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1
Comparison between Lamarckian Evolution and Baldwin Evolution of neural network
Published 2006“…Hybrid genetic algorithms are the combination of learning algorithms(Back propagation), usually working as evaluation functions, and genetic algorithms. …”
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2
Fuzzy adaptive teaching learning-based optimization for solving unconstrained numerical optimization problems
Published 2022“…The performance of the fuzzy adaptive teaching learning-based optimization is evaluated against other metaheuristic algorithms including basic teaching learning-based optimization on 23 unconstrained global test functions. …”
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3
Comparative study on job scheduling using priority rule and machine learning
Published 2021“…VM is allocated in space-sharing mode all the time. We’ve achieved better for SJF and a decent machine learning algorithm outcome as well.…”
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4
Application Of Multi-Layer Perceptron Technique To Detect And Locate The Base Of A Young Corn Plant
Published 2007“…Results of studying color segmentation using machine-learning algorithm and color space analysis is presented in this thesis. …”
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5
Artificial Intelligence as A Common Heritage of Mankind
Published 2023“…Artificial intelligence technologies today employ techniques known as machine learning and deep learning, which apply datasets to a suitable mathematical or statistical technique known as an algorithm. …”
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Improving neural networks training using experiment design approach
Published 2005“…Randomly select the m data set for conventional training algorithm. One more data (m+ 1) is entered to train the NN again. …”
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7
Nomadic people optimizer (NPO) for large-scale optimization problems
Published 2019“…The basic component of the algorithm consists of several clans and each clan searches for the best place (or best solution) based on the position of their leader. …”
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8
Predictive modeling and feature attribution of CO₂ adsorption on LDH-derived materials using machine learning approach
Published 2025“…The findings revealed that CatBoost is most suitable with R2 of 0.99 for training and 0.87 for test, and RMSE of 0.184 compared to the other 5 algorithms. AdaBoost, XGBoost, GBDT, LightGBM, and RF performed in an acceptable range. …”
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9
Sensorless induction motor speed control for electric vehicles using enhanced hybrid flux estimator with ann-ifoc controller
Published 2022“…The function of the ANN was to improve speed-tracking performance, and the learning rate of the ANN inside the indirect FOC’s structure trained using the Levenberg-Marquardt (LM) algorithm was varied in order to increase speed-tracking accuracy when combined with the improved ANN speed controller. …”
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