[HTML][HTML] Deep hybrid learning framework for spatiotemporal crash prediction using big traffic data

MT Kashifi, M Al-Turki, AW Sharify - International journal of transportation …, 2023 - Elsevier
model integrates Convolutional Neural Network (CNN), Long Short-term Memory (LSTM),
and Artificial Neural Network (ANN) to incorporate … , including identifying hazardous locations, …

HCF: A hybrid CNN framework for behavior detection of distracted drivers

C Huang, X Wang, J Cao, S Wang, Y Zhang - IEEE access, 2020 - ieeexplore.ieee.org
… camera technology, convolutional neural network (CNN) can … module, which integrates
three pretrained models, ResNet50, … as safe driving, which is dangerous for practical use. The …

Driver behavior detection and classification using deep convolutional neural networks

M Shahverdy, M Fathy, R Berangi… - Expert Systems with …, 2020 - Elsevier
… on only one risky behavior and neglected the other unsafe behaviors such as drowsiness and
… have ignored some risky behaviors such as driving with constant speed and risky steering. …

Effects of dataset characteristics on the performance of fatigue detection for crane operators using hybrid deep neural networks

P Liu, HL Chi, X Li, J Guo - Automation in Construction, 2021 - Elsevier
… Furthermore, operators' unsafe behaviour is the main reason … This study develops a hybrid
learning architecture to explore … states and behaviours according to the integration of the three …

Deep learning‐based safety helmet detection in engineering management based on convolutional neural networks

Y Li, H Wei, Z Han, J Huang… - Advances in Civil …, 2020 - Wiley Online Library
… on convolutional neural networks is used to train the model, … an alternative solution to detect
the unsafe operation of failure of … a hybrid deep learning model that integrates a convolution

A Hybrid Learning-Architecture for Mental Disorder Detection using Emotion Recognition

J Aina, O Akinniyi, MM Rahman, V Odero-Marah… - IEEE …, 2024 - ieeexplore.ieee.org
… result in suicidal, harmful behaviors and even death… hybrid architecture that integrates object
detection with ensemble of various learning methods (ie, shallow and deep neural networks

Risk factor recognition for automatic safety management in construction sites using fast deep convolutional neural networks

J Park, H Lee, HY Kim - Applied Sciences, 2022 - mdpi.com
… are widely used in image classification and object detection. … , we propose an integrated
recognition model that includes … this paper refer to unsafe human behavior and the presence or …

An unsafe behavior detection method based on improved YOLO framework

B Chen, X Wang, Q Bao, B Jia, X Li, Y Wang - Electronics, 2022 - mdpi.com
… fault-detection model was proposed in [12], which is based on the faster region convolutional
neural network (R-… To better integrate more high-quality upper and lower semantic feature …

HDLNIDS: hybrid deep-learning-based network intrusion detection system

EUH Qazi, MH Faheem, T Zia - Applied Sciences, 2023 - mdpi.com
… A convolutional recurrent neural network is employed in this … packets to detect harmful
network traffic behavior patterns. … A hybrid strategy integrating the linear correlation analysis …

Wearable Sensor‐Based Human Activity Recognition Using Hybrid Deep Learning Techniques

H Wang, J Zhao, J Li, L Tian, P Tu… - … Networks, 2020 - Wiley Online Library
… , we first build a deep convolutional neural network (CNN) for … elderly patients care, bad
habits detection, and identification. … integration model, integrating different LSTM learners into an …