Improved stampede prediction model on context-awareness framework using machine learning techniques
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F62690094%3A18450%2F17%3A50013658" target="_blank" >RIV/62690094:18450/17:50013658 - isvavai.cz</a>
Result on the web
<a href="https://link.springer.com/chapter/10.1007%2F978-3-319-48517-1_4" target="_blank" >https://link.springer.com/chapter/10.1007%2F978-3-319-48517-1_4</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/978-3-319-48517-1_4" target="_blank" >10.1007/978-3-319-48517-1_4</a>
Alternative languages
Result language
angličtina
Original language name
Improved stampede prediction model on context-awareness framework using machine learning techniques
Original language description
The determination of stampede occurrence through abnormal behaviors is an important research in context-awareness using individual activity recognition (IAR). An application such as an intelligent smartphone for crowd monitoring using inbuilt sensors is used. Meanwhile, there are few algorithms to recognize abnormal behaviors that can lead to a stampede for mitigation of crowd disasters. This study proposed an improved stampede prediction model which can facilitate abnormal detection with k-means. It can identify cluster areas among a group of people to know susceptible places that can help to predict stampede occurrence using IAR with the help of geographical positioning system (GPS) and accelerometer sensor data. To achieve this, two research questions were formulated and answered in this paper. (i) How to determine crowd of people in an area? (ii) How to know when stampede will occur in the identified area? The experimental results on the proposed model with decision tree (DT) algorithm shows an improved performance of 98.6 %, 97.7 % and 10.9 % over 94.4 %, 95 % and 18 % in the baselines for specificity, accuracy and false-negative rate (FNR) respectively thereby reducing high false negative alarm.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
—
Continuities
S - Specificky vyzkum na vysokych skolach
Others
Publication year
2017
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Advances in intelligent systems and computing
ISBN
978-3-319-48516-4
ISSN
2194-5357
e-ISSN
neuvedeno
Number of pages
13
Pages from-to
39-51
Publisher name
Springer
Place of publication
Cham
Event location
Gadong; Brunei Darussalam
Event date
Nov 18, 2016
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
000405210000004