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    Analysis of air quality index (AQI) in Klang valley using artificial neural network (ANN) technique / Rosli Idris by Idris, Rosli

    Published 2007
    “…In the ANN technique, three methods will be used. The methods are Levenberg-Marquardt Algorithms, Resilient Backpropagation and Quasi-Newton Algorithms will be considered adopted to analyze the AQI data. …”
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    Condition diagnosis of bearing system using multiple classifiers of ANNs and adaptive probabilities in genetic algorithms by Wulandhari, Lili A., Wibowo, Antoni, Desa, Mohammad I.

    Published 2014
    “…Therefore, finding the best weights in learning process is an important task for obtaining good performance of ANNs.Previous researchers have proposed some methods to get the best weights such as simple average and majority voting.However, these methods have some limitations in providing the best weights especially in condition diagnosis of bearing systems.In this paper, we propose a hybrid technique of multiple classifier-ANNs (mANNs) and adaptive probabilities in genetic algorithms (APGAs) to obtain the best weights of ANNs in order to increase the classification performance of ANNs in condition diagnosis of bearing systems. …”
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    Data mining based damage identification using imperialist competitive algorithm and artificial neural network by Gordan, Meisam, Razak, Hashim Abdul, Ismail, Zubaidah, Ghaedi, Khaled

    Published 2018
    “…Based on the obtained results, it is concluded that the ICA-ANN indicates a better performance in detection of damage severity over the ANN method used only.…”
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    Hybrid honey badger algorithm with artificial neural network (HBA-ANN) for website phishing detection by Muhammad Arif, Mohamad, Muhammad Aliif, Ahmad, Zuriani, Mustaffa

    Published 2024
    “…HBA as metahueristic algorithm is used to optimize the network training process of ANN to improve their performances. …”
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    Improved prediction accuracy of biomass heating value using proximate analysis with various ANN training algorithms by Veza, I., Irianto, Panchal, H., Paristiawan, P.A., Idris, M., Fattah, I.M.R., Putra, N.R., Silambarasan, R.

    Published 2022
    “…However, most studies of ANN to estimate the biomassâ�� HHV only use one algorithm to train a small number of biomass datasets. …”
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    PROPOSED METHODOLOGY FOR OPTIMIZING THE TRAINING PARAMETERS OF A MULTILAYER FEED-FORWARD ARTIFICIAL NEURAL NETWORKS USING A GENETIC ALGORITHM by ABDALLA, OSMAN AHMED

    Published 2011
    “…To overcome these limitations, there have been attempts to use genetic algorithm (GA) to optimize some of these parameters. …”
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    Eye Diagram Modeling Of High-Speed Channels Using Artificial Neural Networks With An Improved Adaptive Sampling Algorithm by Goay, Chan Hong

    Published 2019
    “…This work proposes an improvement to the original adaptive sampling algorithm and uses it as the sampling method for eye diagram modeling. …”
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    Enhancing NoC-based MPSoC performance: a predictive approach with ANN and guaranteed convergence arithmetic optimization algorithm by Muhsen, Yousif Raad, Husin, Nor Azura, Zolkepli, Maslina, Manshor, Noridayu, Al-Hchaimi, Ahmed Abbas Jasim, Ridha, Hussein Mohammed

    Published 2023
    “…The main idea of the proposed method is to develop a prediction model, speci‚cally an Arti‚cial Neural Network (ANN) optimized using the Guaranteed Convergence Arithmetic Optimization Algorithm (GCAOA-ANN), for predicting the utilized routing algorithm in NoC-based MPSoC platform during the DSE process. …”
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    An intelligent method to estimate the inertia matrix of a robot arm for active force control using on-line neural network training scheme by Hussein, Shamsul Bahri, Jamaluddin, Hishamuddin, Mailah, Musa

    Published 1999
    “…During the training stage, the proposed ANN scheme trains the ANN parameters (weights and biases) for a period of time by utilising the back-propagation (BP) learning method. …”
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    Benchmarking routing algorithms in NoC-based MPSoCs using guaranteed convergence arithmetic optimization with artificial neural networks and fuzzy MCDM by R Muhsin, Al-Molla Yousif

    Published 2024
    “…The methodology includes two phases; phase 1 includes developing a prediction model, specifically an ANN optimized using the Guaranteed Convergence Arithmetic Optimization Algorithm (GCAOA-ANN). …”
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    Optimization and discretization of dragonfly algorithm for solving continuous and discrete optimization problems by Bibi Amirah Shafaa, Emambocus

    Published 2024
    “…Based on the experimental results, the optimized DA algorithm is a much better training algorithm for ANNs as compared to the usual gradient-descent backpropagation algorithm since the resultant ANNs trained by the optimized DA achieve higher accuracy. …”
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    Maximum power point tracking using artificial neural network for photovoltaic standalone system by Khanaki, Razieh

    Published 2014
    “…In this work, an improved MPPT using Artificial Neural Network (ANN) has been presented. …”
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    Sauvola Segmentation and Support Vector Machine-Salp Swarm Algorithm Approach for Identifying Nutrient Deficiencies in Citrus Reticulata Leaves by Lia, Kamelia

    Published 2024
    “…With SSA optimization, the Sauvola method combined with SVM reaches an accuracy of 99.58%, surpassing other methods that use image processing and ANN classification. …”
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    Modeling time series data using Genetic Algorithm based on Backpropagation Neural network by Haviluddin

    Published 2018
    “…One form of ANNs models are widely used for various applications are Feedforward Neural Networks (FFNN). …”
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    Rainfall prediction system using Artificial Neural Network (ANN) / Izzat Izzuddin Zulkarnain by Zulkarnain, Izzat Izzuddin

    Published 2024
    “…The Rainfall Prediction System using Artificial Neural Network (ANN) project aimed to develop a model for predicting rainfall based on weather attributes. …”
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    Mussels wandering optimization algorithmn based training of artificial neural networks for pattern classification by Abusnaina, Ahmed A., Abdullah, Rosni

    Published 2013
    “…Traditional training algorithms have some drawbacks such as local minima and its slowness.Therefore, evolutionary algorithms are utilized to train neural networks to overcome these issues.This research tackles the ANN training by adapting Mussels Wandering Optimization (MWO) algorithm.The proposed method tested and verified by training an ANN with well-known benchmarking problems.Two criteria used to evaluate the proposed method were overall training time and classification accuracy.The obtained results indicate that MWO algorithm is on par or better in terms of classification accuracy and convergence training time.…”
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