Search Results - (( global distribution a algorithm ) OR ( (parameter OR parameters) automation model algorithm ))
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Automated-tuned hyper-parameter deep neural network by using arithmetic optimization algorithm for Lorenz chaotic system
Published 2023“…Deep neural networks (DNNs) are very dependent on their parameterization and require experts to determine which method to implement and modify the hyper-parameters value. This study proposes an automated-tuned hyper�parameter for DNN using a metaheuristic optimization algorithm, arithmetic optimization algorithm (AOA). …”
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Automated-tuned hyper-parameter deep neural network by using arithmetic optimization algorithm for Lorenz chaotic system
Published 2023“…Deep neural networks (DNNs) are very dependent on their parameterization and require experts to determine which method to implement and modify the hyper-parameters value. This study proposes an automated-tuned hyper-parameter for DNN using a metaheuristic optimization algorithm, arithmetic optimization algorithm (AOA). …”
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Automated Model Generation Approach Using MATLAB
Published 2011“…An estimation algorithm is then required in order to obtain parameters for these models. …”
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Spatiotemporal extraction of aquaculture ponds under complex surface conditions based on deep learning and remote sensing indices
Published 2025“…The CWI approach is implemented based on three index algorithms of remote sensing analysis such as the Water Index (WI), the Modified Normalized Difference Water Index (MNDWI) and the Automated Water Extraction Index with Shadow (AWEIsh). …”
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Using simulated annealing algorithm for optimization of quay cranes and automated guided vehicles scheduling
Published 2011“…In this paper, an integrated scheduling of quay cranes and automated guided vehicles is formulated as a mixed integer linear programming model. …”
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Fuzzy state space modeling for solving inverse problems in multivariable dynamic systems
Published 2005“…This model is used for optimization of input parameters in multivariable dynamic systems. …”
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Forward-Backward Time Stepping with Automated Edge-preserving Regularization Technique for Wood Defects Detection
Published 2019“…Therefore, two automated procedures are developed to determine these parameters iteratively. …”
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Federated deep learning for automated detection of diabetic retinopathy
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Different mutation and crossover set of genetic programming in an automated machine learning
Published 2020“…The function of Genetic Programming is to optimize the best combination of solutions from the possible pipelines of machine learning modelling, including selection of algorithms and parameters optimization of the selected algorithm. …”
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Different mutation and crossover set of genetic programming in an automated machine learning
Published 2020“…The function of Genetic Programming is to optimize the best combination of solutions from the possible pipelines of machine learning modelling, including selection of algorithms and parameters optimization of the selected algorithm. …”
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System identification for an autonomous quadrotor using extended and unscented kalman filters
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Proceeding Paper -
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A comparative analysis of metaheuristic algorithms in fuzzy modelling for phishing attack detection
Published 2021“…The optimization method derives from the metaheuristic algorithm. Therefore, the aim of this study is to make a comparative analysis between the metaheuristic algorithms in fuzzy modelling. …”
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Fuzzy expert system modeling of laser processing
Published 2009“…The machine head to table complex movement, with at least 14 controlable parameters and eight uncontrolable parameters often discourage researchers for traditional modeling approaches. …”
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Neural network modeling for prediction of weld bead geometry in laser microwelding
Published 2013“…The backpropagation with the Levenberg-Marquardt training algorithm was used to train the neural network model. …”
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Hybrid meta-heuristic algorithm for solving multi-objective aggregate production planning in fuzzy environment
Published 2017“…On the other hand, consideration of all parameters in an APP model makes the generation of a master production schedule deeply complicated especially in real-world APP problems, where input data or parameters are frequently imprecise (fuzzy) due to incomplete or un obtain able information and daily changes patterns of demand and manufacturers capacity (Sakalhet al., 2010). …”
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Deep Learning Based image segmentation for expensive soil desiccation crack recognition and qualification
Published 2025“…Compared to traditional approaches, deep learning models, particularly with DeepLabv3+ variants, produced more reliable crack segmentation masks, thus enabling more accurate quantification of crack geometrical parameters, as demonstrated by lower error rates. …”
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