Synonyme wurden verwendet für: learning
Suche ohne Synonyme: keywords:(learning)

41–60 von 84 Ergebnissen
|

    Interpretability Analysis of Shear Capacity in Reinforced Recycled Aggregate Concrete Beams Using Tree Models

    Li, Li / Qin, Yapeng / Zhang, Yang et al. | Springer Verlag | 2024
    Schlagwörter: Machine learning

    Internal Defect Detection of Structures Based on Infrared Thermography and Deep Learning

    Deng, Lu / Zuo, Hui / Wang, Wei et al. | Springer Verlag | 2023
    Schlagwörter: Deep learning

    Inter-modality Face Recognition

    Freier Zugriff
    Lin, Dahua / Tang, Xiaoou | Springer Verlag | 2006
    Schlagwörter: Learning Objective

    Integrated YOLO and CNN Algorithms for Evaluating Degree of Walkway Breakage

    Choi, Min Je / Ku, Dong Gyun / Lee, Seung Jae | Springer Verlag | 2022
    Schlagwörter: Image deep learning

    Hybrid Prediction Model of Engineering Classification of Slope Rock Mass Based on DCWA-EO-AdaBoost Model and BQ Method

    Wang, Han / Gao, Yongtao / Xie, Yongsheng et al. | Springer Verlag | 2024
    Schlagwörter: Machine learning

    Generalized Linear Models to Identify the Impact of Road Geometric Design Features on Crash Frequency in Rural Roads

    Khedher, Moataz Bellah Ben / Yun, Dukgeun | Springer Verlag | 2022
    Schlagwörter: Machine learning

    Four Decades of Computing in Civil Engineering

    Adeli, Hojjat | Springer Verlag | 2019
    Schlagwörter: machine learning

    Flood Hazard Rating Prediction for Urban Areas Using Random Forest and LSTM

    Kim, Hyun Il / Kim, Byung Hyun | Springer Verlag | 2020
    Schlagwörter: Machine learning

    Extraction of Main Urban Roads from High Resolution Satellite Images by Machine Learning

    Wang, Yanqing / Tian, Yuan / Tai, Xianqing et al. | Springer Verlag | 2006
    Schlagwörter: Machine Learning

    Explainable Boosting Machine for Predicting Wind Shear-Induced Aircraft Go-around based on Pilot Reports

    Khattak, Afaq / Chan, Pak-wai / Chen, Feng et al. | Springer Verlag | 2023
    Schlagwörter: Machine learning

    Evaluation of Machine Learning Algorithms for Classification of Infrastructure Elements in Complex Structures

    Yilmaz, Yalcin / Bayrak, Onur Can / Soycan, Arzu | Springer Verlag | 2024
    Schlagwörter: Machine learning

    Evaluating Multi-class Multiple-Instance Learning for Image Categorization

    Xu, Xinyu / Li, Baoxin | Springer Verlag | 2007
    Schlagwörter: Multi-Class Multiple-Instance Learning

    Ensemble-based Deep Learning Approach for Performance Improvement of BIM Element Classification

    Yu, Young Su / Kim, Si Hyun / Lee, Won Bok et al. | Springer Verlag | 2023
    Schlagwörter: Deep learning , Ensemble learning

    Enhancing Vibration-based Damage Assessment with 1D-CNN: Parametric Studies and Field Applications

    Park, Soyeon / Kim, Sunjoong | Springer Verlag | 2024
    Schlagwörter: Deep Learning

    Effects of user equilibrium assumptions on network traffic pattern

    Kim, Hyunmyung / Oh, Jun-Seok / Jayakrishnan, R. | Springer Verlag | 2009
    Schlagwörter: inductive learning

    Effectiveness of an Immersive VR System for Construction Site Planning Education

    Wang, Kun-Chi / Hsu, Liang-Yu | Springer Verlag | 2024
    Schlagwörter: Learning effect evaluation

    Drivers' adaptive expectations formation in nonstationary traffic environment

    Do, Myungsik | Springer Verlag | 2001
    Schlagwörter: coefficient of learning

    Do Perceptions of Hydrogen Energy Effect on Vehicle Preference? A Learning-Based Model Approach

    Kim, Woojin / Kim, Junghwa / Jang, Jeong Ah et al. | Springer Verlag | 2023
    Schlagwörter: Machine learning

    Disclosing the Impact of Micro-level Environmental Characteristics on Dockless Bikeshare Trip Volume: A Case Study of Ithaca

    Song, Qiwei / Li, Wenjing / Li, Jintai et al. | Springer Verlag | 2023
    Schlagwörter: Machine learning

    Development of Point Cloud Data-Denoising Technology for Earthwork Sites Using Encoder-Decoder Network

    Choi, Yeongjun / Park, Suyeul / Kim, Seok | Springer Verlag | 2022
    Schlagwörter: Deep learning