Search Results - (( trust classification modeling algorithm ) OR ( java adaptation optimization algorithm ))

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  1. 1
    A Voting Technique Of Multilayer Perceptron Ensemble For Classification Application

    A Voting Technique Of Multilayer Perceptron Ensemble For Classification Application by Talib, Hafizah

    Published 2014
    “…In order to choose the final output of MLPE, a new voting algorithm named Trust-Sum Voting (TSV) is proposed. …”
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    Thesis
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  2. 2
    Parallel distributed genetic algorithm development based on microcontrollers framework

    Parallel distributed genetic algorithm development based on microcontrollers framework by Krishnan P.S., Kiong T.S., Koh J.

    Published 2023
    Conference paper
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  3. 3
    Individual And Ensemble Pattern Classification Models Using Enhanced Fuzzy Min-Max Neural Networks

    Individual And Ensemble Pattern Classification Models Using Enhanced Fuzzy Min-Max Neural Networks by F. M., Mohammed

    Published 2014
    “…Focused on computational intelligence models, this thesis describes in-depth investigations on two possible directions to design robust and flexible pattern classification models with high performance. …”
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  4. 4
    BIOLOGICAL INSPIRED INTRUSION PREVENTION AND SELF-HEALING SYSTEM FOR CRITICAL SERVICES NETWORK

    BIOLOGICAL INSPIRED INTRUSION PREVENTION AND SELF-HEALING SYSTEM FOR CRITICAL SERVICES NETWORK by MOHAMED AHMED ELSHEIK, MUNA ELSADIG

    Published 2011
    “…Secondly, specification language, system design, mathematical and computational models for IPS and SH system are established, which are based upon nonlinear classification, prevention predictability trust, analysis, self-adaptation and self-healing algorithms. …”
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  5. 5
    Enhancement of neural network based multi agent system for classification and regression in energy system

    Enhancement of neural network based multi agent system for classification and regression in energy system by Yaw C.T., Yap K.S., Wong S.Y., Yap H.J., Paw J.K.S.

    Published 2023
    Article
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  6. 6
    Hybrid Models Of Fuzzy Artmap And Qlearning For Pattern Classification

    Hybrid Models Of Fuzzy Artmap And Qlearning For Pattern Classification by Navan, Farhad Pourpanah

    Published 2015
    “…As a result, an agent-based QFAM ensemble model with a new trust measurement and negotiation method is proposed. …”
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  7. 7
    Interpretable hybrid models of Kolmogorov–Arnold Networks and transformer for mental health classification in low-resource languages: a Malay social media case study

    Interpretable hybrid models of Kolmogorov–Arnold Networks and transformer for mental health classification in low-resource languages: a Malay social media case study by Ahmad, Zaaba

    Published 2025
    “…The structured approach of KAN improves model interpretability by clarifying feature importance, thereby enhancing trust and potential usability in clinical or community mental health settings. …”
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  8. 8
    A modified single-stacked ensemble framework for prognostic analysis of leukemia relapse

    A modified single-stacked ensemble framework for prognostic analysis of leukemia relapse by Chuan, Zun Liang

    Published 2026
    “…By embedding structured statistical modeling within an AI framework, the approach integrates clinical insight with algorithmic reasoning. …”
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  9. 9
    Anfis Modelling On Diabetic Ketoacidosis For Unrestricted Food Intake Conditions

    Anfis Modelling On Diabetic Ketoacidosis For Unrestricted Food Intake Conditions by Saraswati, Galuh Wilujeng

    Published 2017
    “…The project has also implemented the optimization process onto the proposed ANFIS model through the hybrid of Genetic Algorithm on the fuzzy membership function of the ANFIS model. …”
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  10. 10
    Classification and visualization of e-commerce product reviews comparison using support vector machine / Nuwairah Aimi Ahmad Kushairi

    Classification and visualization of e-commerce product reviews comparison using support vector machine / Nuwairah Aimi Ahmad Kushairi by Ahmad Kushairi, Nuwairah Aimi

    Published 2023
    “…Afterward, the system recommends the best shop to purchase from and visualizes the reviews to compare products from different shops. The SVM classifier model successfully classified the reviews with an accuracy of 96.8% during the testing stage of the classification. …”
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