The buffered work-pool approach for search-tree based optimization algorithms حسب Abu-Khzam, Faisal N.

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Automating UML Models Refactoring using Search-Based Algorithms حسب unknown

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A genetic-based algorithm for fuzzy unit commitment model حسب Mantawy, A.H.

"…The genetic algorithm (GA) approach is then used to solve the proposed fuzzy UCP model. …"
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Multi-Target Tracking Resources Allocation Using Multi-Agent Modeling and Auction Algorithm حسب De Rochechouart, Maxence

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Power System Transient Stability Assessment Based on Machine Learning Algorithms and Grid Topology حسب Senyuk, Mihail

"…In this study, the emergency control algorithms based on ensemble machine learning algorithms (XGBoost and Random Forest) were developed for a low-inertia power system. …"
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Modeling and Control of a Robot Based Rehabilitation System for the Head-Neck Joint حسب Raslan, Ismail Tareq

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A hybrid heuristic approach to optimize rule based software quality estimation models. (c2008) حسب Korkmaz, Rita

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Incremental and Heuristic Algorithms for Deriving Adaptive Distinguishing Test Cases for Nondeterministic Finite State Machines حسب Saleh, Ayat

الموضوعات: "…Model Based Testing…"
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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)

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

Particle swarm optimization based algorithm for protein structure prediction in the 3D HP model. (c2008) حسب Kanj, Fatima Hassan

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Calibration of building model based on indoor temperature for overheating assessment using genetic algorithm: Methodology, evaluation criteria, and case study حسب Mutasim Baba, Fuad

"…This methodology includes a variance-based sensitivity analysis to determine building parameters that significantly influence indoor air temperatures, the Multi-Objective Genetic Algorithm to calibrate different rooms simultaneously based on the significant param eters identified by the sensitivity analysis, and new evaluation criteria to achieve a high-accuracy calibrated model. …"
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A systematic review of text classification research based on deep learning models in Arabic language حسب Wahdan, Ahlam

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Novel algorithms for accurate DNA base-calling حسب Mohammed, Omniyah Gul

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Metaheuristic Algorithm for State-Based Software Testing حسب Haraty, Ramzi A.

"…This article presents a metaheuristic algorithm for testing software, especially web applications, which can be modeled as a state transition diagram. …"
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An Uncertainty Based Genetic Algorithm Approach for Project Resource Scheduling حسب ALKETBI, SAIF

"…Several nonlinear optimization models were developed for this purpose assuming uniform resource availability and sequence based project tasks. …"
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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. …"

Sensitivity analysis and genetic algorithm-based shear capacity model for basalt FRC one-way slabs reinforced with BFRP bars حسب Abathar Al-Hamrani (16494884)

"…Finally, a design equation that can predict the shear capacity of one-way BFRC-BFRP slabs was proposed based on genetic algorithm. The proposed model showed the best prediction accuracy compared to the available design codes and guidelines with a mean of predicted to experimental shear capacities (V_pred/V_exp) ratio of 0.97 and a coefficient of variation of 17.91%.…"

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

MLMRS-Net: Electroencephalography (EEG) motion artifacts removal using a multi-layer multi-resolution spatially pooled 1D signal reconstruction network حسب Sakib Mahmud (15302404)

"…Because the diagnosis of many neurological diseases is heavily reliant on clean EEG data, it is critical to eliminate motion artifacts from motion-corrupted EEG signals using reliable and robust algorithms. Although a few deep learning-based models have been proposed for the removal of ocular, muscle, and cardiac artifacts from EEG data to the best of our knowledge, there is no attempt has been made in removing motion artifacts from motion-corrupted EEG signals: In this paper, a novel 1D convolutional neural network (CNN) called multi-layer multi-resolution spatially pooled (MLMRS) network for signal reconstruction is proposed for EEG motion artifact removal. …"