CFP last date
22 April 2024
Reseach Article


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

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 = { },
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
%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

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.

  1. Kejriwal M, Zhou P. On detecting urgency in short crisis messages using minimal supervision and transfer learning. Soc Netw Anal Min. 2020;10(1):58. doi: 10.1007/s13278- 020-00670-7. Epub 2020 Jul 8. PMID: 32834866; PMCID: PMC7341028.
  2. Alam F, Ofli F, Imran M (2018) Crisismmd: multimodal twitter datasets from natural disasters. In: Twelfth international AAAI conference on web and social media
  3. Anderson KM, Schram A, Alzabarah A, Palen L. Architectural implications of social media analytics in support of crisis informatics research. IEEE Data Eng Bull. 2013;36:13– 20. [Google Scholar]
  4. Arthur R, Boulton CA, Shotton H, Williams HT (2017) Social sensing of floods in the UK. arXiv preprint arXiv:1711.04695 [PMC free article] [PubMed]
  5. Avvenuti M, Cresci S, La Polla MN, Marchetti A, Tesconi M (2014) Earthquake emergency management by social sensing. In: Pervasive computing and communications workshops (PERCOM Workshops), 2014 IEEE international conference on, pp 587–592. IEEE
  6. Mikel Artetxe and Holger Schwenk. 2019. Massively Multilingual Sentence Embeddings for Zero-Shot CrossLingual Transfer and Beyond. Trans. Assoc. Comput. Linguistics, 7:597–610.
  7. M. Aljabri et al., "Detecting Malicious URLs Using Machine Learning Techniques: Review and Research Directions," in IEEE Access, vol. 10, pp. 121395-121417, 2022, doi: 10.1109/ACCESS.2022.3222307.Burel G, Alani H (2018) Crisis event extraction service (crees)-automatic detection and classification of crisis-related content on social media
  8. Aydoğan, M., & Karci, A. (2020). Improving the accuracy using pre-trained word embeddings on deep neural networks for Turkish text classification. Physica Astatistical Mechanics and Its Applications, 541, 123288.
  9. Collobert R, Weston J, Bottou L, Karlen M, Kavukcuoglu K, Kuksa P. Natural language processing (almost) from scratch. J Mach Learn Res. 2011;12:2493– 2537. [Google Scholar].
  10. Crooks A, Croitoru A, Stefanidis A, Radzikowski J. # earthquake: Twitter as a distributed sensor system. Trans GIS. 2013;17(1):124–147. doi: 10.1111/j.1467- 9671.2012.01359.x. [CrossRef] [Google Scholar]
  11. Song G, Huang D, Xiao Z. A Study of Multilingual Toxic Text Detection Approaches under Imbalanced Sample Distribution. Information. 2021; 12(5):205.
  12. Zhang, YD., Satapathy, S.C., Zhang, X. et al. COVID-19 Diagnosis via DenseNet and Optimization of Transfer Learning Setting. Cogn Comput (2021).
  13. Ali, Jehad & Khan, Rehanullah & Ahmad, Nasir & Maqsood, Imran. (2012). Random Forests and Decision Trees. International Journal of Computer Science Issues(IJCSI).
  14. Joulin A, Grave E, Bojanowski P, Mikolov T (2016) Bag of tricks for efficient text classification. arXiv preprint arXiv:1607.01759
  15. Kulikov, V. & Yerkebulan, Gulnur. (2019). ABOUT USING GOOGLE CUSTOM SEARCH AND GOOGLE TRANSLATE API IN DETECTION OF CROSS-LANGUAGE PLAGIATE. Вестник Алматинского университета энергетики и связи. 109-116. 10.51775/1999-9801_2019_47_4_109.
  16. Verma S, Vieweg S, Corvey WJ, Palen L, Martin JH, Palmer M, Schram A, Anderson KM (2011) Natural language processing to the rescue? extracting” situational awareness” tweets during mass emergency. In: Fifth international AAAI conference on weblogs and social media
  17. Shang, Weiyi & Adams, Bram & Hassan, Ahmed E.. (2012). Using Pig as a data preparation language for large-scale mining software repositories studies: An experience report. Journal of Systems and Software - JSS. 85. 10.1016/j.jss.2011.07.034.
  18. Habibian, Amirhossein & Mensink, Thomas & Snoek, Cees. (2014). VideoStory: A New Multimedia Embedding for Few-Example Recognition and Translation of Events. MM 2014 - Proceedings of the 2014 ACM Conference on Multimedia. 10.1145/2647868.26549
  19. Chen, T., & Guestrin, C. (2016, March 9). XGBoost: A Scalable Tree Boosting System. Retrieved December 20, 2022, from
Index Terms

Computer Science
Information Sciences


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.