CFP last date
22 April 2024
Reseach Article

SOS SYNC

by Anupama Kaushik, Shruti Sagar, Sameep Punjani, Priyanshu Mahendra
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 186 - Number 6
Year of Publication: 2024
Authors: Anupama Kaushik, Shruti Sagar, Sameep Punjani, Priyanshu Mahendra
10.5120/ijca2024923395

Anupama Kaushik, Shruti Sagar, Sameep Punjani, Priyanshu Mahendra . SOS SYNC. International Journal of Computer Applications. 186, 6 ( Jan 2024), 6-13. DOI=10.5120/ijca2024923395

@article{ 10.5120/ijca2024923395,
author = { Anupama Kaushik, Shruti Sagar, Sameep Punjani, Priyanshu Mahendra },
title = { SOS SYNC },
journal = { International Journal of Computer Applications },
issue_date = { Jan 2024 },
volume = { 186 },
number = { 6 },
month = { Jan },
year = { 2024 },
issn = { 0975-8887 },
pages = { 6-13 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume186/number6/33073-2024923395/ },
doi = { 10.5120/ijca2024923395 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T01:29:52.614992+05:30
%A Anupama Kaushik
%A Shruti Sagar
%A Sameep Punjani
%A Priyanshu Mahendra
%T SOS SYNC
%J International Journal of Computer Applications
%@ 0975-8887
%V 186
%N 6
%P 6-13
%D 2024
%I Foundation of Computer Science (FCS), NY, USA
Abstract

In times of distress situation, people find themselves in critical situations and are in a need of assistance due to various incidents like natural disasters, medical emergencies, or other life threatening events. Many people are often unable to get aid at the right time. So, in this research paper we are exploring the potential of harnessing machine learning and natural learning process (NLP) technologies to create a reliable system for classifying and detecting distress call using multiple languages. We have described an approach for automatically identifying the messages. The paper provides a comprehensive overview of the entire process involved in developing an efficient distress call detection system using machine learning. It addresses various aspects, including the challenges associated with multi-lingual NLP, methods for identifying urgency in text, data preprocessing techniques to improve accuracy, and the evaluation of performance results. Additionally, the paper delves into the four key steps of the machine learning pipeline: text vectorization, Tf-Idf normalization, model training, and hyperparameter tuning. By combining NLP and machine learning methodologies, this research aims to establish an effective and precise system for recognizing urgency in multi-lingual texts.

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Index Terms

Computer Science
Information Sciences

Keywords

Multilingual Classification Platform Natural Language Processing (NLP) Machine Learning Features Pre-Processing Data Benchmark Performance Results ETL Pipeline Text Vectorization Term Frequency-Inverse Document Frequency (Tf-Idf) Normalization XGBClassifier Model Training Hyper-Parameter Tuning.