References
Abel, F., Hauff, C., Houben, G.-J., Tao, K. and Stronkman, R. (2012) Twitcident: Fighting fire with information from social web streams. Proceedings of the 21st International Conference on World Wide Web. ACM, Lyon, France, 305–308. Castillo, C. (2016). Big Crisis Data. Cambridge University Press. De Smet, H., Leysen J and Lagadee, P. (2011). The Response Phase of the Disaster Life Cycle Revisited. Proceedings of the 2011 Industrial Engineering Research Conference. Endsley, M.R. (1988). Design and evaluation for situation awareness enhancement. Human Factors and Ergonomics Society, 32, 97–101. Imran, M., Castillo, C., Diaz, F., and Vieweg, S. (2015). Processing social media messages in mass emergency: A survey. ACM Comput. Surv. . 47(4), 67. Iannella, R. (2009). Emergency Information Interoperability Frameworks https://www.w3.org/2005/Incubator/eiif/XGR-framework-20090220/ Khan, A., Bibi, F., Dilshad, M., Ahmed, S., Ullah, Z. and Ali, H. (2918). Accident Detection and Smart Rescue System using Android Smartphone with Real-Time Location Tracking, International Journal of Advanced Computer Science and Applications , 9(6), 341-355. Krytska, Y., Skarga-Bandurova, I., & Velykzhanin, A. (2017). IoT-based Situation Awareness Support System for Real-Time Emergency Management. Proceedings of the 9th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications, Bucharest, Romania Ku-Mahamud, K., Norwawi, N., Katuk, N., & Deris, S. (2008). Autonomous Notification and Situation Reporting for Flood Disaster Management. Comput. Inf. Sci., 1(3), 20-26. Lagerstrom, R., Arzhaeva, Y., Szul, P., Obst, O., Power, R., Robinson, B., and Bednarz, T. (2016). Image classification to Support Emergency Situation Awareness. Frontiers in Robotics and AI. Nguyen, D.T., Joty, S, Imran, M., Sajjad, H. and Mitra, P. (2016) Applications of Online Deep Learning for Crisis Response Using Social Media Information. 4th International Workshop on Social Web for Disaster Management . Sako, DJS., Onuodu, FE. and Eke, BO. (2021a). A Method for Classifying Tweets about Emergency Events using Deep Neural Networks. Journal of Software Engineering and Simulation, 7(9), 38-45. Sako, DJS., Onuodu, FE. and Eke, BO. (2021b). A Model for Improving Image Classification Using Convolutional Neural Network for Emergency Situation Reporting. International Journal of Advanced Research in Computer and Communication Engineering. 10(9), 53-60. Sako, DJS., Onuodu, FE. and Eke, BO. (2022). A Real-Time Emergency Event Detection and Location Prediction Framework for Twitter Streams. Int. J. Comp. Sci. and Math. Theory, 7(2). 45-60 Schulz, A., Mencia, E. L., and Schmidt, B. (2016). A Rapid-Prototyping Framework for Extracting Small-Scale Incident-Related Information in Microblogs: Application of Multi-Label Classification on Tweets. Information Systems, 57, 88–110. Yang, S., Chung, H., Lin, X., Lee, S., Chen, L., Wood, A., Kavanaugh, A.L., Sheetz, S.D., Shoemaker, D.J. and Fox, E.A. (2013). PhaseVis: What, When, Where, and Who in Visualizing the Four Phases of Emergency Management Through the Lens of Social Media. Proceedings of the 10th International ISCRAM Conference – Baden-Baden, Germany.