Earticle

초록

영어

Early warning is essential for reducing disaster damage. There is a need for an automated catastrophe classification model that can respond fast to local disaster damage by using the properties of social media, where information is exchanged swiftly. Recently, research on automatically identifying disasters using deep learning has begun, with the goal of supplementing the deep learning model's performance. Here, we propose a novel framework, Metamon-Disaster for deep learning model that automatically classifies disasters based on disaster-related keyword data collected from social media. To classify disasters by type, a learning model generated from NAS was employed, and when compared to other classification such as RF (Random Forest), SVC (Support Vector Classifier) or GBM (Gradient Boost Machine) to check the optimal performance of the model, the suggested model exhibited the best performance with an 0.8928 F1-Score. The model for disaster notification service will provide automated disaster notice and rapid reaction.

목차

Abstract
I. INTRODUCTION
II. THE PROPOSED MODEL
A. Data Collection
B. Data Preprocessiong
C. Proposed Model
D. Comparison Models
III. EXPERIMENT
A. Experimental Set-up
B. Experimental results
IV. CONCLUSION
REFERENCES

키워드

저자

• Hyunju Kang [ AI Applied Research Team Mobigen Inc. Seoul, Republic of Korea ] Corresponding Author
• sunghoon An [ AI Applied Research Team Mobigen Inc. Seoul, Republic of Korea ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  한국차세대컴퓨팅학회 학술대회

• 간기

  반년간

• 수록기간

  2021~2025

• 십진분류

  KDC 566 DDC 004