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Author |
Loïc Bidoux; Jean-Paul Pignon; Frédérick Bénaben |
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Title |
A model driven system to support optimal collaborative processes design in crisis management |
Type |
Conference Article |
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Year |
2014 |
Publication |
ISCRAM 2014 Conference Proceedings – 11th International Conference on Information Systems for Crisis Response and Management |
Abbreviated Journal |
ISCRAM 2014 |
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Volume |
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Issue |
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Pages |
245-249 |
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Keywords |
Algorithms; Benchmarking; Decision making; Inference engines; Optimization; Process design; Collaborative process; Crisis management; Inter-agencies coordination; Key performance indicators; Model-driven; Multi-criteria decision analysis; Optimization algorithms; Technical design; Information systems |
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Abstract |
This paper presents a system dedicated to support crises managers that is focused on the collaboration issues of the actors involved in the response. Based on context knowledge, decision makers' objectives and responders' capabilities, the system designs in a semi-automatic way a set of collaborative process alternatives that can optimize coordination activities during an ongoing crisis resolution. The technical design of the system mixes optimization algorithms with inference of logical rules on an ontology. Candidate processes are evaluated through multi-criteria decision analysis and proposed to the decision-makers with associated key performance indicators to help them with their choice. The overall approach is model driven through a crisis meta-model and an axiomatic theory of crisis management. |
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Address |
Mines Albi – Université de Toulouse, France; Thales Communications and Security, France |
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Publisher |
The Pennsylvania State University |
Place of Publication |
University Park, PA |
Editor |
S.R. Hiltz, M.S. Pfaff, L. Plotnick, and P.C. Shih. |
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Language |
English |
Summary Language |
English |
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ISSN |
2411-3387 |
ISBN |
9780692211946 |
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Track |
Decision Support Systems |
Expedition |
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Conference |
11th International ISCRAM Conference on Information Systems for Crisis Response and Management |
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no |
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Call Number |
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Serial |
325 |
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Author |
Chanthujan Chandrakumar; Raj Prasanna; Max Stephens; Marion Lara Tan; Caroline Holden; Amal Punchihewa; Julia S. Becker; Seokho Jeong; Danuka Ravishan |
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Title |
Algorithms for Detecting P-Waves and Earthquake Magnitude Estimation: Initial Literature Review Findings |
Type |
Conference Article |
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Year |
2023 |
Publication |
Proceedings of the ISCRAM Asia Pacific Conference 2022 |
Abbreviated Journal |
Proc. ISCRAM AP 2022 |
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Issue |
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Pages |
138-155 |
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Keywords |
Earthquake Early Warning; P-Waves; Magnitude Estimation; EEW Algorithms |
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Abstract |
Earthquake Early Warning System (EEWS) plays a major role during an earthquake in alerting the public and authorities to take appropriate safety measures during an earthquake. Generally, EEWSs use three types of algorithms to generate alerts during an earthquake; namely: source-based, ground motion or wavefield-based and on-site-based approaches. However, source-based algorithms are commonly used in most of EEWSs worldwide. A source-based EEWS uses a particular time frame of the P-wave of an earthquake to estimate the source parameters such as magnitude and the location of that earthquake with the support of P-wave detection and earthquake magnitude and location estimation algorithms. As the initial step of a research project which aims to explore the best use of P-waves to generate earthquake alerts, this Work in Progress paper (WiPe) presents the initial partial findings from an ongoing literature review on exploring the algorithms used for P-wave detection and earthquake magnitude estimation. |
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Address |
Massey University; Massey University; University of Auckland; Massey University; SeismoCity; ADP Consultancy; Massey University; Changwon National University; Synopsys |
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Publisher |
Massey Unversity |
Place of Publication |
Palmerston North, New Zealand |
Editor |
Thomas J. Huggins, V.L. |
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Language |
English |
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Track |
Building Disaster Resilience |
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Approved |
no |
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Call Number |
ISCRAM @ idladmin @ |
Serial |
2488 |
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Author |
Benjamin Heuer; Jan Zibuschka; Heiko Roßnagel; Johannes Maucher |
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Title |
Empirical analysis of passenger trajectories within an urban transport hub |
Type |
Conference Article |
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Year |
2012 |
Publication |
ISCRAM 2012 Conference Proceedings – 9th International Conference on Information Systems for Crisis Response and Management |
Abbreviated Journal |
ISCRAM 2012 |
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Volume |
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Issue |
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Pages |
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Keywords |
Algorithms; Information systems; Trajectories; Urban transportation; Central stations; Data mining algorithm; Empirical analysis; Empirical data; Passenger movements; Simulation framework; Urban transport; Data mining |
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Abstract |
In this contribution we present an analysis of passenger trajectories in an urban transportation hub. We collected an extensive amount of empirical data consisting of both gate and individual stalking observation in the central station of Cologne. Three different data mining algorithms are used to analyze this data, producing both data that may be used as input for simulation frameworks, and, as an aside, visualizations of passenger movements that could be of high interest to transport and emergency managers. © 2012 ISCRAM. |
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Address |
Hochschule der Medien (HdM), Germany; Fraunhofer IAO, Germany |
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Publisher |
Simon Fraser University |
Place of Publication |
Vancouver, BC |
Editor |
L. Rothkrantz, J. Ristvej, Z.Franco |
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Language |
English |
Summary Language |
English |
Original Title |
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Series Editor |
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Series Volume |
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Series Issue |
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Edition |
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ISSN |
2411-3387 |
ISBN |
9780864913326 |
Medium |
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Track |
Analytical Modelling and Simulation |
Expedition |
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Conference |
9th International ISCRAM Conference on Information Systems for Crisis Response and Management |
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Approved |
no |
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Call Number |
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Serial |
129 |
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Author |
Alexander Kiselev; Sergey Bogatov |
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Title |
Model PROLOG for countermeasures efficacy assessment and its calculation algorithm verification on the base of the Chazhma Bay accident data |
Type |
Conference Article |
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Year |
2012 |
Publication |
ISCRAM 2012 Conference Proceedings – 9th International Conference on Information Systems for Crisis Response and Management |
Abbreviated Journal |
ISCRAM 2012 |
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Pages |
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Keywords |
Algorithms; Cobalt compounds; Dispersions; Efficiency; Information systems; Landforms; Radioactivity; Calculation algorithms; Complex terrains; Computational model; Dry deposition velocities; Gaussian dispersions; Methodical approach; Radioactive contamination; Surface contaminations; Accidents |
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Abstract |
Methodical approaches used in the computational model “PROLOG” are given in the paper. This model is intended for assessing radiological situations and an efficiency of counter measures after short term radioactive releases. Basic local Gaussian dispersion algorithm is supplemented with modules for assessing a plume rise, dry deposition velocities, effect of buildings and complex terrain, etc. The modules provide a compromise between simplicity, shortage of initial data and adequacy of the model in case of real accident. Approaches to assess the dose and countermeasure efficiency are presented as well. Plume rise, complex terrain and contaminant polydispersity modeling approaches were tested on the basis of comparison of calculation and experimental results for dose rate and Co-60 surface contamination measured after the Chazhma bay accident in 1985. © 2012 ISCRAM. |
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Address |
IBRAE RAN, Russian Federation |
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Publisher |
Simon Fraser University |
Place of Publication |
Vancouver, BC |
Editor |
L. Rothkrantz, J. Ristvej, Z.Franco |
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Language |
English |
Summary Language |
English |
Original Title |
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Series Issue |
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Edition |
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ISSN |
2411-3387 |
ISBN |
9780864913326 |
Medium |
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Track |
Analytical Modelling and Simulation |
Expedition |
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Conference |
9th International ISCRAM Conference on Information Systems for Crisis Response and Management |
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Notes |
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Approved |
no |
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Call Number |
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Serial |
140 |
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Author |
Kostas Kolomvatsos; Kakia Panagidi; Stathes Hadjiefthymiades |
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Title |
Optimal spatial partitioning for resource allocation |
Type |
Conference Article |
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Year |
2013 |
Publication |
ISCRAM 2013 Conference Proceedings – 10th International Conference on Information Systems for Crisis Response and Management |
Abbreviated Journal |
ISCRAM 2013 |
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Volume |
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Issue |
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Pages |
747-757 |
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Keywords |
Algorithms; Disaster prevention; Disasters; Image segmentation; Information systems; Particle swarm optimization (PSO); Risk management; Disaster management; Emergency management; Emergency response; Intelligent techniques; Numerical results; Particle swarm optimization algorithm; Pso; Spatial partitioning; Resource allocation |
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Abstract |
Spatial partitioning consists of the problem of finding the best segmentation of an area under specific conditions. The final goal is to identify parts of the area where a number of resources could be allocated. Such cases are common in disaster management scenarios. In this paper, we consider such a scenario and propose a methodology for the resource allocation for emergency response. We utilize an intelligent technique that is based on the Particle Swarm Optimization algorithm. We define the problem by giving specific formulations and describe the proposed algorithm. Moreover, we provide a method for separating the area into cells and describe a technique for calculating cell weights based on the underlying spatial data. Finally, we present a case study for allocating a number of ambulances and give numerical results concerning the run time and the total coverage of the examined area. |
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Address |
Dept. of Informatics and Telecommunications, National and Kapodistrian University of Athens, Greece |
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Publisher |
Karlsruher Institut fur Technologie |
Place of Publication |
KIT; Baden-Baden |
Editor |
T. Comes, F. Fiedrich, S. Fortier, J. Geldermann and T. Müller |
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Language |
English |
Summary Language |
English |
Original Title |
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Series Editor |
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Series Volume |
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Series Issue |
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Edition |
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ISSN |
2411-3387 |
ISBN |
9783923704804 |
Medium |
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Track |
Planning and Foresight |
Expedition |
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Conference |
10th International ISCRAM Conference on Information Systems for Crisis Response and Management |
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Notes |
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Approved |
no |
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Call Number |
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Serial |
658 |
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Author |
Konstantinos Koufos; Krisztina Cziner; Pekka Parviainen |
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Title |
Multicast video performance evaluation for emergency response communications |
Type |
Conference Article |
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Year |
2007 |
Publication |
Intelligent Human Computer Systems for Crisis Response and Management, ISCRAM 2007 Academic Proceedings Papers |
Abbreviated Journal |
ISCRAM 2007 |
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Volume |
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Issue |
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Pages |
595-604 |
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Keywords |
Ad hoc networks; Image communication systems; Mobile ad hoc networks; Multicasting; Network routing; Routing algorithms; Telecommunication links; Telecommunication networks; Edge effect; Multicast routing; Network simulators; On-demand multicast routing protocols; Random Waypoint mobility model; Unidirectional links; Emergency services |
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Abstract |
Group-oriented services including data dissemination, group calls and real-time video transmission are considered as an important application in public safety communications. The main interest is in one-way real-time video transmission from the hot spot to multiple recipients. This is important for efficient emergency response. The changing topology of the multi-hop communication links in a public safety environment makes routing and multicasting extremely challenging task. The purpose of this paper is to study the performance of wireless mobile ad-hoc networks with one-way multicast video traffic. To consider a realistic public safety scenario, the effect of extensive unidirectional links is investigated. The system performance study of various ad-hoc network configurations is done by simulations. For wireless multicast routing, the On Demand Multicast Routing Protocol is used. The performance results are compared with the requirements provided by Statement of Requirement document of standardization project MESA. |
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Address |
Teknillinen Korkeakoulu, TKK, Finland |
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Corporate Author |
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Thesis |
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Publisher |
Information Systems for Crisis Response and Management, ISCRAM |
Place of Publication |
Delft |
Editor |
B. Van de Walle, P. Burghardt, K. Nieuwenhuis |
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Language |
English |
Summary Language |
English |
Original Title |
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Series Editor |
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Abbreviated Series Title |
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Series Volume |
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Series Issue |
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Edition |
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ISSN |
2411-3387 |
ISBN |
9789054874171; 9789090218717 |
Medium |
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Track |
MSCT |
Expedition |
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Conference |
4th International ISCRAM Conference on Information Systems for Crisis Response and Management |
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Notes |
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Approved |
no |
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Call Number |
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Serial |
660 |
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Author |
Michael K. Lindell |
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Title |
Evacuation modelling: Algorithms, assumptions, and data |
Type |
Conference Article |
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Year |
2011 |
Publication |
8th International Conference on Information Systems for Crisis Response and Management: From Early-Warning Systems to Preparedness and Training, ISCRAM 2011 |
Abbreviated Journal |
ISCRAM 2011 |
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Volume |
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Issue |
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Pages |
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Keywords |
Algorithms; Decision making; Hurricanes; Information systems; Empirical data; Evacuation modelling; Hurricane evacuation; Information display; Local government; Training program; Uncertainty analysis |
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Abstract |
Survey researchers need to, Find out what assumptions evacuation modelers are making and collect empirical data to replace incorrect assumptions;, Obtain data on the costs of evacuation to households, businesses, and local government; and, Extend their analyses to address the logistics of evacuation and the process of re-entry. Evacuation modelers need to, Incorporate available empirical data on household evacuation behavior, and, Generate estimates of the uncertainties in their analyses. Cognitive scientists need to, Conduct experiments on hurricane tracking and evacuation decision making to better understand these processes, and, Develop training programs, information displays, and performance aids to assist local officials who have little or no previous experience in hurricane evacuation decision making. |
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Address |
Texas A and M University, Hazard Reduction and Recovery Center, United States |
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Thesis |
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Publisher |
Information Systems for Crisis Response and Management, ISCRAM |
Place of Publication |
Lisbon |
Editor |
M.A. Santos, L. Sousa, E. Portela |
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Language |
English |
Summary Language |
English |
Original Title |
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Series Editor |
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Abbreviated Series Title |
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Series Volume |
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Series Issue |
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Edition |
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ISSN |
2411-3387 |
ISBN |
9789724922478 |
Medium |
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Track |
Conference Keynote |
Expedition |
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Conference |
8th International ISCRAM Conference on Information Systems for Crisis Response and Management |
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Notes |
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Approved |
no |
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Call Number |
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Serial |
707 |
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Author |
Md Fitrat Hossain; Thomas Kissane; Priyanka Annapureddy; Wylie Frydrychowicz; Sheikh Iqbal Ahamed; Naveen Bansal; Praveen Madiraju; Niharika Jain; Mark Flower; Katinka Hooyer; Lisa Rein; Zeno Franco |
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Title |
Implementing Algorithmic Crisis Alerts in mHealth Systems for Veterans with PTSD |
Type |
Conference Article |
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Year |
2020 |
Publication |
ISCRAM 2020 Conference Proceedings – 17th International Conference on Information Systems for Crisis Response and Management |
Abbreviated Journal |
Iscram 2020 |
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Volume |
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Issue |
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Pages |
122-133 |
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Keywords |
Crisis; Machine Learning Algorithms; mHealth; PTSD |
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Abstract |
This paper seeks to establish a machine learning driven method by which a military veteran with Post-Traumatic Stress Disorder (PTSD) is classified as being in a crisis situation or not, based upon a given set of criteria. Optimizing alerting decision rules is critical to ensure that veterans at highest risk for mental health crisis rapidly receive additional attention. Subject matter experts in our team (a psychologist, a medical anthropologist, and an expert veteran), defined acute crisis, early warning signs and long-term crisis from this dataset. First, we used a decision tree to find an early time point when the peer mentors (who are also veterans) need to observe the behavior of veterans to make a decision about conducting an intervention. Three different machine learning algorithms were used to predict long term crisis using acute crisis and early warning signs within the determined time point. |
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Address |
Marquette University; Marquette University; Marquette University; Marquette University; Marquette University; Marquette University; Marquette University; Marquette University; Mental Health America; Medical College of Wisconsin; Medical College of Wisconsin; Medical College of Wisconsin |
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Publisher |
Virginia Tech |
Place of Publication |
Blacksburg, VA (USA) |
Editor |
Amanda Hughes; Fiona McNeill; Christopher W. Zobel |
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Language |
English |
Summary Language |
English |
Original Title |
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Series Editor |
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Abbreviated Series Title |
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Series Volume |
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Series Issue |
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Edition |
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ISSN |
978-1-949373-27-12 |
ISBN |
2411-3398 |
Medium |
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Track |
AI Systems for Crisis and Risks |
Expedition |
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Conference |
17th International Conference on Information Systems for Crisis Response and Management |
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Notes |
mdfitrat.hossain@marquette.edu |
Approved |
no |
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Call Number |
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Serial |
2213 |
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Author |
Hussain Aziz Saleh |
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Title |
Dynamic optimisation of the use of space technology for rapid disaster response and management |
Type |
Conference Article |
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Year |
2005 |
Publication |
Proceedings of ISCRAM 2005 – 2nd International Conference on Information Systems for Crisis Response and Management |
Abbreviated Journal |
ISCRAM 2005 |
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Volume |
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Issue |
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Pages |
139-141 |
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Keywords |
Algorithms; Artificial intelligence; Disaster prevention; Information systems; Optimization; Satellite ground stations; Disaster management; Disaster warnings; Dynamic optimisation; Intelligent Algorithms; Meta heuristics; Natural and man-made disasters; Real-world problem; Space technologies; Disasters |
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Abstract |
Modern space and information technologies provide valuable tools for the solution of many real-world problems in fields of managing effects of natural and man-made disasters, geomatic engineering, etc. Therefore, the need to develop and optimise the use of these technologies in an efficient manner is necessary for providing reliable solutions. This paper aims to develop powerful optimisation algorithms extending current highly successful ideas of artificial intelligence for developing of the disaster warning network which is a system of satellites and ground stations for providing real time early warning of the impact of the disaster and minimise its effects (e.g., earthquakes, landslides, floods, volcanoes, etc). Such intelligent algorithms can provide a degree of functionality and flexibility suitable both for constructing high-accuracy models and in monitoring their behaviour in real time. |
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Address |
Department of Civil Engineering, Faculty of Engineering, Ghent University, Krijgslaan 281 IDM, S8, B-9000 Gent, Belgium |
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Publisher |
Royal Flemish Academy of Belgium |
Place of Publication |
Brussels |
Editor |
B. Van de Walle, B. Carle |
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Language |
English |
Summary Language |
English |
Original Title |
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Series Editor |
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Series Title |
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Abbreviated Series Title |
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Series Volume |
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Series Issue |
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Edition |
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ISSN |
2411-3387 |
ISBN |
9076971099 |
Medium |
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Track |
POSTER SESSION |
Expedition |
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Conference |
2nd International ISCRAM Conference on Information Systems for Crisis Response and Management |
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Notes |
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Approved |
no |
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Call Number |
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Serial |
905 |
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Author |
Kui Wang; Jose Marti; Ming Bai; K.D. Srivastava |
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Title |
Optimal decision maker algorithm for disaster response management with I2Sim applications |
Type |
Conference Article |
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Year |
2012 |
Publication |
ISCRAM 2012 Conference Proceedings – 9th International Conference on Information Systems for Crisis Response and Management |
Abbreviated Journal |
ISCRAM 2012 |
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Volume |
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Issue |
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Pages |
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Keywords |
Algorithms; Computer software; Disasters; Emergency services; Information systems; Lagrange multipliers; Optimization; Human-readable; I2Sim toolbox; Infrastructure interdependencies; Infrastructure resources; Infrastructures interdependencies; Optimization algorithms; Software simulation; University of British Columbia; Decision making |
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Abstract |
Disaster response management has become an important area of research in recent years, with authorities spending more resources in the area. Infrastructure resource interdependencies are key critical points for a system to operate optimally. After a disaster occurs, infrastructures would have sustained certain degrees of damage, the allocation of limited resources to maximize human survival becomes a top priority. The I2Sim (Infrastructures Interdependencies Simulator) research group at the University of British Columbia (UBC) has developed a software simulation toolbox to help authorities plan for disaster responses. This paper presents an optimization decision algorithm based on Lagrange multipliers, which provides the theoretical basis for I2Sim software decision maker layer. There is a simple scenario of three hospitals constructed with the I2Sim toolbox to illustrate the interdependencies of water and electricity. © 2012 ISCRAM. |
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Corporate Author |
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Publisher |
Simon Fraser University |
Place of Publication |
Vancouver, BC |
Editor |
L. Rothkrantz, J. Ristvej, Z.Franco |
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Language |
English |
Summary Language |
English |
Original Title |
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Series Editor |
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Abbreviated Series Title |
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Series Volume |
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Series Issue |
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Edition |
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ISSN |
2411-3387 |
ISBN |
9780864913326 |
Medium |
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Track |
Track Decision Support Methods for Complex Crises |
Expedition |
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Conference |
9th International ISCRAM Conference on Information Systems for Crisis Response and Management |
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Notes |
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Approved |
no |
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Call Number |
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Serial |
235 |
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Author |
Duncan T. Wilson; Glenn I. Hawe; Graham Coates; Roger S. Crouch |
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Title |
Estimating the value of casualty health information to optimization-based decision support in response to major incidents |
Type |
Conference Article |
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Year |
2012 |
Publication |
ISCRAM 2012 Conference Proceedings – 9th International Conference on Information Systems for Crisis Response and Management |
Abbreviated Journal |
ISCRAM 2012 |
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Volume |
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Issue |
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Pages |
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Keywords |
Algorithms; Combinatorial optimization; Decision support systems; Information systems; Optimization; Accurate modeling; Computational experiment; Decision supports; Emergency response; Health informations; Optimization algorithms; Uncertain features; Work-in-progress; Emergency services |
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Abstract |
In this paper we describe a work-in-progress decision support program designed for use in the response to major incidents in the UK. The proposed program is designed for use in a continuous fashion, where the updating of its model, the search for solutions to the model through an optimization algorithm, and the issuing of these solutions are carried out concurrently. The model facilitates the inclusion of dynamic and uncertain features of emergency response. The potential of such an approach to deliver high-quality response plans through enabling more accurate modeling is evaluated through focusing on the case of casualty health information. Computational experiments show there is significant value in monitoring the dynamic and uncertain health progression of casualties and updating the model accordingly. © 2012 ISCRAM. |
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Address |
School of Engineering and Computing Sciences, Durham University, Durham, United Kingdom |
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Corporate Author |
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Thesis |
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Publisher |
Simon Fraser University |
Place of Publication |
Vancouver, BC |
Editor |
L. Rothkrantz, J. Ristvej, Z.Franco |
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Language |
English |
Summary Language |
English |
Original Title |
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Series Editor |
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Series Title |
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Abbreviated Series Title |
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Series Volume |
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Series Issue |
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Edition |
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ISSN |
2411-3387 |
ISBN |
9780864913326 |
Medium |
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Track |
Track Decision Support Methods for Complex Crises |
Expedition |
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Conference |
9th International ISCRAM Conference on Information Systems for Crisis Response and Management |
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Notes |
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Approved |
no |
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Call Number |
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Serial |
240 |
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Author |
Duncan T. Wilson; Glenn I. Hawe; Graham Coates; Roger S. Crouch |
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Title |
Scheduling response operations under transport network disruptions |
Type |
Conference Article |
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Year |
2013 |
Publication |
ISCRAM 2013 Conference Proceedings – 10th International Conference on Information Systems for Crisis Response and Management |
Abbreviated Journal |
ISCRAM 2013 |
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Volume |
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Issue |
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Pages |
683-687 |
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Keywords |
Algorithms; Decision theory; Disasters; Emergency services; Information systems; Optimization; Stochastic systems; Disaster response; Optimization algorithms; Predictive performance; Real-time information; Road transport networks; Routing; Scheduling problem; Transport networks; Scheduling |
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Abstract |
Modeling the complex decision problems faced in the coordination of disaster response as a scheduling problem to be solved using an optimization algorithm has the potential to deliver efficient and effective support to decision makers. However, much of the utility of such a model lies in its ability to accurately predict the outcome of any proposed solution. The stochastic nature of the disaster response environment can make such prediction difficult. In this paper we examine the effect of unknown disruptions to the road transport network on the utility of a disaster response scheduling model. The effects of several levels of disruption are measured empirically and the potential of using real-time information to revise model parameters, and thereby improve predictive performance, is evaluated. |
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Address |
School of Engineering and Computing Sciences, Durham University, Durham DH1 3LE, United Kingdom |
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Corporate Author |
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Thesis |
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Publisher |
Karlsruher Institut fur Technologie |
Place of Publication |
KIT; Baden-Baden |
Editor |
T. Comes, F. Fiedrich, S. Fortier, J. Geldermann and T. Müller |
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Language |
English |
Summary Language |
English |
Original Title |
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Series Editor |
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Series Title |
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Abbreviated Series Title |
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Series Volume |
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Series Issue |
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Edition |
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ISSN |
2411-3387 |
ISBN |
9783923704804 |
Medium |
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Track |
Intelligent Systems |
Expedition |
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Conference |
10th International ISCRAM Conference on Information Systems for Crisis Response and Management |
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Notes |
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Approved |
no |
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Call Number |
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Serial |
1093 |
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Author |
Matt Wolff |
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Title |
Unsupervised methods for detecting a malicious insider |
Type |
Conference Article |
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Year |
2010 |
Publication |
ISCRAM 2010 – 7th International Conference on Information Systems for Crisis Response and Management: Defining Crisis Management 3.0, Proceedings |
Abbreviated Journal |
ISCRAM 2010 |
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Volume |
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Issue |
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Pages |
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Keywords |
Information systems; Natural language processing systems; Network security; Unsupervised learning; Insider Threat; Malicious insiders; Masquerade attacks; Supervised algorithm; Unsupervised algorithms; Unsupervised method; User masquerades; Algorithms |
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Abstract |
One way a malicious insider can attack a network is by masquerading as a different user. Various algorithms have been proposed in an effort to detect when a user masquerade attack has occurred. In this paper, two unsupervised algorithms are proposed with the intended goal of detecting user masquerade attacks. The effectiveness of these two unsupervised algorithms are then compared against supervised algorithms. |
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Address |
University of Hawaii, United States |
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Corporate Author |
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Thesis |
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Publisher |
Information Systems for Crisis Response and Management, ISCRAM |
Place of Publication |
Seattle, WA |
Editor |
S. French, B. Tomaszewski, C. Zobel |
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Language |
English |
Summary Language |
English |
Original Title |
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Series Editor |
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Series Title |
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Abbreviated Series Title |
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Series Volume |
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Series Issue |
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Edition |
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ISSN |
2411-3387 |
ISBN |
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Medium |
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Track |
Special Session: Information Credibility, Trust, Privacy and Security in Information Systems for Emergency Management |
Expedition |
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Conference |
7th International ISCRAM Conference on Information Systems for Crisis Response and Management |
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Notes |
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Approved |
no |
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Call Number |
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Serial |
1097 |
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