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modeling algorithm » scheduling algorithm (Expand Search)
data algorithm » jaya algorithm (Expand Search), deer algorithm (Expand Search)
element » elements (Expand Search)
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Machine Learning-Driven Prediction of Corrosion Inhibitor Efficiency: Emerging Algorithms, Challenges, and Future Outlooks
Published 2025“…In contrast, fragmented datasets suffer from overfitting with R<sup>2</sup> often under 0.70. Graph neural networks lower prediction error by up to thirty percent relative to descriptor-driven QSAR models for structurally diverse inhibitors. …”
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Prediction of EV Charging Behavior Using Machine Learning
Published 2021“…Using data-driven tools and machine learning algorithms to learn the EV charging behavior can improve scheduling algorithms. …”
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UniBFS: A novel uniform-solution-driven binary feature selection algorithm for high-dimensional data
Published 2024“…To overcome these challenges, a new FS algorithm named Uniform-solution-driven Binary Feature Selection (UniBFS) has been developed in this study. …”
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A neural networks algorithm for data path synthesis
Published 2003“…The algorithm is driven by a motion equation that determines the neurons firing conditions based on the modified Hopfield neural network model of computation. …”
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Cyberbullying Detection Model for Arabic Text Using Deep Learning
Published 2023“…Hence, detecting any act of cyberbullying in an automated manner will be helpful for stakeholders to prevent any unfortunate results from the victim’s perspective. Data-driven approaches, such as machine learning (ML), par ticularly deep learning (DL), have shown promising results. …”
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A Parallel Neural Networks Algorithm for the Clique Partitioning Problem
Published 2002“…In this paper we present a parallel algorithm to solve the above problem for arbitrary graphs using a Hopfield Neural Network model of computation. …”
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A Neural Networks Algorithm for the Minimum Colouring Problem Using FPGAs†
Published 2010“…The proposed algorithm has a time complexity of O(1) for a neural network with n vertices and k colours. …”
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Estimation of the methanol loss in the gas hydrate prevention unit using the artificial neural networks: Investigating the effect of training algorithm on the model accuracy
Published 2023“…Adjusting the weight and bias of the ANN model using an optimization algorithm is known as the training process. …”
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Enhancing Breast Cancer Diagnosis With Bidirectional Recurrent Neural Networks: A Novel Approach for Histopathological Image Multi-Classification
Published 2025“…The BRNN model, refined using the Adagrad optimization algorithm, efficiently integrates the learned features from both branches. …”
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A systematic review of text classification research based on deep learning models in Arabic language
Published 2020“…The evaluation criteria used in the algorithms of different neural network types and how they play a large role in the highly accurate classification of Arabic texts are discussed. …”
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Data-Driven Electricity Demand Modeling for Electric Vehicles Using Machine Learning
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NEURAL NETWORK MODEL FOR PLANNED REPLACEMENT OF BOEING 737 BRAKES
Published 2020“…., Boeing 737, is analyzed using the Artificial Neural Network and Weibull regression models. One-layered feed-forward back-propagation algorithm for artificial neural network whereas three parameters model for Weibull are used for the analysis. …”
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Dynamic performance evaluation and machine learning-assisted optimization of a solar-driven system integrated with PCM-based thermal energy storage: A case study approach
Published 2025“…A comprehensive techno-economic analysis is conducted, supported by a machine learning-assisted optimization framework that combines artificial neural networks with genetic algorithms. Considering optimum conditions, the system attains an exergetic efficiency of 30.13 % and a power generation of 7.24 MW, with a cost rate of 232.06 $/h and a payback period of 4.09 years. …”
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Modelling Exchange Rates during Currency Crisis using Neural Networks
Published 2006“…The models are built using the feedforward ANN structure trained by the backpropagation algorithm. …”
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