Detección de situaciones de emergencias usando el modelo Naive- Bayes de machine learning.
Nowadays, social networks have gained ground in the generation and obtaining of information instantly, this feature makes it very useful in the detection and warnings of emergencies such as road accidents, fires, storms, floods, etc. This has motivated the generation of a large number of works about...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Fundación de Estudios Superiores Comfanorte
2023-01-01
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| Series: | Mundo Fesc |
| Subjects: | |
| Online Access: | https://www.fesc.edu.co/Revistas/OJS/index.php/mundofesc/article/view/1286 |
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| Summary: | Nowadays, social networks have gained ground in the generation and obtaining of information instantly, this feature makes it very useful in the detection and warnings of emergencies such as road accidents, fires, storms, floods, etc. This has motivated the generation of a large number of works about the use of this information to face the problems generated by such emergencies, work such as A. Kansal, Y. Singh, N. Kumar "Detection of forest fire using Machine Learning technique" [1] or Chamorro Verónica "Classification of tweets using supervised learning models" [2], show the use of machine learning techniques for the detection of extraordinary situations. After these catastrophic or emergency situations, it is necessary to manage the care and protection services of the population, problems such as information chaos, uncertainty in the needs and services can find a solution in the timely detection of which events are really emergencies, so the purpose of this work we use X messages (Twitter) to classify which emergencies really are or are not. We use the machine learning algorithm known as Naive-Bayes in this problem of classifying messages from X, to determine real emergencies, with a result in the evaluation of the accuracy in the classification of real emergencies with a proportion of 73.4% among those classified as emergencies and classifies false emergencies with a precision of 75.4% among those classified as false. In general, the obtained model has an accuracy of 74.6% in its classification predictions. It is considered that the use of a Naive-Bayes model for a prototype in the classification of emergency messages from the social network X could be very useful based on the results of the evaluation of its classification performance. |
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| ISSN: | 2216-0353 2216-0388 |