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Showing 1 - 20 results of 66 for search '(( test mining algorithm ) OR ((( element method algorithm ) OR ( element data algorithms ))))', query time: 0.12s Refine Results
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    Bird’s Eye View feature selection for high-dimensional data by Samir Brahim Belhaouari (16855434)

    Published 2023
    “…This approach is inspired by the natural world, where a bird searches for important features in a sparse dataset, similar to how a bird search for sustenance in a sprawling jungle. BEV incorporates elements of Evolutionary Algorithms with a Genetic Algorithm to maintain a population of top-performing agents, Dynamic Markov Chain to steer the movement of agents in the search space, and Reinforcement Learning to reward and penalize agents based on their progress. …”
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    Application of updated joint detection algorithm for the analysis of drilling parameters of roof bolters in multiple joints conditions by Liu, Wenpeng

    Published 2017
    “…Fletcher & Co. facility. This paper reviews testing procedures, data analysis, updated algorithms used for joint detection, and discusses the latest round of testing in samples with simulated joints at various angles along the borehole.…”
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    The Effects of Data Mining on Small Businesses in Dubai by AlMutawa, Rasha

    Published 2011
    “…While there are numerous studies on the best data mining models and their uses, even on certain industries, this study focuses on the applications more than the algorithms and models and their usefulness for small businesses specifically. …”
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    A reduced model for phase-change problems with radiation using simplified PN approximations by Belhamadia, Youssef

    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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    article
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    Data mining approach to predict student's selection of program majors by SIDDARTHA, SHARMILA

    Published 2019
    “…The approach includes a methodology to manage data mining projects, sampling techniques to handle imbalanced data and multiclass data, a set of classification algorithms to predict and measures to evaluate performance of models. …”
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    Monitoring Bone Density Using Microwave Tomography of Human Legs: A Numerical Feasibility Study by Alkhodari, Mohanad Ahmed

    Published 2021
    “…This study was performed using an in-house finite-element method contrast source inversion algorithm (FEM-CSI). …”
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    article
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    Nonlinear analysis of shell structures using image processing and machine learning by M.S. Nashed (16392961)

    Published 2023
    “…The proposed approach can be significantly more efficient than training a machine learning algorithm using the raw numerical data. To evaluate the proposed method, two different structures are assessed where the training data is created using nonlinear finite element analysis. …”
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    The Relation Between Respiratory & Acute Coronary Syndrome Using Data Mining Techniques by DOULEH, HANI ABDULLAH YOSEF ABU

    Published 2018
    “…The purpose of this study is to diagnose acute coronary syndrome using the widely available respiratory diagnosing tools and laboratory test results using data mining classification techniques (Decision tree, Gradient boosted tree, Neural Network, and Naïve Bias). …”
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    Design of adaptive arrays based on element position perturbations by Dawoud, M.M.

    Published 1993
    “…The main advantage of using this technique over the other commonly used methods is that the amplitudes and phases of the array elements can be used mainly to steer the main beam towards the desired signal. …”
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    article
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    Correlation Clustering with Overlaps by Fakhereldine, Amin

    Published 2020
    “…Moreover, we allow the new vertex splitting operation, which allows the resulting clusters to overlap. 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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