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Power System Transient Stability Assessment Based on Machine Learning Algorithms and Grid Topology
Published 2023“…This work employs machine learning methods to develop and test a technique for dynamic stability analysis of the mathematical model of a power system. A distinctive feature of the proposed method is the absence of a priori parameters of the power system model. …”
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Extended Behavioral Modeling of FET and Lattice-Mismatched HEMT Devices
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Boosting the visibility of services in microservice architecture
Published 2023“…We utilized parameter optimization techniques, namely Grid Search, Random Search, Bayes Search, Halvin Grid Search, and Halvin Random Search to fine-tune the hyperparameters of our classifier models. Experimental results demonstrated that the CatBoost algorithm achieved the highest level of accuracy (90.42%) in predicting microservice quality.…”
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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. …”
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Estimating Construction Project Duration Using a Machine Learning Algorithm
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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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Predicting Dropouts among a Homogeneous Population using a Data Mining Approach
Published 2019“…Our research relies solely on pre-college and college performance data available in the institutional database. Our research reveals that the Gradient Boosted Trees is a robust algorithm that predicts dropouts with an accuracy of 79.31% and AUC of 88.4% using only pre-enrollment data. …”
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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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Valuation of commodity option prices under a regime-switching model with stochastic convenience yield: Model calibration using flower pollination optimization algorithm
Published 2025Subjects: “…Expectation–maximization algorithm…”
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New fault models and efficient BIST algorithms for dual-portmemories
Published 1997“…These modifications allow multiple access of memory cells for increased test speed with minimal overhead on both silicon area and device performance. New fault models are proposed, and efficient O(n) test algorithms are described for both the memory array and the address decoders. …”
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Parameter Estimation Of Wiener-Hammerstein Models Via Genetic Algorithms
Published 2020“…Also, the algorithm is applied to model a DC generator with some nonlinear characteristics…”
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Automating UML Models Refactoring using Search-Based Algorithms
Published 2020Get full text
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A genetic-based algorithm for fuzzy unit commitment model
Published 2000“…The genetic algorithm (GA) approach is then used to solve the proposed fuzzy UCP model. …”
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Using genetic algorithms to optimize software quality estimation models
Published 2004“…This thesis explores the use of genetic algorithms for the problem of optimizing existing rule-based software quality estimation models. …”
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Combining and adapting software quality predictive models by genetic algorithms
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Data-Driven Electricity Demand Modeling for Electric Vehicles Using Machine Learning
Published 2024Get full text
doctoralThesis