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modeling algorithm » scheduling algorithm (Expand Search)
method algorithm » mould algorithm (Expand Search)
data finding » data mining (Expand Search), data hiding (Expand Search)
modeling algorithm » scheduling algorithm (Expand Search)
method algorithm » mould algorithm (Expand Search)
data finding » data mining (Expand Search), data hiding (Expand Search)
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Extended Behavioral Modeling of FET and Lattice-Mismatched HEMT Devices
Published 2016Subjects: Get full text
doctoralThesis -
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A multi-class discriminative motif finding algorithm for autosomal genomic data. (c2015)
Published 2016“…Our algorithm first performs a feature selection step to define differentiable SNPs. …”
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masterThesis -
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A Hybrid Deep Learning Model Using CNN and K-Mean Clustering for Energy Efficient Modelling in Mobile EdgeIoT
Published 2023“…This research proposed a hybrid model for energy-efficient cluster formation and a head selection (E-CFSA) algorithm based on convolutional neural networks (CNNs) and a modified k-mean clustering (MKM) method for MEC. …”
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Estimating Construction Project Duration Using a Machine Learning Algorithm
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masterThesis -
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A reduced model for phase-change problems with radiation using simplified PN approximations
Published 2025“…The integro-differential equation for the full radiative transfer is replaced by a set of differential equations which are independent of the angle variable and easy to solve using conventional computational methods. To solve the coupled equations, we implement a second-order implicit scheme for the time integration and a mixed finite element method for the space discretization. …”
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Using machine learning algorithm for detection of cyber-attacks in cyber physical systems
Published 2022“…We begin a process to cleaning up the data in the CPS database by applying normalization to eliminate errors and duplication. …”
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A Hybrid Intrusion Detection Model Using EGA-PSO and Improved Random Forest Method
Published 2022“…Due to a limited training dataset, an ML-based IDS generates a higher false detection ratio and encounters data imbalance issues. To deal with the data-imbalance issue, this research develops an efficient hybrid network-based IDS model (HNIDS), which is utilized using the enhanced genetic algorithm and particle swarm optimization(EGA-PSO) and improved random forest (IRF) methods. …”
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Correlation Clustering via s-Club Cluster Edge Deletion
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masterThesis -
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On-site workshop investment problem: A novel mathematical approach and solution procedure
Published 2023“…Two new (linear) mathematical programming models are proposed for MOSWIPT. Next, due to the NP-hardness of the problem, an enhanced Genetic Algorithm (GA)-based metaheuristic with efficient problem-specific improvement rules as local search and effective crossover and mutation operators is proposed. …”
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A New Parallel Genetic Algorithm Model
Published 2020“…This paper presents an implementation of three Genetic Algorithm models for solving a reliability optimization problem for a redundancy system with several failure modes, a modification on a parallel a genetic algorithm model and a new parallel genetic algorithm model. …”
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Multilayer Reversible Data Hiding Based on the Difference Expansion Method Using Multilevel Thresholding of Host Images Based on the Slime Mould Algorithm
Published 2022“…The slime mould algorithm (SMA) gives good results in finding the best solutions to optimization problems. …”
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Genetic and heuristic algorithms for regrouping service sites. (c2000)
Published 2000Get full text
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masterThesis -
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A flexible genetic algorithm-fuzzy regression approach for forecasting: The case of bitumen consumption
Published 2019“…Findings Production, import, export, road construction and price are considered as the input data used in the present study. …”
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Predicting Patient ICU Readmission Using Recurrent Neural Networks With Long Short-Term Memory
Published 2025Subjects: -
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Machine Learning-Driven Prediction of Corrosion Inhibitor Efficiency: Emerging Algorithms, Challenges, and Future Outlooks
Published 2025“…At the same time, virtual sample augmentation and genetic algorithm feature selection elevate sparse data performance, raising k-nearest neighbor models from R<sup>2</sup> = 0.05 to 0.99 in a representative thiophene set. …”