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  1. 1
    A simplified PID-like ANFIS controller trained by genetic algorithm to control nonlinear systems

    A simplified PID-like ANFIS controller trained by genetic algorithm to control nonlinear systems by Lutfy, Omar Farouq, Mohd Noor, Samsul Bahari, Marhaban, Mohammad Hamiruce, Abbas, Kassim A.

    Published 2010
    “…Moreover, the GA was used to find the optimal settings for the input and output scaling factors for this controller, instead of the widely used trial and error method. …”
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  2. 2
    Schelkunoff array synthesis methods using adaptive-iterative algorithm

    Schelkunoff array synthesis methods using adaptive-iterative algorithm by Abdul Latiff, Nurul Mu'azzah

    Published 2003
    “…Basically, this algorithm is a combination of iterative algorithm with adaptive algorithm. …”
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  3. 3
    An Integrated Approach to the Reduction of Reactive Power Losses in Radial Distribution Network

    An Integrated Approach to the Reduction of Reactive Power Losses in Radial Distribution Network by Than, Khong Hon

    Published 2004
    “…The objective of this project is to compensate reactive power with appropriate methods. There are basically two commonly usedmethods: network reconfiguration and capacitor insertion. …”
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    Final Year Project
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  4. 4
    Development of a new nonlinear filtering algorithm for impulse noise reduction

    Development of a new nonlinear filtering algorithm for impulse noise reduction by Ahmad, Mohd Ashraf

    Published 2020
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    Research Report
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  5. 5
    Finger vein verification system using repeated line tracking and dimensionality reduction using PCA algorithms with SURF matching

    Finger vein verification system using repeated line tracking and dimensionality reduction using PCA algorithms with SURF matching by Ei Wei., Ting, M. Z., Ibrahim, D.J., Mulvaney

    Published 2017
    “…The feature vector of the vein pattern was then dimensionality reduction by Principal Component Analysis (PCA). The Speeded-Up Robust Features (SURF) Algorithms is used to determine the interest points. …”
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  6. 6
    Enhancement processing time and accuracy training via significant parameters in the batch BP algorithm

    Enhancement processing time and accuracy training via significant parameters in the batch BP algorithm by Fatma Susilawati, Mohamad, Mumtazimah, Mohamad, Sarhan, AlDuais

    Published 2020
    “…We created the dynamic learning rate and dynamic momentum factor for increasing the efficiency of the algorithm. …”
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  7. 7
    A Feature Ranking Algorithm in Pragmatic Quality Factor Model for Software Quality Assessment

    A Feature Ranking Algorithm in Pragmatic Quality Factor Model for Software Quality Assessment by Ruzita, Ahmad

    Published 2013
    “…The assessment of quality attributes has been improved using FRA algorithm enriched with a formula to calculate the priority of attributes and followed by learning adaptation through Java Library for Multi Label Learning (MULAN) application. …”
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  8. 8
    Effect of model simplification through manual reduction in number of surfaces on room acoustics simulation

    Effect of model simplification through manual reduction in number of surfaces on room acoustics simulation by Abd Jalil, Nurul Amira, Che Din, Nazli, Keumala, Nila, Abdul-Razak, Asrul Sani

    Published 2019
    “…Various simplification algorithms were previously suggested. However, they are highly demanding methods, which are more suitable for large complicated spaces. …”
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  9. 9
    Modeling time series data using Genetic Algorithm based on Backpropagation Neural network

    Modeling time series data using Genetic Algorithm based on Backpropagation Neural network by Haviluddin

    Published 2018
    “…This study showed the task of optimizing the topology structure and the parameter values (e.g., weights) used in the BPNN learning algorithm by using the GA. …”
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  10. 10
    Prediction models of heritage building based on machine learning / Nur Shahirah Ja'afar

    Prediction models of heritage building based on machine learning / Nur Shahirah Ja'afar by Ja'afar, Nur Shahirah

    Published 2021
    “…These algorithms were developed by using prewar shophouses dataset from 2004 until 2018 based on factors of heritage properties. …”
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  11. 11
    Learning Algorithm effect on Multilayer Feed Forward Artificial Neural Network performance in image coding

    Learning Algorithm effect on Multilayer Feed Forward Artificial Neural Network performance in image coding by Mahmoud, Omer, Anwar, Farhat, Salami, Momoh Jimoh Emiyoka

    Published 2007
    “…One of the essential factors that affect the performance of Artificial Neural Networks is the learning algorithm. …”
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  12. 12
    Optimization of chest X-ray exposure factors using machine learning algorithm

    Optimization of chest X-ray exposure factors using machine learning algorithm by Hamd, Zuhal Y., Alrebdi, H.I., Osman, Eyas G., Awwad, Areej, Alnawwaf, Layan, Nashri, Nawal, Alfnekh, Rema, Khandaker, Mayeen Uddin *

    Published 2023
    “…In this study, the chest X-ray exposure factors for 178 patients with different body mass index (BMI) values have been analyzed using the Python Machine Learning algorithm. …”
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  13. 13
    A bayesian network approach to identify factors affecting learning of Additional Mathematics

    A bayesian network approach to identify factors affecting learning of Additional Mathematics by Ong, Hong Choon, Kumarenthiran A/L Chandrasekaran

    Published 2015
    “…Constraint-based algorithms and score-based algorithms are used to generate the networks into several categories to compare and identify the strong relationships among the factors that affect the students’ learning of the subject. …”
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  14. 14
    Hexagon pattern particle swarm optimization based block matching algorithm for motion estimation / Siti Eshah Che Osman

    Hexagon pattern particle swarm optimization based block matching algorithm for motion estimation / Siti Eshah Che Osman by Che Osman, Siti Eshah

    Published 2019
    “…The final results have proved that HPSO algorithm could achieve 5% - 34% of computation cost reduction with satisfying degradation value of image quality. …”
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  15. 15
    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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  16. 16
    Reverse migration prediction model based on machine learning / Azreen Anuar

    Reverse migration prediction model based on machine learning / Azreen Anuar by Anuar, Azreen

    Published 2024
    “…A significant way to minimize the errors is by using a machine learning approach that can predict reverse migration intelligently depending on the tested dataset. …”
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  17. 17
    Twofold Integer Programming Model for Improving Rough Set Classification Accuracy in Data Mining.

    Twofold Integer Programming Model for Improving Rough Set Classification Accuracy in Data Mining. by Saeed, Walid

    Published 2005
    “…The accuracy for rules and classification resulted from the TIP method are compared with other methods such as Standard Integer Programming (SIP) and Decision Related Integer Programming (DRIP) from Rough Set, Genetic Algorithm (GA), Johnson reducer, HoltelR method, Multiple Regression (MR), Neural Network (NN), Induction of Decision Tree Algorithm (ID3) and Base Learning Algorithm (C4.5); all other classifiers that are mostly used in the classification tasks. …”
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  18. 18
    Genetic algorithm optimization of product design for environmental impact reduction / Julirose Gonzales

    Genetic algorithm optimization of product design for environmental impact reduction / Julirose Gonzales by Julirose , Gonzales

    Published 2018
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  19. 19
    Electromagnetic fields policy implications for risk assessment

    Electromagnetic fields policy implications for risk assessment by Farag A., Hussain H., Said I., Ahmad A.S.

    Published 2023
    Conference paper
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  20. 20
    A comparative study of deep learning algorithms in univariate and multivariate forecasting of the Malaysian stock market

    A comparative study of deep learning algorithms in univariate and multivariate forecasting of the Malaysian stock market by Mohd. Ridzuan Ab. Khalil, Azuraliza Abu Bakar

    Published 2023
    “…This study aims to develop a univariate and multivariate stock market forecasting model using three deep learning algorithms and compare the performance of those models. …”
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