Search Results - (( parameter optimization mead algorithm ) OR ( frames interactive learning algorithm ))

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    Single and Multiple variables control using Tree Physiology Optimization by Halim, A.H., Ismail, I.

    Published 2017
    “…In the proposed method, each shoot from each branch search for possible solution in parallel and the fitness is evaluated based on all best values found by branch search. The proposed algorithm is also compared with deterministic gradient-free algorithm: Nelder-Mead simplex (NMS) and another metaheuristic algorithm: Particle Swarm Optimization (PSO). …”
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    Article
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    Particle swarm optimization with deep learning for human action recognition by Usmani, U.A., Watada, J., Jaafar, J., Aziz, I.A., Roy, A.

    Published 2021
    “…We infer that our approach helps improve the tracking stability and increases the robustness of the tracking process by an accuracy of 97.09 on the Stony Brook University interaction dataset, 98.02 on the High-level Human Interaction Recognition Challenge interaction dataset, 96.17 on the Weizmann dataset. …”
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    Modelling transmission dynamics of covid-19 during Pre-vaccination period in Malaysia: a predictive guiseird model using streamlit by Zulkarnain, Norsyahidah, Abdul Hadi, Muhammad Salihi, Mohammad, Nurul Farahain, Shogar, Ibrahim

    Published 2023
    “…The mathematical model is solved using Scipy odeint function, which uses Livermore Solver for Ordinary Differential Equations with an Automatic method switching (LSODA) algorithm. The time-varying coefficients of SEIRD model that best fit the real data of COVID-19 cases are obtained using the Nelder-Mead optimization algorithm. …”
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    Proceeding Paper
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    Enhanced emotion recognition in videos: a convolutional neural network strategy for human facial expression detection and classification by Ashraf, Arselan, Gunawan, Teddy Surya, Arifin, Fatchul, Kartiwi, Mira, Sophian, Ali, Habaebi, Mohamed Hadi

    Published 2023
    “…Automated emotion recognition has garnered increasing interest due to its potential applications in various fields, such as human-computer interaction, machine learning, robotic control, and driver emotional state monitoring. …”
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    CNN architectures for road surface wetness classification from acoustic signals by Bahrami, Siavash, Doraisamy, Shyamala, Azman, Azreen, Nasharuddin, Nurul Amelina, Yue, Shigang

    “…Recorded acoustic signals were segmented into equal frames and thirteen MFCCs were extracted for each frame to train the CNNs. …”
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