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Showing 121 - 140 results of 660 for search '(( elements method algorithm ) OR ((( data setting algorithm ) OR ( based models algorithm ))))*', query time: 0.12s Refine Results
  1. 121

    "Particle Swarm Based Design of Variable Structure Stabilizer for a Nonlinear Model of SMIB System" by Al-Hamouz, Z.

    Published 2006
    “…In this paper, a particle swarm-(PSO) based variable structure stabilizer (VSC) is proposed for enhancing the dynamic stability of a nonlinear model of synchronous machine infinite busbar system (SMIB). …”
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    article
  2. 122

    On Equivalent Circuit Model-Based State-of-Charge Estimation for Lithium-Ion Batteries in Electric Vehicles by Fatma Ahmed (11084787)

    Published 2025
    “…Two real-time model-based estimation algorithms, Extended Kalman Filter (EKF) and Unscented Kalman Filter (UKF), are compared for SoC estimation. …”
  3. 123
  4. 124

    Improving MRI Resolution: A Cycle Consistent Generative Adversarial Network-Based Approach for 3T to 7T Translation by Zakaria Shams Siam (22048001)

    Published 2024
    “…Efforts are underway to develop algorithms that can generate 7T MRI from 3T MRI to achieve better image quality without the need for 7T MRI machines. …”
  5. 125

    Artificial Intelligence for the Prediction and Early Diagnosis of Pancreatic Cancer: Scoping Review by Zainab Jan (17306614)

    Published 2023
    “…A higher level of accuracy (99%) was found in studies that used support vector machine, decision trees, and k-means clustering algorithms. </p><h3>Conclusions </h3><p dir="ltr">This review presents an overview of studies based on AI models and algorithms used to predict and diagnose pancreatic cancer patients. …”
  6. 126

    Distributed Tree-Based Machine Learning for Short-Term Load Forecasting With Apache Spark by Ameema Zainab (16864263)

    Published 2021
    “…The optimal value of clustering is used in this paper to cluster the data into groups to be able to reduce the computational time additionally. Multiple tree-based machine learning algorithms are tested with parallel computation to evaluate the performance with tunable parameters on a real-world dataset. …”
  7. 127

    Stochastic evolution algorithm for technology mapping by Al-Mulhem, A.S.

    Published 1998
    “…SELF-Map is based on the Stochastic Evolution (SE) algorithm. …”
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    article
  8. 128

    Evolutionary algorithm for protein structure prediction by Mansour, Nashat

    Published 2010
    “…In this paper, we present an improved genetic algorithm (GA) for predicting 3D structures of proteins based on the hydrophobic polar model. …”
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    conferenceObject
  9. 129

    Improvement Algorithm for Limited Space Scheduling by Zouein, Pierrette

    Published 2001
    “…The model characterizes resource space requirements over time and establishes a time-space relationship for each activity in the schedule, based on alternative resource levels. …”
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    article
  10. 130
  11. 131

    Correlation Clustering via s-Club Cluster Edge Deletion by Makarem, Norma

    Published 2023
    “…In certain situations, the requirement for clusters to be cliques was deemed excessively stringent, leading to the proposal of alternative relaxed clique models for dense subgraphs, such as s-club. In this work, we implement three approaches to tackle the 2-club clustering via edge deletion: a heuristic approach based on the influence of the edge to resolve maximum conflicts, a parameterized algorithm in which by deleting a maximum of k edges, the graph can be transformed into a 2-club cluster based on a branching algorithm, and the approach in Integer Linear Programming to find the optimized solution in an integer formulation. …”
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    masterThesis
  12. 132

    Using Educational Data Mining Techniques in Predicting Grade-4 students’ performance in TIMSS International Assessments in the UAE by SHWEDEH, FATEN

    Published 2018
    “…We examined different feature selection methods and classification algorithms to find the best prediction model with the highest accuracy. …”
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  13. 133

    Robust Kalman filter and smoother for errors-in-variables model with observation outliers based on Least-Trimmed-Squares by ALMutawa, Jaafar

    Published 2020
    “…Keywords: Errors-in-variables model, Least-Trimmed- Squares, Kalman filter and smoother, outliers, random search algorithm, subsampling method.…”
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    article
  14. 134

    Stochastic Geometry-Based Model for Dynamic Allocation of Metering Equipment in Spatio-Temporal Expanding Power Grids by Atat, Rachad

    Published 2019
    “…Using the developed model, a multi-year algorithm for the allocation of metering equipment is proposed based on finite horizon dynamic programming, given budgetary and technical constraints on system observability. …”
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    article
  15. 135

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

    Published 2021
    “…Then, the stacked LSTM model, optimized by Tabu search algorithm, uses the residual error correction associated with the original data to produce a point and interval PVPF. …”
  16. 136

    An Intensive and Comprehensive Overview of JAYA Algorithm, its Versions and Applications by Abu Zitar, Raed

    Published 2021
    “…In this review paper, JAYA algorithm, which is a recent population-based algorithm is intensively overviewed. …”
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  17. 137

    A kernelization algorithm for d-Hitting Set by Abu-Khzam, Faisal N.

    Published 2010
    “…For 3-Hitting Set, an arbitrary instance is reduced into an equivalent one that contains at most 5k2+k elements. This kernelization is an improvement over previously known methods that guarantee cubic-order kernels. …”
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    article
  18. 138
  19. 139

    A SIMULATED ANNEALING ALGORITHM FOR THE CLUSTERING PROBLEM by Selim, S.Z.

    Published 2020
    “…We also find optimal parameters values for a specific class of data sets and give recommendations on the choice of parameters for general data sets. …”
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    article
  20. 140

    NEW ALGORITHMS FOR SOLVING THE FUZZY CLUSTERING PROBLEM by Kamel, M.S.

    Published 2020
    “…The performance of the new algorithms is compared with the fuzzy c-means algorithm by testing them on four published data sets. …”
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    article