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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

  • Czech description

Classification

  • Type

    D - Article in proceedings

  • CEP classification

  • 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