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  1. 1
    Functional link neural network with modified bee-firefly learning algorithm for classification task

    Functional link neural network with modified bee-firefly learning algorithm for classification task by Mohmad Hassim, Yana Mazwin

    Published 2016
    “…The single layer property of FLNN also make the learning algorithm used less complicated compared to MLP network. …”
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  2. 2
    An approach to improve functional link neural network training using modified artificial bee colony for classification task

    An approach to improve functional link neural network training using modified artificial bee colony for classification task by Yana Mazwin Mohmad Hassim, Rozaida Ghazali

    Published 2013
    “…The standard method for tuning the weight in FLNN is using a Backpropagation (BP) learning algorithm. …”
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  3. 3
    An approach to improve functional link neural network training using modified artificial bee colony for classification task

    An approach to improve functional link neural network training using modified artificial bee colony for classification task by Mohmad Hassim, Yana Mazwin, Ghazali, Rozaida

    Published 2012
    “…The standard method for tuning the weight in FLNN is using a Backpropagation (BP) learning algorithm. …”
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  4. 4
    An approach to improve functional link neural network training using modified artificial bee colony for classification task

    An approach to improve functional link neural network training using modified artificial bee colony for classification task by Mohmad Hassim, Yana Mazwin, Ghazali, Rozaida

    Published 2012
    “…The standard method for tuning the weight in FLNN is using a Backpropagation (BP) learning algorithm. …”
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  5. 5
    Modified anfis architecture with less computational complexities for classification problems

    Modified anfis architecture with less computational complexities for classification problems by Talpur, Noureen

    Published 2018
    “…Furthermore, researchers have mainly used metaheuristic algorithms to avoid the problem of local minima in standard learning method. …”
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  6. 6
    Binary whale optimization algorithm with logarithmic decreasing time-varying modified sigmoid transfer function for descriptor selection problem

    Binary whale optimization algorithm with logarithmic decreasing time-varying modified sigmoid transfer function for descriptor selection problem by Yusof, Norfadzlia Mohd, Muda, Azah Kamilah, Pratama, Satrya Fajri, Carbo-Dorca, Ramon, Abraham, Ajith

    Published 2023
    “…This work introduced a new Binary Whale Optimization Algorithm, which utilized a novel time-varying modified Sigmoid transfer function with a modified logarithmic decreasing time-varying update strategy to improve the balancing of exploration and exploitation in WOA. …”
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    Conference or Workshop Item
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  7. 7
    A hybrid approach for artificial immune recognition system / Mahmoud Reza Saybani

    A hybrid approach for artificial immune recognition system / Mahmoud Reza Saybani by Mahmoud Reza, Saybani

    Published 2016
    “…The components of the AIRS2 algorithm that pose problems will be modified. This thesis proposes three new hybrid algorithms: The FRA-AIRS2 algorithm uses fuzzy logic to improve data reduction capability of AIRS2 and to solve the linearity problem associated with resource allocation of AIRS. …”
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  8. 8
    Application Of Neural Network In Malaria Parasites Classification

    Application Of Neural Network In Malaria Parasites Classification by Lim, Chia Li

    Published 2006
    “…Multilayer Perceptron (MLP) network and Radial Basis Function (RBF) network will be developed using MATLAB in which MLP network is trained with Back Propagation, Bayesian Rule and Levenberg-Marquardt learning algorithm and RBF network is trained with k-means clustering algorithm. …”
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  9. 9
    A framework of modified adaptive neuro-fuzzy inference engine

    A framework of modified adaptive neuro-fuzzy inference engine by Hossen, Md. Jakir

    Published 2012
    “…The Takagi-Sugeno-Kang (TSK) type fuzzy inference system was chosen and constructed by an automatic generation of clusters as well as membership functions and minimal rules through the use of hybrid fuzzy clustering and the modified apriori algorithms respectively. …”
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  10. 10
    A Hybrid Rough Sets K-Means Vector Quantization Model For Neural Networks Based Arabic Speech Recognition

    A Hybrid Rough Sets K-Means Vector Quantization Model For Neural Networks Based Arabic Speech Recognition by Babiker, Elsadig Ahmed Mohamed

    Published 2002
    “…Classification rules were generated from training feature vectors set, and a modified form of the standard voter classification algorithm, that use the rough sets generated rules, was applied. …”
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  11. 11
    Optimal placement and sizing of FACTS devices for optimal power flow using metaheuristic optimizers

    Optimal placement and sizing of FACTS devices for optimal power flow using metaheuristic optimizers by Mohd Herwan, Sulaiman, Zuriani, Mustaffa

    Published 2022
    “…These algorithms are selected from the different metaheuristics classification groups, where the implementation of these algorithms into the said problems will be tested on the modified IEEE 14-bus system. …”
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  12. 12
    Modern fuzzy min max neural networks for pattern classification

    Modern fuzzy min max neural networks for pattern classification by Al Sayaydeh, Osama Nayel Ahmad

    Published 2019
    “…Among these algorithms, Fuzzy Min Max (FMM) neural network algorithm has been proven to be one of the premier neural networks for undertaking the pattern classification problems. …”
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  13. 13
    Development of a modified adaptive protection scheme using machine learning technique for fault classification in renewable energy penetrated transmission line

    Development of a modified adaptive protection scheme using machine learning technique for fault classification in renewable energy penetrated transmission line by Olufemi, Osaji Emmanuel

    Published 2020
    “…The hybrid Wavelet Multiresolution Analysis and Machine learning algorithm (WMRA-ML) is used to extracts the useful hidden knowledge from decomposed one-cycle fault transient signals (voltage & current) from four Matlab/Simulink CIGRE models. …”
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  14. 14
    Decoding of visual activity patterns from fMRI responses using multivariate pattern analyses and convolutional neural network

    Decoding of visual activity patterns from fMRI responses using multivariate pattern analyses and convolutional neural network by Zafar, R., Kamel, N., Naufal, M., Malik, A.S., Dass, S.C., Ahmad, R.F., Abdullah, J.M., Reza, F.

    Published 2017
    “…In this study, an algorithm is developed using Multivariate pattern analysis (MVPA) and modified CNN to decode the behavior of brain for different images with limited data set. …”
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  15. 15
    The effect of pre-processing techniques and optimal parameters on BPNN for data classification

    The effect of pre-processing techniques and optimal parameters on BPNN for data classification by HUSSEIN, AMEER SALEH

    Published 2015
    “…In this research, a performance analysis based on different activation functions; gradient descent and gradient descent with momentum, for training the BP algorithm with pre-processing techniques was executed. …”
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  16. 16
    The use of SOM for fingerprint classification

    The use of SOM for fingerprint classification by Turky A.M., Ahmad M.S.

    Published 2023
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  17. 17
    Accelerating learning performance of back propagation algorithm by using adaptive gain together with adaptive momentum and adaptive learning rate on classification problems

    Accelerating learning performance of back propagation algorithm by using adaptive gain together with adaptive momentum and adaptive learning rate on classification problems by Abdul Hamid, Norhamreeza, Mohd Nawi, Nazri, Ghazali, Rozaida, Mohd Salleh, Mohd Najib

    Published 2011
    “…Over the years, many improvements and modifications of the back propagation learning algorithm have been reported. In this research, we propose a new modified back propagation learning algorithm by introducing adaptive gain together with adaptive momentum and adaptive learning rate into weight update process. …”
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  18. 18
    Modified word representation vector based scalar weight for contextual text classification

    Modified word representation vector based scalar weight for contextual text classification by Abbas Saliimi, Lokman

    Published 2024
    “…For this experiment, the modified word vectors serve as input to train a Machine Learning (ML) model for the text classification process, aiming for the developed ML model to have a significantly smaller parameter count. …”
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  19. 19
    Study Of Modified Training Algorithm For Optimized Convergence Speed Of Neural Network

    Study Of Modified Training Algorithm For Optimized Convergence Speed Of Neural Network by Kang, Miew How

    Published 2016
    “…First proposed algorithm is the combination of momentum algorithm with adaptive learning rate (ALR) algorithm, and second proposed algorithm is the combination of momentum algorithm with automatic learning rate selection (ALRS) algorithm. …”
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  20. 20
    A modified weighted support vector machine (WSVM) to reduce noise data in classification problem

    A modified weighted support vector machine (WSVM) to reduce noise data in classification problem by Mohd Dzulkifli, Syarizul Amri

    Published 2021
    “…Therefore, this research work proposes a modified WSVM utilized with instance selection method and weighted learning to improve WSVM training and classification accuracy. …”
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