DangerDet: A mobile application-based danger detection platform for women and children using deep learning
Year: 2024
Type: software-paper
Abstract: The use of mobile technology has grown dramatically on a global scale in recent years. As a result, people have difficulties when attempting to apply danger detection features efficiently. Thus, the purpose of this research is to create an Android mobile application that will facilitate the detection of danger for women and children. The application records the surroundings of the users and classifies the audio using deep-learning models to detect the voice, whether it is a normal, or frightened voice. When an adverse incident is identified, the Android application additionally initiates the proper steps to avoid further deprivation. Users of Android devices can use this application for free, opening the door to ensuring women’s and children’s safety.
Source: SoftwareX
Authors: Md. Ashikuzzaman, Abdul Aziz, Awal Ahmed Fime
Institution: Khulna University of Engineering and Technology
Language: English
Subfield: Mechanical Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $970
OpenAlex ID: W4404743879