Showing 721 - 740 results of 2,507 for search '"Deep Learning"', query time: 0.08s Refine Results
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    Evaluation of Spatial and Temporal Performance of Deep Learning Models for Travel Demand Forecasting: Application to Bike-Sharing Demand Forecasting by Jaehyung Lee, Jinhee Kim

    Published 2022-01-01
    “…Deep learning approaches are widely employed for forecasting short-term travel demand to respond to real-time demand. …”
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    Identification of Intracranial Germ Cell Tumors Based on Facial Photos: Exploratory Study on the Use of Deep Learning for Software Development by Yanong Li, Yixuan He, Yawei Liu, Bingchen Wang, Bo Li, Xiaoguang Qiu

    Published 2025-01-01
    “…MethodsA multicenter, phased approach was adopted for the development and validation of a deep learning model, GVisageNet, dedicated to the screening of midline brain tumors from normal controls (NCs) and iGCTs from other midline brain tumors. …”
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    Deep learning models for analysis of non‐destructive evaluation data to evaluate reinforced concrete bridge decks: A survey by Dayakar Naik Lavadiya, Sattar Dorafshan

    Published 2025-01-01
    “…Abstract Application of deep learning (DL) for automatic condition assessment of bridge decks has been on the raise in the last few years. …”
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    A deep learning-based method for the intelligent identification of the quantity of coals flushed out during borehole hydraulic flushing by Xiaojun LI, Mingyang ZHAO, Miao LI

    Published 2025-01-01
    “…The results of this study provide a technical and practical foundation for the integration of the YOLO series of algorithms with other deep learning techniques and its wide applications. Besides, these results serve as a valuable reference for achieving intelligent advances in complex underground coal mine scenarios such as drilling sites for gas drainage.…”
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    A comprehensive review on early detection of drusen patterns in age-related macular degeneration using deep learning models by Kiruthika M, Malathi G

    Published 2025-02-01
    “…Furthermore, the existing models have difficulty in correctly predicting the drusen regions because of the resolution of fundus images, for which a solution is proposed as a model based on deep learning. Performance can be optimized by employing both local and global knowledge when AMD issues are still in the early phases. …”
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    Improving WRF-Chem PM2.5 predictions by combining data assimilation and deep-learning-based bias correction by Xingxing Ma, Hongnian Liu, Zhen Peng

    Published 2025-01-01
    “…Four parallel experiments were conducted during winter 2019: a control experiment directly forecasted by WRF-Chem (experiment name: WRF-Chem); an experiment that assimilated in situ observations based on the GSI (Gridpoint Statistical Interpolation) system (WRF-Chem_DA); an experiment with deep-learning-based BC (WRF-Chem_BC); and an experiment considering the combination of DA on the initial conditions and BC (WRF-Chem_DA_BC). …”
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