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| 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
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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