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Signature Generation Algorithms for Polymorphic Worms
Published in Mohssen Mohammed, Al-Sakib Khan Pathan, Automatic Defense Against Zero-day Polymorphic Worms in Communication Networks, 2016
Mohssen Mohammed, Al-Sakib Khan Pathan
The HMM is a powerful statistical tool for modeling generative sequences that can be characterized by an underlying process generating an observable sequence. HMMs have found applications in many areas interested in signal processing, in particular speech processing, but have also been applied with success to low-level NLP (natural language processing) tasks such as part-of-speech tagging, phrase chunking, and extracting target information from documents. Andrei Markov gave his name to the mathematical theory of Markov processes in the early twentieth century [44], but it was Baum and his colleagues who developed the theory of HMMs in the 1960s [45].
What do riders say and where? The detection and analysis of eyewitness transit tweets
Published in Journal of Intelligent Transportation Systems, 2023
O. Kabbani, W. Klumpenhouwer, T. El-Diraby, A. Shalaby
Even though users can tag their tweets with geographic metadata such as a location name or coordinates, it is estimated that only a small portion (1% to 3%) of the tweets are geotagged (Liao et al., 2021; Sloan et al., 2013). This has pressed researchers to develop methods to extract location information from tweet text using context clues. Francalanci et al. (2017) utilized named entry recognition and geocoding to extract location information from text, increasing the number of geolocated tweets by a factor of six. A similar study focused on sentence structure and employed natural language processing to identify tweets that contain location information (T. B. N. Hoang & Mothe, 2018). Additionally, Liu et al. (2019) utilized the natural language processing tool of noun-phrase chunking to extract location data from tweets.