TY - GEN
T1 - Tweets Monitoring for Real-Time Emergency Events Detection in Smart Campus
AU - Ramírez-García, Jorge
AU - Ibarra-Orozco, Rodolfo E.
AU - Argüelles Cruz, Amadeo J.
N1 - Publisher Copyright:
© 2020, Springer Nature Switzerland AG.
PY - 2020
Y1 - 2020
N2 - An intelligent campus has the purpose of improving the quality of life of students, making intensive, global, sustainable and efficient use of information technologies to interconnect all the actors and services for the benefit of the entire community, to establish an intelligent environment of teaching, learning and living[2]. In such smart environments, the role of users is becoming increasingly relevant, going from passive beneficiaries of services to participants assets through their social media activities. In this project, a system for detecting emergency events was developed for the IPN-Zacatenco Intelligent Campus for detecting emergency events, through analyzing messages (tweets) from Twitter users near of the area of interest. Tweets were classified under 4 categories: Mobility, Fire, Health and None (to discard unrelated tweets). In this article, we compare the machine learning models got with the Bayes Multinomial, Vector Support Machines and k-Nearest Neighbors algorithms.
AB - An intelligent campus has the purpose of improving the quality of life of students, making intensive, global, sustainable and efficient use of information technologies to interconnect all the actors and services for the benefit of the entire community, to establish an intelligent environment of teaching, learning and living[2]. In such smart environments, the role of users is becoming increasingly relevant, going from passive beneficiaries of services to participants assets through their social media activities. In this project, a system for detecting emergency events was developed for the IPN-Zacatenco Intelligent Campus for detecting emergency events, through analyzing messages (tweets) from Twitter users near of the area of interest. Tweets were classified under 4 categories: Mobility, Fire, Health and None (to discard unrelated tweets). In this article, we compare the machine learning models got with the Bayes Multinomial, Vector Support Machines and k-Nearest Neighbors algorithms.
KW - Naive Bayes
KW - Support Vector Machines
KW - k-Nearest Neighbors
UR - http://www.scopus.com/inward/record.url?scp=85092941697&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-60887-3_18
DO - 10.1007/978-3-030-60887-3_18
M3 - Contribución a la conferencia
AN - SCOPUS:85092941697
SN - 9783030608866
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 205
EP - 213
BT - Advances in Computational Intelligence - 19th Mexican International Conference on Artificial Intelligence, MICAI 2020, Proceedings
A2 - Martínez-Villaseñor, Lourdes
A2 - Ponce, Hiram
A2 - Herrera-Alcántara, Oscar
A2 - Castro-Espinoza, Félix A.
PB - Springer Science and Business Media Deutschland GmbH
T2 - 19th Mexican International Conference on Artificial Intelligence, MICAI 2020
Y2 - 12 October 2020 through 17 October 2020
ER -