Search Results - (( java application using algorithm ) OR ( parameter 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 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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Book Section -
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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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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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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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RSA Encryption & Decryption using JAVA
Published 2006“…References and theories to support the research of 'RSA Encryption/Decryption using Java' have been disclosed in Literature Review section. …”
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Final Year Project -
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Provider independent cryptographic tools
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Monograph -
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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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Thesis -
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Federated deep learning for automated detection of diabetic retinopathy
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Proceeding Paper -
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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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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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Yaw rate and sideslip control using h-infinity-pid controller during automated lane change manoeuver
Published 2021“…This research aims to propose a simple PID tuning algorithm and to investigate the possibility of utilising H¥ synthesis during the automated LC manoeuvre by initiating the estimated steering wheel angle of the driver’s model at a constant speed of 80 km=h. …”
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Thesis -
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Real-Time Video Processing Using Native Programming on Android Platform
Published 2012“…However for the Android platform that based on the JAVA language, most of the software algorithm is running on JAVA that consumes more time to be compiled. …”
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Direct approach for mining association rules from structured XML data
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Thesis -
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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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Final Year Project / Dissertation / Thesis
