Life support vehicle(LSV) locating framework using VaDE for crisis and alert mechanism

Registration ID: IJNRD_220702

Published ID: IJNRD2406211

DOI: Click Here to Get

Bhuvaneswari.M , Gayathri Devi.P , Lohithya L.B , Supraja parvathi Y

Variational Deep Embedding (VaDE), Gaussian Mixture Model(GMM), deep neural networks(DNN), Alert mechanism, Ambulance placing, crisis zone

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.

Published Paper   E-Certificate

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

Volume 9 Issue 6, June-2024

Pages : c112-c114

Paper Reg. ID: IJNRD_220702

Published Paper Id: IJNRD2406211

Downloads: 000121979

Research Area: Information Technology 

Country: chennai, Tamil Nadu, India

Published Paper PDF: https://ijnrd.org/papers/IJNRD2406211.pdf

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Journal Name: INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT(IJNRD)

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