Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Video

Updated: Jun 16, 2026

Artificial Intelligence-Based System for Detecting Attention Levels in Students
06:37

Artificial Intelligence-Based System for Detecting Attention Levels in Students

Published on: December 15, 2023

A hybrid CNN-ViT based framework for automatic traffic actions detection in smart cities.

Mucahit Karaduman1, Neunggyu Han2, Gulsah Karaduman3

  • 1Department of Software Engineering, Malatya Turgut Ozal University, Malatya, Turkey.

Plos One
|January 16, 2026
PubMed
Summary

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

CT-Based Liver Segmentation for Liver Surgery: A Hybrid Approach Based on 3D U-Net-ELM Model.

Biomedicines·2026
Same author

RGB-based visual encoding of vibration data for gearbox fault diagnosis using U-Net segmentation model.

PloS one·2026
Same author

An Attention-Enhanced RegNetY Framework for Detection and Classification of Vertical Misfit in Implant-Supported Restorations: A Retrospective Study.

Diagnostics (Basel, Switzerland)·2026
Same author

Deep learning for automatic segmentation of the inferior alveolar nerve using a hybrid CNN-transformer framework.

Scientific reports·2026
Same author

Automated Classification of Maxillary Sinus Ostium Patency Using a ConvNeXt-Tiny + DeiT Gated MLP-Based Hybrid Deep Learning Model: A Retrospective CBCT Study.

Diagnostics (Basel, Switzerland)·2026
Same author

A gated task-attentive multi-task network for unified retinal image analysis.

Scientific reports·2026
Same journal

Epidemiological characteristics of amebiasis in Japan from 2001 to 2022.

PloS one·2026
Same journal

Longitudinal associations of academic stress with eating related patterns, nutrition, somatic indicators, and depressive symptoms in university students: A study protocol.

PloS one·2026
Same journal

Pollution removal efficiency enhancement by agricultural biomass additions in constructed wetlands: A framework integrating meta-analysis with explainable machine learning.

PloS one·2026
Same journal

Insulation failure mapping on power transformer bushing using FRA and electrostatic simulation.

PloS one·2026
Same journal

Enhancing medical Q&A systems with multimodal knowledge graphs and dual-layer attention mechanisms.

PloS one·2026
Same journal

UAMP: Consistent video object segmentation with uncertainty-aware memory propagation.

PloS one·2026
See all related articles
This summary is machine-generated.

This study introduces an AI system for automatic traffic accident detection, achieving 96.88% accuracy. This technology enhances road safety and supports sustainable smart city development.

Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Urban Planning

Background:

  • Traffic accidents cause millions of deaths annually, straining healthcare and impacting urban life.
  • Timely detection of traffic incidents is vital for emergency response, traffic management, and preventing secondary accidents.
  • Artificial intelligence (AI) systems are crucial for developing smart city infrastructure.

Purpose of the Study:

  • To develop an AI-supported system for automatic detection of traffic accidents and hazardous situations.
  • To enhance road safety, economic efficiency, and urban sustainability through early incident detection.

Main Methods:

  • Feature extraction using five Convolutional Neural Network (CNN) and five Vision Transformer (ViT) models.
  • Evaluation of extracted features using various classifiers.

Related Experiment Videos

Last Updated: Jun 16, 2026

Artificial Intelligence-Based System for Detecting Attention Levels in Students
06:37

Artificial Intelligence-Based System for Detecting Attention Levels in Students

Published on: December 15, 2023

  • Development of a hybrid model combining features from the best CNN and ViT models for enhanced classification into eight categories.

    • Main Results:

    • The proposed hybrid model achieved a high accuracy of 96.88% in detecting traffic accidents and hazardous situations.
    • The combined features from CNN and ViT models outperformed individual models and preliminary ten models.
    • The system demonstrated superior performance in classifying traffic scenarios.

    Conclusions:

    • The developed AI model significantly improves the accuracy and timeliness of traffic accident detection.
    • This technology is a key component for building safer, more sustainable smart cities.
    • The high accuracy offers promising potential for future advancements in intelligent transportation systems.