Paper Title
DISTRESS CALL DETECTION SYSTEM FOR EMERGENCY SCENARIOS USING CONVOLUTIONAL NEURAL NETWORKS
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Registration ID: IJNRD_217903
Published ID: IJNRD2404727
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Keywords
Distress Call Detection, AI-based solution, Disaster Scenarios, Audio analysis, Pre-Processing, Convolutional Neural Network, Performance Metrics, Emotion Classification, Web Application Interfac
Abstract
his paper discusses the development of a robust system for the automated detection of distress calls in emergency situations, leveraging advanced technologies and signal processing techniques. In contemporary emergency response scenarios, accurate identification of distress signals is crucial for efficient and swift assistance. The proposed system will employ state-of-the-art algorithms and artificial intelligence to analyze various communication channels, including audio and environmental files, to recognize patterns associated with distress signals. The paper will explore the integration of sophisticated convolutional networks to process real-time data, differentiating distress signals from background noise and non-emergency communications. This model will be trained on a diverse dataset of diverse audio speech datasets to enhance the system's adaptability and accuracy in recognizing varying communication patterns. Key objectives include the development of a user-friendly interface for emergency response teams, facilitating seamless integration of distress call data into their decision-making processes. The project also aims to address challenges such as signal variability, multiple communication formats, and evolving technologies by implementing adaptive algorithms.
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How To Cite (APA)
Sai Pavan Gurugubelli, A.Durga Praveen Kumar, DANTULURI HARSHA VARDHAN RAJU, Mahesh Reddy Dharmala, & Sukesh chandu Pakkurthi (April-2024). DISTRESS CALL DETECTION SYSTEM FOR EMERGENCY SCENARIOS USING CONVOLUTIONAL NEURAL NETWORKS. INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 9(4), h224-h231. https://ijnrd.org/papers/IJNRD2404727.pdf
Issue
Volume 9 Issue 4, April-2024
Pages : h224-h231
Other Publication Details
Paper Reg. ID: IJNRD_217903
Published Paper Id: IJNRD2404727
Research Area: Information TechnologyÂ
Author Type: Indian Author
Country: Anakapalli, Andhra Pradesh, India
Published Paper PDF: https://ijnrd.org/papers/IJNRD2404727.pdf
Published Paper URL: https://ijnrd.org/viewpaperforall?paper=IJNRD2404727
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