@inproceedings{1db639f560324d7a9b7770cfbc810c1e,
title = "An Information Retrieval System for CBRNe Incidents",
abstract = "Chemical Biological Radiological Nuclear explosive (CBRNe) incidents are relatively rare. However when they occur these incidents have a significant impact upon the nearby population, and the land it contaminates. The forensic teams who are tasked to investigate the areas are guided by standard operating procedures. These SOPS dictate how the incident is investigates. SOPS can be large and unwieldy documents, and there may be a large number of them at a single incident. Consequently it is possible that an incorrect procedure may be chosen during an incident because of partial or incomplete information. The reselection of SOPS based upon new information will be slow because it is a manual process. This system demonstration introduces an information retrieval that ranks SOPS based upon information generated by a probabilistic reasoning system and the scene commander. It ranks the SOPS relevance to the current incident. The system is designed to reduce the cognitive load upon the scene commander and therefore reduce their errors.",
author = "Brett Drury and Ihsan Ullah and Madden, \{Michael G.\}",
note = "Publisher Copyright: {\textcopyright} 2019, Springer Nature Switzerland AG.; European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2018 ; Conference date: 10-09-2018 Through 14-09-2018",
year = "2019",
doi = "10.1007/978-3-030-13453-2\_17",
language = "English",
isbn = "9783030134525",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer-Verlag",
pages = "211--215",
editor = "Carlos Alzate and Anna Monreale",
booktitle = "ECML PKDD 2018 Workshops - Nemesis 2018, UrbReas 2018, SoGood 2018, IWAISe 2018, and Green Data Mining 2018, Proceedings",
}