Synonyme wurden verwendet für: learning
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81–100 von 180 Ergebnissen
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    Reliability of Jack-up against Punch-through using Failure State Intelligent Recognition Technique

    Lyu, Tao / Xu, Changhang / Chen, Guoming et al. | Springer Verlag | 2019
    Schlagwörter: deep learning

    Deep Learning-Based Prediction of Fire Occurrence with Hydroclimatic Condition and Drought Phase over South Korea

    Sung, Jang Hyun / Ryu, Young / Seong, Kee-Won | Springer Verlag | 2022
    Schlagwörter: Deep learning

    Prediction of wing buffet pressure loads using a convolutional and recurrent neural network framework

    Freier Zugriff
    Zahn, R. / Weiner, A. / Breitsamter, C. | Springer Verlag | 2024
    Schlagwörter: Deep learning

    Spreadsheet Calculators for Stability Number of Armor Units Based on Artificial Neural Network Models

    Kim, In-Chul / Suh, Kyung-Duck | Springer Verlag | 2019
    Schlagwörter: machine learning

    Machine Learning Based Compressive Strength Prediction Model for CFRP-confined Columns

    Yu, Yong / Hu, Tianyu | Springer Verlag | 2024
    Schlagwörter: Machine learning

    Shrinkage Crack Detection in Expansive Soil using Deep Convolutional Neural Network and Transfer Learning

    Andrushia, A. Diana / Neebha, T. Mary / Umadevi, S. et al. | Springer Verlag | 2022
    Schlagwörter: Deep 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

    Vibration-based Damage Detection in Bridges via Machine Learning

    Sun, Shuang / Liang, Li / Li, Ming et al. | Springer Verlag | 2018
    Schlagwörter: machine learning

    Presenting a Hybrid Scheme of Machine Learning Combined with Metaheuristic Optimizers for Predicting Final Cost and Time of Project

    Bakhshi, Reza / Moradinia, Sina Fard / Jani, Rasool et al. | Springer Verlag | 2022
    Schlagwörter: Hybrid machine learning

    Development of Data-based Hierarchical Learning Model for Predicting Condition Rating of Bridge Members over Time

    Choi, Youngjin / Kong, Jungsik | Springer Verlag | 2023
    Schlagwörter: Deep learning

    Prediction of lateral confinement coefficient in reinforced concrete columns using M5′ machine learning method

    Naeej, Mojtaba / Bali, Meysam / Naeej, Mohamad Reza et al. | Springer Verlag | 2013
    Schlagwörter: M5’ machine learning method

    Metaheuristic Optimization of Reinforced Concrete Footings

    Nigdeli, Sinan Melih / Bekdaş, Gebrail / Yang, Xin-She | Springer Verlag | 2018
    Schlagwörter: teaching-learning based optimization

    Investigation of Meta-heuristics Algorithms in ANN Streamflow Forecasting

    Wei, Yaxing / Hashim, Huzaifa / Chong, K. L. et al. | Springer Verlag | 2023
    Schlagwörter: Machine learning

    Evaluation of the aircraft fuel economy using advanced statistics and machine learning

    Freier Zugriff
    Baumann, S. / Neidhardt, T. / Klingauf, U. | Springer Verlag | 2021
    Schlagwörter: Machine learning

    Structural Deformation Sensing Based on Distributed Optical Fiber Monitoring Technology and Neural Network

    Hou, Gong-Yu / Li, Zi-Xiang / Wang, Kai-Di et al. | Springer Verlag | 2021
    Schlagwörter: Machine learning

    Crack Detection of the Urban Underground Utility Tunnel Based on Residual Feature Pyramid Attention Network

    Zhou, Yuan / Li, Chengwei / Wang, Shoubin et al. | Springer Verlag | 2024
    Schlagwörter: Residual learning

    A Hybrid Feature Selection-multidimensional LSTM Framework for Deformation Prediction of Super High Arch Dams

    Cao, Enhua / Bao, Tengfei / Li, Hui et al. | Springer Verlag | 2022
    Schlagwörter: Deep 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

    Semi-Supervised Land Cover Classification of Remote Sensing Imagery Using CycleGAN and EfficientNet

    Kwak, Taehong / Kim, Yongil | Springer Verlag | 2023
    Schlagwörter: Semi-supervised learning