Synonyme wurden verwendet für: Deep Learning
Suche ohne Synonyme: keywords:("Deep Learning")

1–20 von 533 Ergebnissen
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    Highway crash detection and risk estimation using deep learning

    Huang, Tingting / Wang, Shuo / Sharma, Anuj | Elsevier | 2019
    Schlagwörter: Deep learning

    A novel passenger flow prediction model using deep learning methods

    Liu, Lijuan / Chen, Rung-Ching | Elsevier | 2017
    Schlagwörter: Deep learning

      A novel passenger flow prediction model using deep learning methods

      Liu, Lijuan | Online Contents | 2017
      Schlagwörter: Deep learning

    An efficient realization of deep learning for traffic data imputation

    Duan, Yanjie / Lv, Yisheng / Liu, Yu-Liang et al. | Elsevier | 2016
    Schlagwörter: Deep learning

    Theory-based residual neural networks: A synergy of discrete choice models and deep neural networks

    Wang, Shenhao / Mo, Baichuan / Zhao, Jinhua | Elsevier | 2021
    Schlagwörter: Deep learning

    Deep learning and big data for intelligent transportation : enabling technologies and future trends

    Ahmed, Khaled R. / Hassanien, Aboul Ella | TIBKAT | 2021
    Schlagwörter: Deep learning

    Deep learning for short-term traffic flow prediction

    Polson, Nicholas G. / Sokolov, Vadim O. | Elsevier | 2017
    Schlagwörter: Deep Learning

    Deep-learning for ionogram automatic scaling

    Xiao, Zhuowei / Wang, Jian / Li, Juan et al. | Elsevier | 2020
    Schlagwörter: Deep-learning

    A deep learning approach for real-time crash prediction using vehicle-by-vehicle data

    Basso, Franco / Pezoa, Raúl / Varas, Mauricio et al. | Elsevier | 2021
    Schlagwörter: Deep learning

    A novel method for predicting and mapping the occurrence of sun glare using Google Street View

    Li, Xiaojiang / Cai, Bill Yang / Qiu, Waishan et al. | Elsevier | 2019
    Schlagwörter: Deep learning

    Deep learning of user behavior in shared spaces

    Cheng, Hao / Gottfried Wilhelm Leibniz Universität Hannover | TIBKAT | 2021
    Schlagwörter: Deep learning

      Deep learning of user behavior in shared spaces

      Cheng, Hao / Gottfried Wilhelm Leibniz Universität Hannover | GWLB - Gottfried Wilhelm Leibniz Bibliothek | 2021
      Schlagwörter: Deep learning

    Coupled application of generative adversarial networks and conventional neural networks for travel mode detection using GPS data

    Li, Linchao / Zhu, Jiasong / Zhang, Hailong et al. | Elsevier | 2020
    Schlagwörter: Deep learning

    Sequence to sequence learning with attention mechanism for short-term passenger flow prediction in large-scale metro system

    Hao, Siyu / Lee, Der-Horng / Zhao, De | Elsevier | 2019
    Schlagwörter: Deep learning

    Applications of deep learning in congestion detection, prediction and alleviation: A survey

    Kumar, Nishant / Raubal, Martin | Elsevier | 2021
    Schlagwörter: Deep learning

    Modeling train operation as sequences: A study of delay prediction with operation and weather data

    Huang, Ping / Wen, Chao / Fu, Liping et al. | Elsevier | 2020
    Schlagwörter: Deep learning

    One day ahead prediction of global TEC using Pix2pixhd

    Yang, Ding / Li, Qingfeng / Fang, Hanxian et al. | Elsevier | 2022
    Schlagwörter: Deep learning

    Integrated deep learning and stochastic car-following model for traffic dynamics on multi-lane freeways

    Lee, Seunghyeon / Ngoduy, Dong / Keyvan-Ekbatani, Mehdi | Elsevier | 2019
    Schlagwörter: Deep learning

    DeepTrend 2.0: A light-weighted multi-scale traffic prediction model using detrending

    Dai, Xingyuan / Fu, Rui / Zhao, Enmin et al. | Elsevier | 2019
    Schlagwörter: Deep learning

    Domain adaptation from daytime to nighttime: A situation-sensitive vehicle detection and traffic flow parameter estimation framework

    Li, Jinlong / Xu, Zhigang / Fu, Lan et al. | Elsevier | 2020
    Schlagwörter: Deep learning

    WilDa - Dynamische Wildunfallwarnung unter Verwendung heterogener Verkehrs-, Unfall- und Umweltdaten sowie Big Data Ansätze : Abschlussbericht aller Partner : im Rahmen der Fördermaßnahme: Modernitätsfonds/mFUND/Förderlinie 2 des BMI : Berichtszeitraum: 01.04.2017-30.09.2020

    Technische Hochschule Deggendorf, Institut für Angewandte Informatik / Albert-Ludwigs-Universität Freiburg, Professur für Wildtierökologie und Wildtiermanagement / Technische Hochschule Deggendorf, Institut für Existenzgründung | TIBKAT | 2021
    Schlagwörter: Deep learning

    Train Track Crack Classification Using CNN

    Kumar, Manish / Visalakshi, P. | Trans Tech Publications | 2023
    Schlagwörter: Deep Learning