Showing 141 - 160 results of 308 for search '(( elements method algorithm ) OR ((( time processing algorithm ) OR ( relevant data algorithm ))))', query time: 0.12s Refine Results
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    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. …”
  3. 143

    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
    “…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. …”
  4. 144

    A Quasi-Oppositional Method for Output Tracking Control by Swarm-Based MPID Controller on AC/HVDC Interconnected Systems With Virtual Inertia Emulation by Iman M. Hosseini Naveh (16891482)

    Published 2021
    “…Also the proposed fitness function, as deviation characteristics of the step response in MIMO transfer function in virtual inertia emulation based HVDC model, is compared with integral time absolute error (ITAE), as the standard performance index in the optimization process. …”
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    Adaptive Secure Pipeline for Attacks Detection in Networks with set of Distribution Hosts by ALSHAMSI, SUROUR

    Published 2022
    “…So far none addresses the use of Threat Intelligence (IT) data in Ensemble Learning algorithms to improve the detection process, nor does it work as a function of time, that is, taking into account what happens on the network in a limited time interval. …”
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    Enhancing Breast Cancer Diagnosis With Bidirectional Recurrent Neural Networks: A Novel Approach for Histopathological Image Multi-Classification by Rajendra Babu Chikkala (22330876)

    Published 2025
    “…<p dir="ltr">In recent years, deep learning methods have dramatically improved medical image analysis, though earlier models faced difficulties in capturing intricate spatial and contextual details. …”
  9. 149

    An Ultrafast Maximum Power Point Setting Scheme for Photovoltaic Arrays Using Model Parameter Identification by Zhaohui Cen (17217391)

    Published 2015
    “…In order to separate the search algorithms from converter operation, a model parameter identification approach is presented to estimate insolation conditions of each PV panel and build a real-time overallP-Icurve of PV arrays. …”
  10. 150

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

    Published 2021
    “…One thousand distribution transformers' real data from Spain for three years are used to demonstrate the performance of the proposed methodology with a trade-off between accuracy and processing time.</p><h2>Other Information</h2><p>Published in: IEEE Access<br>License: <a href="https://creativecommons.org/licenses/by/4.0/legalcode" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1109/access.2021.3072609" target="_blank">https://dx.doi.org/10.1109/access.2021.3072609</a></p>…”
  11. 151

    Deep Learning-Based Coding Strategy for Improved Cochlear Implant Speech Perception in Noisy Environments by Billel Essaid (22047578)

    Published 2025
    “…<p dir="ltr">Automatic speech recognition (ASR) and speech enhancement are essential tools in modern life, aiding not only in machine interaction but also in supporting individuals with hearing impairments. These processes begin with capturing speech in analog form and applying signal processing algorithms to ensure compatibility with devices like cochlear implants (CIs). …”
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    Multi-Agent Meta Reinforcement Learning for Reliable and Low-Latency Distributed Inference in Resource-Constrained UAV Swarms by Marwan Dhuheir (19170898)

    Published 2025
    “…A key requirement in these applications is minimizing the latency of data processing, particularly for time-sensitive tasks like image classification of IIoT device data. …”
  14. 154

    Enhancing Healthcare Systems With Deep Reinforcement Learning: Insights Into D2D Communications and Remote Monitoring by Zina Chkirbene (16869987)

    Published 2024
    “…The core of DRLLVT is its novel algorithm that leverages Deep Reinforcement Learning (DRL) to dynamically adapt to changing environmental conditions, facilitating real-time decisions that consider node capacities, latency, and the overall network dynamics. …”
  15. 155

    Frontiers and trends of supply chain optimization in the age of industry 4.0: an operations research perspective by Zhitao Xu (2426023)

    Published 2024
    “…It contributes to the literature by identifying the four OR innovations to typify the recent advances in SC optimization: new modeling conditions, new inputs, new decisions, and new algorithms. Furthermore, we recommend four promising research avenues in this interplay: (1) incorporating new decisions relevant to data-enabled SC decisions, (2) developing data-enabled modeling approaches, (3) preprocessing parameters, and (4) developing data-enabled algorithms. …”
  16. 156

    Machine Learning–Based Approach for Identifying Research Gaps: COVID-19 as a Case Study by Alaa Abd-alrazaq (17058018)

    Published 2024
    “…</p><h3>Methods</h3><p dir="ltr">We conducted an analysis to identify research gaps in COVID-19 literature using the COVID-19 Open Research (CORD-19) data set, which comprises 1,121,433 papers related to the COVID-19 pandemic. …”
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    Deep Learning-Based Short-Term Load Forecasting Approach in Smart Grid With Clustering and Consumption Pattern Recognition by Dabeeruddin Syed (16864260)

    Published 2021
    “…The proposed approach delivers an improvement of around 44% in training time while maintaining accuracy using single-core processing as compared to non-clustering models.…”
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