Showing 641 - 660 results of 2,507 for search '"Deep Learning"', query time: 0.08s Refine Results
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    A Deep-Learning Method for Remaining Useful Life Prediction of Power Machinery via Dual-Attention Mechanism by Fan Wang, Aihua Liu, Chunyang Qu, Ruolan Xiong, Lu Chen

    Published 2025-01-01
    “…Remaining useful life (RUL) prediction is a cornerstone of Prognostic and Health Management (PHM) for power machinery, playing a crucial role in ensuring the reliability and safety of these critical systems. In recent years, deep learning techniques have shown great promise in RUL prediction, providing more reliable and accurate outcomes. …”
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    FundusNet: A Deep-Learning Approach for Fast Diagnosis of Neurodegenerative and Eye Diseases Using Fundus Images by Wenxing Hu, Kejie Li, Jake Gagnon, Ye Wang, Talia Raney, Jeron Chen, Yirui Chen, Yoko Okunuki, Will Chen, Baohong Zhang

    Published 2025-01-01
    “…In this study, we present FundusNet, a deep-learning model trained on fundus images, for rapid and cost-effective diagnosis of neurodegenerative diseases. …”
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  8. 648

    Fluorescence Lifetime Endoscopy with a Nanosecond Time-Gated CAPS Camera with IRF-Free Deep Learning Method by Pooria Iranian, Thomas Lapauw, Thomas Van den Dries, Sevada Sahakian, Joris Wuts, Valéry Ann Jacobs, Jef Vandemeulebroucke, Maarten Kuijk, Hans Ingelberts

    Published 2025-01-01
    “…In this work, the capabilities of an endoscopic lifetime imaging system are demonstrated using a rigid endoscope involving various phantoms and an IRF-free deep learning-based method with only 6-time points. The results show that this application’s fluorescence lifetime image has better lifetime uniformity and precision with 6-time points than the conventional methods.…”
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    An Improved Demand Forecasting Model Using Deep Learning Approach and Proposed Decision Integration Strategy for Supply Chain by Zeynep Hilal Kilimci, A. Okay Akyuz, Mitat Uysal, Selim Akyokus, M. Ozan Uysal, Berna Atak Bulbul, Mehmet Ali Ekmis

    Published 2019-01-01
    “…For this purpose, historical data can be analyzed to improve demand forecasting by using various methods like machine learning techniques, time series analysis, and deep learning models. In this work, an intelligent demand forecasting system is developed. …”
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    Mapping global annual urban land cover fractions (2001–2020) derived with multi-objective deep learning by Haoyu Wang, Qian Wang, Xiuyuan Zhang, Shihong Du, Lubin Bai, Shuping Xiong

    Published 2025-02-01
    “…Accordingly, this study proposes a novel deep learning unmixing algorithm, Normalized Non-negative Multi-objective Residual T-ConvLSTM (NNMRT) model, with strong encoding and recognition capacities for extracting GAULCF. …”
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    A Deep Learning Based Estimator for Light Flavour Elliptic Flow in Heavy Ion Collisions at LHC Energies by Barnaföldi Gergely Gábor, Mallick Neelkamal, Prasad Suraj, Sahoo Raghunath, Mishra Aditya Nath

    Published 2025-01-01
    “…We developed a deep learning feed-forward network for estimating elliptic flow (v2) coefficients in heavy-ion collisions from RHIC to LHC energies. …”
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    Annotation-free deep learning for predicting gene mutations from whole slide images of acute myeloid leukemia by Bo-Han Wei, Xavier Cheng-Hong Tsai, Kuo-Jui Sun, Min-Yen Lo, Sheng-Yu Hung, Wen-Chien Chou, Hwei-Fang Tien, Hsin-An Hou, Chien-Yu Chen

    Published 2025-02-01
    “…Abstract The rapid development of deep learning has revolutionized medical image processing, including analyzing whole slide images (WSIs). …”
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