Paper Title
Life support vehicle(LSV) locating framework using VaDE for crisis and alert mechanism
Article Identifiers
: Keywords
Variational Deep Embedding (VaDE), Gaussian Mixture Model(GMM), deep neural networks(DNN), Alert mechanism, Ambulance placing, crisis zone
Abstract
As the number of vehicles on the roads continues to grow, traffic accidents are becoming more frequent. Many lives are lost because medical assistance doesn't arrive quickly enough at the accident site, often due to traffic jams or complicated routes that delay rescue teams. To address this, we can identify high-risk accident areas and determine the best locations for placing ambulances to ensure they reach victims as quickly as possible. Ideally, ambulances should be stationed in locations with the highest need, allowing them to get to accident sites within five minutes. This project introduces a way to reduce ambulance response times at accident scenes. The plan is to modernize emergency response by using a new unsupervised generative clustering technique, Variational Deep Embedding (VaDE). This method stands out because it uses a four-step data generation process involving deep neural networks and a Gaussian Mixture Model to find the best zone for ambulance deployment.
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How To Cite (APA)
Bhuvaneswari.M, Gayathri Devi.P, Lohithya L.B, & Supraja parvathi Y (June-2024). Life support vehicle(LSV) locating framework using VaDE for crisis and alert mechanism . INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 9(6), c112-c114. https://ijnrd.org/papers/IJNRD2406211.pdf
Issue
Volume 9 Issue 6, June-2024
Pages : c112-c114
Other Publication Details
Paper Reg. ID: IJNRD_220702
Published Paper Id: IJNRD2406211
Downloads: 000122255
Research Area: Information TechnologyÂ
Author Type: Indian Author
Country: chennai, Tamil Nadu, India
Published Paper PDF: https://ijnrd.org/papers/IJNRD2406211.pdf
Published Paper URL: https://ijnrd.org/viewpaperforall?paper=IJNRD2406211
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