{"date_created":"2021-12-17T10:05:07Z","page":"4","language":[{"iso":"eng"}],"user_id":"67234","type":"conference","author":[{"id":"72768","orcid":"0000-0003-0215-1278","last_name":"Zahera","full_name":"Zahera, Hamada Mohamed Abdelsamee","first_name":"Hamada Mohamed Abdelsamee"},{"id":"67234","last_name":"Sherif","full_name":"Sherif, Mohamed","orcid":"https://orcid.org/0000-0002-9927-2203","first_name":"Mohamed"},{"full_name":"Ngonga Ngomo, Axel-Cyrille","last_name":"Ngonga Ngomo","first_name":"Axel-Cyrille","id":"65716"}],"status":"public","keyword":["sys:relevantFor:infai sys:relevantFor:bis sys:relevantFor:simba ngonga simba zahera sherif solide limboproject opal group\\_aksw dice"],"year":"2019","title":"Jointly Learning from Social Media and Environmental Data for Typhoon Intensity Prediction","citation":{"bibtex":"@inproceedings{Zahera_Sherif_Ngonga Ngomo_2019, title={Jointly Learning from Social Media and Environmental Data for Typhoon Intensity Prediction}, booktitle={K-CAP 2019: Knowledge Capture Conference}, author={Zahera, Hamada Mohamed Abdelsamee and Sherif, Mohamed and Ngonga Ngomo, Axel-Cyrille}, year={2019}, pages={4} }","ama":"Zahera HMA, Sherif M, Ngonga Ngomo A-C. Jointly Learning from Social Media and Environmental Data for Typhoon Intensity Prediction. In: K-CAP 2019: Knowledge Capture Conference. ; 2019:4.","ieee":"H. M. A. Zahera, M. Sherif, and A.-C. Ngonga Ngomo, “Jointly Learning from Social Media and Environmental Data for Typhoon Intensity Prediction,” in K-CAP 2019: Knowledge Capture Conference, 2019, p. 4.","short":"H.M.A. Zahera, M. Sherif, A.-C. Ngonga Ngomo, in: K-CAP 2019: Knowledge Capture Conference, 2019, p. 4.","apa":"Zahera, H. M. A., Sherif, M., & Ngonga Ngomo, A.-C. (2019). Jointly Learning from Social Media and Environmental Data for Typhoon Intensity Prediction. K-CAP 2019: Knowledge Capture Conference, 4.","mla":"Zahera, Hamada Mohamed Abdelsamee, et al. “Jointly Learning from Social Media and Environmental Data for Typhoon Intensity Prediction.” K-CAP 2019: Knowledge Capture Conference, 2019, p. 4.","chicago":"Zahera, Hamada Mohamed Abdelsamee, Mohamed Sherif, and Axel-Cyrille Ngonga Ngomo. “Jointly Learning from Social Media and Environmental Data for Typhoon Intensity Prediction.” In K-CAP 2019: Knowledge Capture Conference, 4, 2019."},"publication":"K-CAP 2019: Knowledge Capture Conference","abstract":[{"text":"Existing technologies employ different machine learning approaches to predict disasters from historical environmental data. However, for short-term disasters (e.g., earthquakes), historical data alone has a limited prediction capability. In this work, we consider social media as a supplementary source of knowledge in addition to historical environmental data. Further, we build a joint model that learns from disaster-related tweets and environmental data to improve prediction. We propose the combination of semantically-enriched word embedding to represent entities in tweets with their semantics representations computed with the traditional word2vec. Our experiments show that our proposed approach outperforms the accuracy of state-of-the-art models in disaster prediction.","lang":"eng"}],"_id":"29037","date_updated":"2023-08-16T09:24:21Z"}