Showing 81 - 100 results of 250 for search '(((( fluent modeling algorithm ) OR ( element method algorithm ))) OR ( level using algorithm ))', query time: 0.13s Refine Results
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    Acoustic Based Localization of Partial Discharge Inside Oil-Filled Transformers by Hamidreza Besharatifard (16904823)

    Published 2022
    “…<p dir="ltr">This paper addresses the localization of Partial Discharge through a 3D Finite Element Method analysis of acoustic wave propagation inside a 3-phase 35kV transformer with the help of COMSOL Multiphysics software. …”
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    Simultaneous stabilisation of power systems using geneticalgorithms by Abdel-Magid, Y.L.

    Published 1997
    “…The paper considers the simultaneous stabilisation of a power system over a wide range of operating conditions via a single power system stabiliser using genetic algorithms. A power system operating at various load levels is treated as a finite set of plants. …”
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    article
  9. 89

    Design Optimization of Inductive Power Transfer Systems Considering Bifurcation and Equivalent AC Resistance for Spiral Coils by Alireza Namadmalan (16864236)

    Published 2020
    “…Equivalent AC resistance of spiral coils is modeled based on eddy currents simulations using Finite Element Method (FEM) and Maxwell simulator. Based on the FEM simulations, a new approximation method using separation of variables is proposed as a function of spiral coil's main parameters. …”
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    A Survey of Data Clustering Techniques by Sobeh, Salma

    Published 2023
    “…This survey examines seven widely recognized clustering techniques, namely k-means, G-means, DBSCAN, Agglomerative hierarchical clustering, Two-stage density (DBSCAN and k-means) algorithm, Two-levels (DBSCAN and hierarchical) clustering algorithm, and Two-stage MeanShift and K-means clustering algorithm and compares them over a real dataset - The Blockchain dataset, including prominent cryptocurrencies like Binance, Bitcoin, Doge, and Ethereum, under several metrics such as silhouette coefficient, Calinski-Harabasz, Davies-Bouldin Index, time complexity, and entropy.…”
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    masterThesis
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    Efficient Seismic Volume Compression using the Lifting Scheme by Khene, M. F.

    Published 2000
    “…A separable 3-D discrete wavelet transform (DWT) using long biorthogonal filters is used. The computation efficiency of the DWT is improved by factoring the wavelet filters using the lifting scheme. …”
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    article
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    Cyberbullying Detection in Arabic Text using Deep Learning by ALBAYARI, REEM RAMADAN SA’ID

    Published 2023
    “…Therefore, this study aims to evaluate several versions of Recurrent Neural Networks (RNNs) and Feedforward Neural Networks (FNNs) for detecting cyberbullying in the Arabic language. Although these algorithms are widely used in text classification and outperform the performance of classical classifiers, many have been extensively investigated in other domains such as sentiment analysis and dialect identification, as well as cyberbullying detection in English text. …”
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    Modulation With Metaheuristic Approach for Cascaded-MPUC49 Asymmetrical Inverter With Boosted Output by Kaif Ahmed Lodi (16855518)

    Published 2020
    “…For the calculation of optimum angles, a meta-heuristic based Genetic Algorithm (GA) technique is employed. The generation of 49-level output requires 24 transitions in one quarter of a cycle. …”
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    FPGA-Based Network Traffic Classification Using Machine Learning by Elnawawy, Mohammed

    Published 2020
    “…Classification approaches based on machine learning techniques have shown promising results with high levels of accuracy. In this paper, the suitability of packet-level and flow-level features is validated using stepwise regression and random forest feature selection. …”
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    article
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    An efficient approach for textual data classification using deep learning by Abdullah Alqahtani (7128143)

    Published 2022
    “…<p dir="ltr">Text categorization is an effective activity that can be accomplished using a variety of classification algorithms. In machine learning, the classifier is built by learning the features of categories from a set of preset training data. …”
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