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 Concept Videos

Visual System01:26

Visual System

Light enters the eye through the cornea, a transparent, dome-shaped surface covering the surface of the eyeball that helps to direct and focus incoming light. This light is then channeled toward the pupil, an adjustable opening whose size is controlled by the iris. The iris, a pigmented muscle, regulates the amount of light entering the eye by contracting or dilating the pupil, thereby ensuring optimal light levels for clear vision.
Once through the pupil, the light passes through the lens, a...

You might also read

Related Articles

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

Sort by
Same author

An explainable hybrid deep learning framework for computational aesthetics, thematic mining, and sentiment analysis in english poetry.

Scientific reports·2026
Same author

Smart comprehend gesture based emotions recognition system for people with hearing disability utilizing spatio temporal graph convolutional network techniques.

Scientific reports·2026
Same author

An intelligent framework for visually impaired people through indoor object Detection-Based assistive system using YOLO with recurrent neural networks.

Scientific reports·2025
Same author

Intelligent feature fusion with dynamic graph convolutional recurrent network for robust object detection to assist individuals with disabilities in a smart Iot edge-cloud environment.

Scientific reports·2025
Same author

Empowering people with intellectual disabilities using integrated deep learning architecture driven enhanced text-based emotion classification.

Scientific reports·2025
Same author

A novel hybrid attention based deep learning framework for textual emotion recognition using natural language processing technologies for disabled persons.

Scientific reports·2025

Related Experiment Video

Updated: Jul 21, 2026

A Novel Single Animal Motor Function Tracking System Using Simple, Readily Available Software
08:22

A Novel Single Animal Motor Function Tracking System Using Simple, Readily Available Software

Published on: August 31, 2018

6.7K

An advanced fire detection system for assisting visually challenged people using recurrent neural network and

Fahd N Al-Wesabi1, Abeer A K Alharbi2, Ishfaq Yaseen3,4

  • 1Department of Computer Science, Applied College at Mahayil, King Khalid University, Abha, Saudi Arabia. falwesabi@kku.edu.sa.

Scientific Reports
|July 2, 2025
PubMed
Summary

A new smart fire detection system (SFDAB-ARNNSHO) uses AI to help blind individuals by accurately identifying fires early. This innovative approach aims to reduce fire-related casualties and property damage for vulnerable populations.

Keywords:
Attention mechanismFeature extractionFire detectionSea-horse optimizerSobel filtering

More Related Videos

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

650
Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention
06:37

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

Published on: December 15, 2023

4.1K

Related Experiment Videos

Last Updated: Jul 21, 2026

A Novel Single Animal Motor Function Tracking System Using Simple, Readily Available Software
08:22

A Novel Single Animal Motor Function Tracking System Using Simple, Readily Available Software

Published on: August 31, 2018

6.7K
Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

650
Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention
06:37

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

Published on: December 15, 2023

4.1K

Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Assistive Technology

Background:

  • Elderly individuals with visual and cognitive impairments face challenges with independence, especially during emergencies like fires.
  • Existing fire detection systems often fail to consider the specific needs of blind and intellectually impaired individuals.
  • Rapid fire detection is crucial to prevent damage and save lives, particularly for those with sensory impairments.

Purpose of the Study:

  • To develop an intelligent fire detection system tailored for blind individuals.
  • To enhance early fire recognition and notification capabilities for improved safety.
  • To leverage advanced AI techniques for accurate fire classification and alerts.

Main Methods:

  • The proposed Smart Fire Detection System for Assisting the Blind Using Attention Mechanism-Driven Recurrent Neural Network and Seahorse Optimizer Algorithm (SFDAB-ARNNSHO) was developed.
  • Image preprocessing utilized Sobel filtering (SF) for noise reduction.
  • Feature extraction combined EfficientNetB7, CapsNet, and ShuffleNetV2, followed by fire detection and classification using stacked two-layer bidirectional long short-term memory with an attention mechanism (SBiLSTM-AM).
  • The Seahorse Optimizer (SHO) algorithm was employed for parameter tuning of the SBiLSTM-AM model.

Main Results:

  • The SFDAB-ARNNSHO model achieved a superior accuracy of 99.30% in fire detection and classification.
  • Performance validation demonstrated significant improvements over existing models on a fire detection dataset.
  • The system effectively detects and classifies fires, providing crucial information for visually impaired individuals.

Conclusions:

  • The SFDAB-ARNNSHO system offers a highly accurate and effective solution for early fire detection and notification for the blind.
  • This AI-driven approach has the potential to significantly decrease fire-related fatalities and losses among visually impaired populations.
  • The integration of attention mechanisms and optimization algorithms enhances the reliability of fire detection systems for assistive purposes.