Search Results - (( self learning module algorithm ) OR ( java implication based algorithm ))

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    Interactive learning package for artificial neural network (Demonstration Module) / Camellia Mohd Kamal by Camellia , Mohd Kamal

    Published 2004
    “…Interactive Learning Package for Artificial Neural Network (Demonstration Module) is a learning package that allow the users to learn and enhance the understanding of the ANN subject in more detailed way and apply in the demonstration parts. …”
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    Thesis
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    An enhanced synthetic oversampling framework with self-supervised contrastive learning for multi-class image imbalance by Xiaoling, Gao

    Published 2025
    “…Class imbalance significantly affects the performance of machine learning and deep learning classifiers, especially in image recognition tasks where certain classes are underrepresented. …”
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    APPLICATION OF LINK GRAMMAR IN SEMI-SUPERVISED NAMED ENTITY RECOGNITION FOR ACCIDENT DOMAIN by SARI, YUNITA SARI

    Published 2011
    “…For the third contribution, we have applied the Self-Training algorithm which is one of the semi-supervised machines learning technique. …”
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    Incremental learning for large-scale stream data and its application to cybersecurity by Ali, Siti Hajar Aminah

    Published 2015
    “…To process large-scale data sequences, it is important to choose a suitable learning algorithm that is capable to learn in real time. …”
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    Performance comparison of non-invasive blood glucose level using artificial neural network and ultra-wide band antenna by Islam, Minarul Rafiqul, Sabira, Khatun, Kamarul Hawari, Ghazali, Mohd Mawardi, Saari, Shakib, Mohammed Nazmus, Mohamad Shaiful, Abdul Karim

    Published 2020
    “…In this work, feed-forward backpropagation neural network (FFBPNN) has been used as an ANN module. Several experiments were carried out to investigate the optimal ANN learning algorithm (levenberg-marquardt (LM), resilient backpropagation (RP) and scaled conjugate gradient (SCG)) for performance comparison. …”
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    Conference or Workshop Item