Search Results - (( developing using backpropagation algorithm ) OR ( java application mining algorithm ))

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    Letter recognition using backpropagation algorithm by Azrul Hafiz, Awang

    Published 2010
    “…This application will be developed using C++ Builder. Letter Recognition using Backpropagation Algorithm will make analysis and show the accuracy percentage, errors and the result of the letter training. …”
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    Undergraduates Project Papers
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    Study and Implementation of Data Mining in Urban Gardening by Mohana, Muniandy, Lee, Eu Vern

    Published 2019
    “…Attached sensors generate data and send these data to the Java Servlet application through a WIFI module. These data are processed and stored in appropriate formats in a MySQL server database. …”
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    Article
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    Prediction analysis of COVID-19 in Selangor by using Backpropagation Algorithm with Conjugate Gradient Method by Noor Amirah Ajmal Khan, Siti Mahani Marjugi

    Published 2024
    “…Backpropagation is a form of artificial neural network (ANN) algorithm that may be used to resolve issues in prediction analysis. …”
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    Article
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    Prediction analysis of COVID-19 in Selangor by using backpropagation algorithm with conjugate gradient method by Ajmal Khan, Noor Amirah, Marjugi, Siti Mahani

    Published 2024
    “…Backpropagation is a form of artificial neural network (ANN) algorithm that may be used to resolve issues in prediction analysis. …”
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    Article
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    Backpropagation neural network based on local search strategy and enhanced multi-objective evolutionary algorithm for breast cancer diagnosis by Ashraf Osman, Ibrahim, Siti Mariyam, Shamsuddin, Abdulrazak, Yahya Saleh, Ahmed, Ali, Mohd Arfian, Ismail, Shahreen, Kasim

    Published 2019
    “…However, the performance of such methods is based on the algorithms or technique. In this paper, we develop an intelligent technique using multiobjective evolutionary method hybrid with a local search approach to enhance the backpropagation neural network. …”
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    Article
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    A novel hybrid classification model of genetic algorithms, modified k-Nearest Neighbor and developed backpropagation neural network by Salari, Nader, Shohaimi, Shamarina, Najafi, Farid, Nallappan, Meenakshii, Karishnarajah, Isthrinayagy

    Published 2014
    “…Third, using a modified k-Nearest Neighbor method as well as an improved method of backpropagation neural networks, the classification process was advanced based on optimum arrays of the features selected by genetic algorithms. …”
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    Article
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    Development of vision autonomous guided vehicle behaviour using neural network by Husnul ‘Asyiyyah, Mohamad @ Awang

    Published 2012
    “…The objectives of this project are to develop a line recognition algorithm for automated guided vehicle and to understand two types of neural networks that can be use in manufacturing. …”
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    Undergraduates Project Papers
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    Particle swarm optimization for neural network learning enhancement by Abdull Hamed, Haza Nuzly

    Published 2006
    “…Backpropagation (BP) algorithm is widely used to solve many real world problems by using the concept of Multilayer Perceptron (MLP). …”
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    Thesis
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    Comparative Analysis Using Bayesian Approach To Neural Network Of Translational Initiation Sites In Alternative Polymorphic Contex by Herman, Nanna Suryana, Husin, Nurul Arneida, Hussin, Burairah

    Published 2012
    “…The objectives of this paper are to develop useful algorithms and to build a new classification model for the case study.The first approach of neural network includes training on algorithms of Resilient Backpropagation,Scaled Conjugate Gradient Backpropagation and Levenberg-Marquardt.The outputs are used in comparison with Bayesian Neural Network for efficiency comparison.The results showed that Resilient Backpropagation have the consistency in all measurement but performs less in accuracy.In second approach,the Bayesian Classifier_01 outperforms the Resilient Backpropagation by successfully increasing the overall prediction accuracy by 16.0%.The Bayesian Classifier_02 is built to improve the accuracy by adding new features of chemical properties as selected by the Information Gain Ratio method,and increasing the length of the window sequence to 201.The result shows that the built model successfully increases the accuracy by 96.0%.In comparison,the Bayesian model outperforms Tikole and Sankararamakrishnan (2008) by increasing the sensitivity by 10% and specificity by 26%. …”
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    Article
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    Mining Sequential Patterns Using I-PrefixSpan by Dhany , Saputra, Rambli Dayang, R.A., Foong, Oi Mean

    Published 2007
    “…Sequential pattern mining is a relatively new data-mining problem with many areas of application. …”
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    Conference or Workshop Item
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    A web-based implementation of k-means algorithms by Lee, Quan

    Published 2022
    “…This stinginess of proximity measures in data mining tools is stifling the performance of the algorithm. …”
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    Final Year Project / Dissertation / Thesis
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    Intelligent technique for grading tropical fruit using magnetic resonance imaging by A. Balogun, Wasiu, Salami, Momoh Jimoh Emiyoka, J. McCarthy, Michael, Mohd Mustafah, Yasir, Aibinu, Abiodun Musa

    Published 2013
    “…Levenberg-Marquardt algorithm (trainlm) gave the best performance fitness out of different types of backpropagation algorithm used with least Mean Square Error (MSE) of 0.0814 corresponding to R-value of 0.8094. …”
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    Article
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    Jawi recognition system by Nur Aziela, Mansor

    Published 2010
    “…In this project, it design and train network used Radial Basis Function (RBF) with backpropagation Neural Network. …”
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    Undergraduates Project Papers
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    Enhancing riverine load prediction of anthropogenic pollutants: Harnessing the potential of feed-forward backpropagation (FFBP) artificial neural network (ANN) models by Khairudin K., Ul-Saufie A.Z., Senin S.F., Zainudin Z., Rashid A.M., Abu Bakar N.F., Anas Abd Wahid M.Z., Azha S.F., Abd-Wahab F., Wang L., Sahar F.N., Osman M.S.

    Published 2025
    “…The feed-forward neural network model with a backpropagation algorithm and Bayesian regularisation training algorithm outperformed the radial basis neural network. …”
    Article
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