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Showing 81 - 100 results of 153 for search '(((( element method algorithm ) OR ( recent data algorithm ))) OR ( element sampling algorithm ))', query time: 0.11s Refine Results
  1. 81

    The Role of Machine Learning in Diagnosing Bipolar Disorder: Scoping Review by Zainab Jan (17306614)

    Published 2021
    “…Magnetic resonance imaging data were most commonly used for classifying bipolar patients compared to other groups (11, 34%), whereas microarray expression data sets and genomic data were the least commonly used. …”
  2. 82

    An Optimal Approach for Assessing Weibull Parameters and Wind Power Potential for Six Coastal Cities in Pakistan by Ghulam Abbas (764241)

    Published 2024
    “…In this research, we have ameliorated the performance of the recently-introduced novel energy pattern factor method (NEPFM) via a direct search algorithm, i.e., simplex search algorithm (SSA). …”
  3. 83
  4. 84

    Recovery of business intelligence systems by Haraty, Ramzi A.

    Published 2018
    “…The efficiency of the data recovery algorithm is substantial for e-healthcare systems. …”
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  5. 85

    Corona power loss computation in bundled bipolar conductors by Al-Hamouz, Z.M.

    Published 2000
    “…In this paper, a finite element (FE) based algorithm devoted for the computation of the corona current and hence the corona power loss associated with bundled bipolar high voltage direct current (HVDC) conductors is presented. …”
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  6. 86

    Newton-Raphson based adaptive inverse control scheme for tracking of nonlinear dynamic plants by Shafiq, M.

    Published 2006
    “…The U-model is utilized to design an adaptive inverse controller by using a simple root-solving algorithm of Newton-Raphson. The synergy of U-model with AIC structure has provided an effective and straight forward method for adaptive tracking of nonlinear plants. …”
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  7. 87

    Simulations of the penetration of 6061-T6511 aluminum targets by spherical-nosed VAR 4340 steel projectiles by Warren, Thomas

    Published 2000
    “…In the context of an analysis code, this approximation eliminates the need for discretizing the target as well as the need for a contact algorithm. Thus, this method substantially reduces the computer time and memory requirements. …”
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  8. 88

    A Fully Optical Laser Based System for Damage Detection and Localization in Rail Tracks Using Ultrasonic Rayleigh Waves: A Numerical and Experimental Study by Masurkar, Faeez

    Published 2022
    “…Further, As the quality of received signals differs at different sensing points as a result of the surface conditions of the specimen, the Self Adaptive Smart Algorithm (SASA) method was adopted to filter out the noise and accurately pinpoint the defect reflected wave packet which ultimately aids in better detection and localization. …”
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  9. 89

    Vibration suppression in a cantilever beam using a string-type vibration absorber by Issa, Jimmy S.

    Published 2017
    “…The string is rigidly connected to the fixed end of the beam and through a spring and damper to a second point on the beam. The finite element method is used to model the system and a reduced order model is obtained through modal reduction performed on both the string and the beam. …”
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  10. 90

    Topology and parameter estimation in power systems through inverter-based broadband stimulations by Margossian, Harag

    Published 2015
    “…To test its capabilities, the performance of this algorithm is evaluated on a small-scale test system.…”
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  11. 91
  12. 92

    A modified coronavirus herd immunity optimizer for capacitated vehicle routing problem by Abu Zitar, Raed

    Published 2021
    “…Due to the complex nature of the capacitated vehicle routingproblem, metaheuristic optimization algorithms are widely used for tackling this type of challenge.Coronavirus Herd Immunity Optimizer (CHIO) is a recent metaheuristic population-based algorithm thatmimics the COVID-19 herd immunity treatment strategy. …”
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  13. 93

    Benchmark on a large cohort for sleep-wake classification with machine learning techniques by Joao Palotti (8479842)

    Published 2019
    “…In this work, we processed the data of the recently published Multi-Ethnic Study of Atherosclerosis (MESA) Sleep study to have both PSG and actigraphy data synchronized. …”
  14. 94

    Application of Metastructures for Targeted Low-Frequency Vibration Suppression in Plates by Ratiba F. Ghachi (14152455)

    Published 2022
    “…The thin plate and the zigzag cutouts are modelled using the finite element method, and the optimal location and optimal tip mass of the zigzag cutouts are obtained using genetic algorithms through iterative simulations. …”
  15. 95
  16. 96

    A conjugate self-organizing migration (CSOM) and reconciliate multi-agent Markov learning (RMML) based cyborg intelligence mechanism for smart city security by S. Shitharth (12017480)

    Published 2023
    “…Here, the Quantized Identical Data Imputation (QIDI) mechanism is implemented at first for data preprocessing and normalization. …”
  17. 97

    A Full-System Approach of the Elastohydrodynamic Line/Point Contact Problem by Habchi, Wassim

    Published 2008
    “…The use of the finite element method allows the use of variable unstructured meshing and different types of elements within the same model which leads to a reduced size of the problem. …”
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  18. 98

    Estimation of power grid topology parameters through pilot signals by Hargossian, H.

    Published 2016
    “…Pilot voltage stimulations are injected from distributed generators and the induced currents effects are measured at several nodes in the system. The measured data is evaluated through correlation, and a weighed least-square algorithm, applied to the network’s dynamic model, estimates those unknown parameters and provides an accurate snapshot of the power network topology. …”
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  19. 99
  20. 100

    Decomposition-based wind power forecasting models and their boundary issue: An in-depth review and comprehensive discussion on potential solutions by Yinsong Chen (16685508)

    Published 2022
    “…Decomposition-based hybrid models have gained significant popularity in recent years. These methods generally disaggregate the original time series data into sub-time-series with better stationarity, and then the target data is predicted based on the sub-series. …”