / Ogunfolajin Maruff Tunde by Ogunfolajin Maruff , Tunde

    Published 2022
    “…The method was implemented using Java and the results of the simulation were evaluated using five standard performance metrics: accuracy, AUC, precision, recall, and f-Measure. …”
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  3. 3
    The effect of adaptive parameters on the performance of back propagation

    The effect of adaptive parameters on the performance of back propagation by Abdul Hamid, Norhamreeza

    Published 2012
    “…However, this algorithm is well-known to have difficulties with local minima problem particularly caused by neuron saturation in the hidden layer. …”
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  4. 4
    Different mutation and crossover set of genetic programming in an automated machine learning

    Different mutation and crossover set of genetic programming in an automated machine learning by Masrom, S., Mohamad, M., Hatim, S.M., Baharun, N., Omar, N., Abd. Rahman, A.S.

    Published 2020
    “…As a family of evolutionary based algorithm, the effectiveness of Genetic Programming in providing the best machine learning pipelines for a given problem or dataset is substantially depending on the algorithm parameterizations including the mutation and crossover rates. …”
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  5. 5
    Different mutation and crossover set of genetic programming in an automated machine learning

    Different mutation and crossover set of genetic programming in an automated machine learning by Masrom, S., Mohamad, M., Hatim, S.M., Baharun, N., Omar, N., Abd. Rahman, A.S.

    Published 2020
    “…As a family of evolutionary based algorithm, the effectiveness of Genetic Programming in providing the best machine learning pipelines for a given problem or dataset is substantially depending on the algorithm parameterizations including the mutation and crossover rates. …”
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  6. 6
    Embedded system for indoor guidance parking with Dijkstra’s algorithm and ant colony optimization

    Embedded system for indoor guidance parking with Dijkstra’s algorithm and ant colony optimization by Mohammad Ata, Karimeh Ibrahim

    Published 2019
    “…This study proposes a car parking management system which applies Dijkstra’s algorithm, Ant Colony Optimization (ACO) and Binary Search Tree (BST) in structuring a guidance system for indoor parking. …”
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  7. 7
    Three-term backpropagation algorithm for classification problem

    Three-term backpropagation algorithm for classification problem by Saman, Fadhlina Izzah

    Published 2006
    “…This algorithm utilizes two term parameters which are Learning Rate, α and Momentum Factor,β. …”
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  8. 8
    Enhancing three variants of harmony search algorithm for continuous optimization problems

    Enhancing three variants of harmony search algorithm for continuous optimization problems by Alomoush, Alaa A., Alsewari, Abdulrahman A., Kamal Z., Zamli, Alrosan, Ayat, Alomoush, Waleed, Alissa, Khalid

    Published 2021
    “…Meta-heuristic algorithms are well-known optimization methods, for solving real-world optimization problems. …”
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  9. 9
    Autism Spectrum Disorder Classification Using Deep Learning

    Autism Spectrum Disorder Classification Using Deep Learning by Abdulrazak Yahya, Saleh, Lim Huey, Chern

    Published 2021
    “…Recently, deep learning methods have significantly sharpened the cutting edge of learning algorithms in a wide range of artificial intelligence tasks. …”
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  10. 10
    Machine learning model for performance prediction in mobile network management / Muhammad Hazim Wahid

    Machine learning model for performance prediction in mobile network management / Muhammad Hazim Wahid by Wahid, Muhammad Hazim

    Published 2022
    “…One of the major challenges when applying machine learning is to identify the best algorithm from a variety of algorithms to solve a problem. …”
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  11. 11
    Optimize and deploy machine learning algorithms on embedded devices for manufacturing applications

    Optimize and deploy machine learning algorithms on embedded devices for manufacturing applications by Teoh, Ming Xue

    Published 2025
    “…This proposal discusses the techniques of optimizing and deploying machine learning algorithms on embedded devices for manufacturing applications; We investigate problems of printed circuit board (PCB) defects and artificial intelligence in embedded system. …”
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  12. 12
    Realization of a Hybrid Locally Connected Extreme Learning Machine with DeepID for Face Verification

    Realization of a Hybrid Locally Connected Extreme Learning Machine with DeepID for Face Verification by Wong S.Y., Yap K.S., Zhai Q., Li X.

    Published 2023
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  13. 13
    Self learning neuro-fuzzy modeling using hybrid genetic probabilistic approach for engine air/fuel ratio prediction

    Self learning neuro-fuzzy modeling using hybrid genetic probabilistic approach for engine air/fuel ratio prediction by Al-Himyari, Bayadir Abbas

    Published 2017
    “…The model was compared to other learning algorithms for NFS such as Fuzzy c-means (FCM) and grid partition algorithm. …”
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  14. 14
    Unified neural network controller of series active power filter for power quality problems mitigation

    Unified neural network controller of series active power filter for power quality problems mitigation by Ghazanfarpour, Behzad

    Published 2013
    “…First, Widrow-Hoff algorithm is examined and its constant learning rate is modified by adding an adaptive learning rule to change the learning rate value. …”
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  15. 15
    Reinforcement learning based techniques in uncertain environments: problems and solutions

    Reinforcement learning based techniques in uncertain environments: problems and solutions by AlDahoul, Nouar, Htike@Muhammad Yusof, Zaw Zaw, Akmeliawati, Rini, Shafie, Amir Akramin, Khan, Sheroz

    Published 2015
    “…Reinforcement learning (RL) is a well-known class of machine learning algorithms used in planning and controlling of autonomous agents. …”
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  16. 16
    Evaluating the performance of machine learning techniques in the classification of Wisconsin Breast Cancer

    Evaluating the performance of machine learning techniques in the classification of Wisconsin Breast Cancer by Obaid, Omar Ibrahim, Mohammed, Mazin Abed, Abd Ghani, Mohd Khanapi, A. Mostafa, Salama, AL-Dhief, Fahad Taha

    Published 2018
    “…Breast cancer is a considerable problem among the women and causes death around the world. …”
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  17. 17
    Multi-label learning based on positive label correlations using predictive apriori

    Multi-label learning based on positive label correlations using predictive apriori by Al Azaidah, Raed Hasan Saleh

    Published 2019
    “…Along with that, two algorithms have been constructed by capturing the positive correlations where the first algorithm (MLR-PC) captures the positive global correlations and the second algorithm (MLCBA) proposes an adaption of AC algorithm to handle MLC based on the positive local correlations. …”
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  18. 18
    Sedentary sitting posture recognition with YOLOv3 algorithm / Muhammad Amin Mohamad Razali

    Sedentary sitting posture recognition with YOLOv3 algorithm / Muhammad Amin Mohamad Razali by Mohamad Razali, Muhammad Amin

    Published 2023
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  19. 19
    Detection of in-car-abandoned children via deep learning algorithm / Mohd Farhan Mohd Pauzi

    Detection of in-car-abandoned children via deep learning algorithm / Mohd Farhan Mohd Pauzi by Mohd Pauzi, Mohd Farhan

    Published 2022
    “…The CNN method has been used in this study to detect children because the method can automatically learn pattern features and reduce the incompleteness caused by artificial design features. …”
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  20. 20
    Extending the decomposition algorithm for support vector machines training

    Extending the decomposition algorithm for support vector machines training by Zaki, N,M., Deris, S., Chin, K.K.

    Published 2003
    “…The training of SVM is not as straightforward as it seems. Numerical problems will cause the training to give non- optimal decision boundaries. …”
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