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1–50 von 608 Ergebnissen
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    Deep Reinforcement Learning for Drone Delivery

    Freier Zugriff
    Guillem Muñoz / Cristina Barrado / Ender Çetin et al. | DOAJ | 2019
    Schlagwörter: deep learning , reinforcement learning , Q-learning

    A Survey of Offline- and Online-Learning-Based Algorithms for Multirotor Uavs

    Freier Zugriff
    Serhat Sönmez / Matthew J. Rutherford / Kimon P. Valavanis | DOAJ | 2024
    Schlagwörter: offline learning , online learning , reinforcement learning , deep learning , machine learning

    A reinforcement learning model for personalized driving policies identification

    Freier Zugriff
    Dimitris M. Vlachogiannis / Eleni I. Vlahogianni / John Golias | DOAJ | 2020
    Schlagwörter: Reinforcement learning , Q-learning , Machine learning

    Using reinforcement learning to minimize taxi idle times

    O’Keeffe, Kevin / Anklesaria, Sam / Santi, Paolo et al. | Taylor & Francis Verlag | 2022
    Schlagwörter: machine learning , reinforcement learning

    Learning through policy transfer? Reviewing a decade of scholarship for the field of transport

    Freier Zugriff
    Glaser, Meredith / Bertolini, Luca / te Brömmelstroet, Marco et al. | Taylor & Francis Verlag | 2022
    Schlagwörter: policy learning , learning

    GPS-based citywide traffic congestion forecasting using CNN-RNN and C3D hybrid model

    Guo, Jingqiu / Liu, Yangzexi / Yang, Qingyan (Ken) et al. | Taylor & Francis Verlag | 2021
    Schlagwörter: deep learning

    Reinforcement Learning for Ramp Control: An Analysis of Learning Parameters

    Freier Zugriff
    Chao Lu / Jie Huang / Jianwei Gong | DOAJ | 2016
    Schlagwörter: reinforcement learning , Q-learning , ent learning

    Online longitudinal trajectory planning for connected and autonomous vehicles in mixed traffic flow with deep reinforcement learning approach

    Cheng, Yanqiu / Hu, Xianbiao / Chen, Kuanmin et al. | Taylor & Francis Verlag | 2023
    Schlagwörter: deep Q-learning , reinforcement learning

    Modular Reinforcement Learning for Autonomous UAV Flight Control

    Freier Zugriff
    Jongkwan Choi / Hyeon Min Kim / Ha Jun Hwang et al. | DOAJ | 2023
    Schlagwörter: reinforcement learning , modular learning , curriculum learning

    Few-Shot traffic prediction based on transferring prior knowledge from local network

    Yu, Lin / Guo, Fangce / Sivakumar, Aruna et al. | Taylor & Francis Verlag | 2023
    Schlagwörter: Few-shot learning , Transfer learning

    A cold-start-free reinforcement learning approach for traffic signal control

    Xiao, Nan / Yu, Liang / Yu, Jinqiang et al. | Taylor & Francis Verlag | 2022
    Schlagwörter: deep learning , reinforcement learning

    Deep reinforcement learning in dynamic positioning control: by rewarding small response of riser angles

    Wang, Fang / Bai, Yong / Bai, Jie et al. | Taylor & Francis Verlag | 2023
    Schlagwörter: Reinforcement learning , Q-learning

    Deep Q learning-based traffic signal control algorithms: Model development and evaluation with field data

    Wang, Hao / Yuan, Yun / Yang, Xianfeng Terry et al. | Taylor & Francis Verlag | 2023
    Schlagwörter: deep reinforcement learning , Q-learning

    Drones Chasing Drones: Reinforcement Learning and Deep Search Area Proposal

    Freier Zugriff
    Moulay A. Akhloufi / Sebastien Arola / Alexandre Bonnet | DOAJ | 2019
    Schlagwörter: deep learning , reinforcement learning

    Energy-Efficient Inference on the Edge Exploiting TinyML Capabilities for UAVs

    Freier Zugriff
    Wamiq Raza / Anas Osman / Francesco Ferrini et al. | DOAJ | 2021
    Schlagwörter: machine learning , deep learning

    Maritime Energy Efficiency in a Sociotechnical System: A Collaborative Learning Synergy via Mediating Technologies

    Freier Zugriff
    Yemao Man / Monica Lundh / Scott MacKinnon | DOAJ | 2018
    Schlagwörter: Learning Synergy , Collaborative Learning Synergy , Collaborative Learning

    Federated Learning for Spanish Ports as an Aid to Digitization

    Freier Zugriff
    González Cancelas Nicoleta / Molina Serrano Beatriz / Soler Flores Francisco | DOAJ | 2021
    Schlagwörter: machine learning , federated learning

    PENGEMBANGAN MEDIA PEMBELAJARAN RODA PUTAR FISIKA UNTUK MENINGKATKAN MOTIVASI BELAJAR SISWA

    Freier Zugriff
    Hamzah Hamzah / Linda Sekar Utami / Zulkarnain Zulkarnain | DOAJ | 2020
    Schlagwörter: learning media development , learning motivation.

    Multiple-UAV Reinforcement Learning Algorithm Based on Improved PPO in Ray Framework

    Freier Zugriff
    Guang Zhan / Xinmiao Zhang / Zhongchao Li et al. | DOAJ | 2022
    Schlagwörter: deep reinforcement learning , curriculum learning

    Road Artery Traffic Light Optimization with Use of the Reinforcement Learning

    Freier Zugriff
    Rok Marsetič / Darja Šemrov / Marijan Žura | DOAJ | 2014
    Schlagwörter: reinforcement learning , Q learning

    SPONTANEOUS CATEGORIZATION AND SELF-LEARNING WITH DEEP AUTOENCODER MODELS

    Freier Zugriff
    Serge Dolgikh | DOAJ | 2019
    Schlagwörter: machine learning , unsupervised learning

    FedRDR: Federated Reinforcement Distillation-Based Routing Algorithm in UAV-Assisted Networks for Communication Infrastructure Failures

    Freier Zugriff
    Jie Li / Anqi Liu / Guangjie Han et al. | DOAJ | 2024
    Schlagwörter: reinforcement learning , federated learning

    Performance evaluation of mode choice models under balanced and imbalanced data assumptions

    Rezaei, Shahrbanoo / Khojandi, Anahita / Haque, Antora Mohsena et al. | Taylor & Francis Verlag | 2022
    Schlagwörter: imbalanced learning , machine learning

    Vision-Based Deep Reinforcement Learning of UAV-UGV Collaborative Landing Policy Using Automatic Curriculum

    Freier Zugriff
    Chang Wang / Jiaqing Wang / Changyun Wei et al. | DOAJ | 2023
    Schlagwörter: deep reinforcement learning , automatic curriculum learning

    Deep Learning-Based Airspeed Estimation System for a Commercial Aircraft

    Freier Zugriff
    Uğur Kılıç | DOAJ | 2023
    Schlagwörter: machine learning , deep learning

    Deep Reinforcement Learning with Corrective Feedback for Autonomous UAV Landing on a Mobile Platform

    Freier Zugriff
    Lizhen Wu / Chang Wang / Pengpeng Zhang et al. | DOAJ | 2022
    Schlagwörter: deep reinforcement learning , interactive learning

    Shipping market forecasting by forecast combination mechanism

    Gao, Ruobin / Liu, Jiahui / Du, Liang et al. | Taylor & Francis Verlag | 2022
    Schlagwörter: machine learning

    Deep Learning Classification of 2D Orthomosaic Images and 3D Point Clouds for Post-Event Structural Damage Assessment

    Freier Zugriff
    Yijun Liao / Mohammad Ebrahim Mohammadi / Richard L. Wood | DOAJ | 2020
    Schlagwörter: deep learning , transfer learning

    Machine Learning for Precision Agriculture Using Imagery from Unmanned Aerial Vehicles (UAVs): A Survey

    Freier Zugriff
    Imran Zualkernan / Diaa Addeen Abuhani / Maya Haj Hussain et al. | DOAJ | 2023
    Schlagwörter: machine learning , deep learning

    Automated Identification and Classification of Plant Species in Heterogeneous Plant Areas Using Unmanned Aerial Vehicle-Collected RGB Images and Transfer Learning

    Freier Zugriff
    Girma Tariku / Isabella Ghiglieno / Gianni Gilioli et al. | DOAJ | 2023
    Schlagwörter: machine learning , transfer learning

    Detecting transportation modes using smartphone data and GIS information: evaluating alternative algorithms for an integrated smartphone-based travel diary imputation

    Liu, Yicong / Miller, Eric / Habib, Khandker Nurul | Taylor & Francis Verlag | 2022
    Schlagwörter: machine learning , tree-based ensemble learning

    Enhancing UAV Aerial Docking: A Hybrid Approach Combining Offline and Online Reinforcement Learning

    Freier Zugriff
    Yuting Feng / Tao Yang / Yushu Yu | DOAJ | 2024
    Schlagwörter: offline reinforcement learning , online reinforcement learning

    Autonomous Unmanned Aerial Vehicles in Bushfire Management: Challenges and Opportunities

    Freier Zugriff
    Shouthiri Partheepan / Farzad Sanati / Jahan Hassan | DOAJ | 2023
    Schlagwörter: machine learning , deep learning

    UAV Path Planning Optimization Strategy: Considerations of Urban Morphology, Microclimate, and Energy Efficiency Using Q-Learning Algorithm

    Freier Zugriff
    Anderson Souto / Rodrigo Alfaia / Evelin Cardoso et al. | DOAJ | 2023
    Schlagwörter: machine learning , Q-learning

    Microscopic modeling of cyclists on off-street paths: a stochastic imitation learning approach

    Mohammed, Hossameldin / Sayed, Tarek / Bigazzi, Alexander | Taylor & Francis Verlag | 2022
    Schlagwörter: machine learning

    A data-driven lane-changing behavior detection system based on sequence learning

    Gao, Jun / Murphey, Yi Lu / Yi, Jiangang et al. | Taylor & Francis Verlag | 2022
    Schlagwörter: sequence learning

    Big data and artificial intelligence in the maritime industry: a bibliometric review and future research directions

    Freier Zugriff
    Munim, Ziaul Haque / Dushenko, Mariia / Jimenez, Veronica Jaramillo et al. | Taylor & Francis Verlag | 2020
    Schlagwörter: machine learning

    Testing and enhancing spatial transferability of artificial neural networks based travel behavior models

    Koushik, Anil NP / Manoj, M / Nezamuddin, N et al. | Taylor & Francis Verlag | 2023
    Schlagwörter: Machine learning , Transfer learning

    Deep Neural Networks and Transfer Learning for Food Crop Identification in UAV Images

    Freier Zugriff
    Robert Chew / Jay Rineer / Robert Beach et al. | DOAJ | 2020
    Schlagwörter: machine learning , deep learning

    Task Allocation of Multiple Unmanned Aerial Vehicles Based on Deep Transfer Reinforcement Learning

    Freier Zugriff
    Yongfeng Yin / Yang Guo / Qingran Su et al. | DOAJ | 2022
    Schlagwörter: deep reinforcement learning , transfer learning

    Leveraging reinforcement learning for dynamic traffic control: A survey and challenges for field implementation

    Freier Zugriff
    Yu Han / Meng Wang / Ludovic Leclercq | DOAJ | 2023
    Schlagwörter: Reinforcement learning , Learning cost

    A bibliometric analysis and review on reinforcement learning for transportation applications

    Li, Can / Bai, Lei / Yao, Lina et al. | Taylor & Francis Verlag | 2023
    Schlagwörter: Machine learning

    A Drone-Powered Deep Learning Methodology for High Precision Remote Sensing in California’s Coastal Shrubs

    Freier Zugriff
    Jon Detka / Hayley Coyle / Marcella Gomez et al. | DOAJ | 2023
    Schlagwörter: deep learning , machine learning

    Application of algorithmic models of machine learning to the freight transportation process

    Freier Zugriff
    Viktoriia Kotenko | DOAJ | 2022
    Schlagwörter: machine learning , algorithmic models of machine learning

    Review of intelligent detection and statistical methods of wild animals in UAV aerial photography

    Freier Zugriff
    ZHU Ninghua / ZHENG Jiangbin / ZHANG Yang | DOAJ | 2023
    Schlagwörter: deep learning , transfer learning

    Uplink Throughput Maximization in UAV-Aided Mobile Networks: A DQN-Based Trajectory Planning Method

    Freier Zugriff
    Yuping Lu / Ge Xiong / Xiang Zhang et al. | DOAJ | 2022
    Schlagwörter: deep reinforcement learning , Q-learning

    A Study of Correlation between Fishing Activity and AIS Data by Deep Learning

    Freier Zugriff
    Kuan Yu Shen / Ying Jui Chu / Shwu Jing Chang et al. | DOAJ | 2020
    Schlagwörter: deep learning framework , deep learning , learning methods

    Convolutional neural network for detecting railway fastener defects using a developed 3D laser system

    Zhan, You / Dai, Xianxing / Yang, Enhui et al. | Taylor & Francis Verlag | 2021
    Schlagwörter: deep learning

    SATP-GAN: self-attention based generative adversarial network for traffic flow prediction

    Zhang, Liang / Wu, Jianqing / Shen, Jun et al. | Taylor & Francis Verlag | 2021
    Schlagwörter: reinforcement learning

    A data-driven approach to characterize the impact of connected and autonomous vehicles on traffic flow

    Parsa, Amir Bahador / Shabanpour, Ramin / Mohammadian, Abolfazl (Kouros) et al. | Taylor & Francis Verlag | 2021
    Schlagwörter: machine learning