Extended Behavioral Modeling of FET and Lattice-Mismatched HEMT Devices حسب Khawam, Yahya Bader

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Cluster-based anonymity model and algorithm for 1:1 dataset with a single sensitive attribute using machine learning technique حسب J. Jayapradha (21323774)

Sentiment Mining of Arabic Twitter Data حسب Ahmed, Soha Galalaldin Khider

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A Multiprocessing-Based Sensitivity Analysis of Machine Learning Algorithms for Load Forecasting of Electric Power Distribution System حسب Ameema Zainab (16864263)

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Multimodal EEG and Keystroke Dynamics Based Biometric System Using Machine Learning Algorithms حسب Arafat Rahman (8065562)

A combined algorithm based on finite elements and a modified methodof characteristics for the analysis of the corona in wire-ductelectrostatic precipitators حسب Al-Hamouz, Z.M.

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Machine Learning-Driven Prediction of Corrosion Inhibitor Efficiency: Emerging Algorithms, Challenges, and Future Outlooks حسب Najam Us Sahar Riyaz (22927843)

"…At the same time, virtual sample augmentation and genetic algorithm feature selection elevate sparse data performance, raising k-nearest neighbor models from R² = 0.05 to 0.99 in a representative thiophene set. …"

Graph Computing Systems and Partitioning Techniques: A Survey حسب Tewodros Alemu Ayall (19273717)

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An Effective Hybrid NARX-LSTM Model for Point and Interval PV Power Forecasting حسب Mohamed Massaoudi (16888710)

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Using genetic algorithms to optimize software quality estimation models حسب Azar, Danielle

"…In the first approach, we assume the existence of several models, and we use a genetic algorithm to combine them, and adapt them to a given data set. …"
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Second-order conic programming for data envelopment analysis models حسب Mourad, Nahia

"…This paper constructs a second-order conic optimization problem unifying several DEA models. Moreover, it presents an algorithm that solves the former problem, and provides a MATLAB function associated with it. …"
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The automation of the development of classification models and improvement of model quality using feature engineering techniques حسب Sjoerd Boeschoten (17347045)

"…In this article, we propose a framework that combines feature engineering techniques such as data imputation, transformation, and class balancing to compare the performance of different prediction models and select the best final model based on predefined parameters. …"

Data-Driven Electricity Demand Modeling for Electric Vehicles Using Machine Learning حسب Far, Reza Davoodi

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Modeling and Control of a Thermally Driven MEMS Actuator for RF Applications حسب Estahbanati, Shahabeddin Vamegh

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An ant colony optimization algorithm to improve software quality prediction models حسب Azar, D.

"…For this purpose, software quality prediction models have been extensively used. However, building accurate prediction models is hard due to the lack of data in the domain of software engineering. …"
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A Genetic Algorithm for Improving Accuracy of Software Quality Predictive Models حسب Azar, Danielle

"…In this paper, we present a genetic algorithm that adapts such models to new data. We give empirical evidence illustrating that our approach out-beats the machine learning algorithm C4.5 and random guess.…"
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Power System Transient Stability Assessment Based on Machine Learning Algorithms and Grid Topology حسب Senyuk, Mihail

"…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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Correlation Clustering with Overlaps حسب Fakhereldine, Amin

"…In other words, data elements (or vertices) will be allowed to be members in more than one cluster instead of limiting them to only one single cluster, as in classical clustering methods. …"
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Impact of AI and Dynamic Ensemble Techniques in Enhancing Healthcare Services: Opportunities and Ethical Challenges حسب Haseeb Javed (18576340)

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Using Machine Learning Algorithms to Forecast Solar Energy Power Output حسب Ali Jassim Lari (22597940)

"…The data forecasting horizon used was a 24-h window in steps of 30 min. …"