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  1. 1
    An ensemble learning method for spam email detection system based on metaheuristic algorithms

    An ensemble learning method for spam email detection system based on metaheuristic algorithms by Behjat, Amir Rajabi

    Published 2015
    “…In order to address the challenges that mentioned above in this study, in the first phase, a novel architecture based on ensemble feature selection techniques include Modified Binary Bat Algorithm (NBBA), Binary Quantum Particle Swarm Optimization (QBPSO) Algorithm and Binary Quantum Gravita tional Search Algorithm (QBGSA) is hybridized with the Multi-layer Perceptron (MLP) classifier in order to select relevant feature subsets and improve classification accuracy. …”
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  2. 2
    Adaptive neurofuzzy inference system for an active transfemoral prosthetic leg using in-socket sensory system / Nur Hidayah Mohd Yusof

    Adaptive neurofuzzy inference system for an active transfemoral prosthetic leg using in-socket sensory system / Nur Hidayah Mohd Yusof by Nur Hidayah , Mohd Yusof

    Published 2019
    “…The first part of this work is the development of an Adaptive Neurofuzzy Inference System (ANFIS) - based algorithm to human gait phase recognition. …”
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  3. 3
    A Novel Path Prediction Strategy for Tracking Intelligent Travelers

    A Novel Path Prediction Strategy for Tracking Intelligent Travelers by Motlagh, Omid Reza Esmaeili

    Published 2009
    “…The FCM nodes are a novel selection of kinematical factors. Genetic algorithm (GA) is then used to train the FCM to be able to replicate the decisional behaviors of the intelligent traveler. …”
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  4. 4
    GLCM correlation approach for blood vessel identification in thermal image

    GLCM correlation approach for blood vessel identification in thermal image by Rusli, Nazreen, Md Yusof, Hazlina, Sidek, Shahrul Na'im, Ishak, Nor Izzati

    Published 2019
    “…In this work, healthy children were assigned as subjects prior to the development of the algorithm for thermal imaging analysis to form a reference model. …”
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  5. 5
    Development of low-overhead soft error mitigation technique for safety critical neural networks applications

    Development of low-overhead soft error mitigation technique for safety critical neural networks applications by Khalid Adam, Ismail Hammad

    Published 2021
    “…To address this issue, this study developed a Selective Mitigation Technique based on the standard Triple Modular Redundancy (S-MTTM-R) to determine the model’s vulnerable parts, distinguishing Malfunction and Light-Malfunction errors. …”
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