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Enhanced Approach Using Reduced SBTFD Features and Modified Individual Behavior Estimation for Crowd Condition Prediction

The result's identifiers

  • Result code in IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F62690094%3A18450%2F19%3A50015728" target="_blank" >RIV/62690094:18450/19:50015728 - isvavai.cz</a>

  • Result on the web

    <a href="https://www.mdpi.com/1099-4300/21/5/487" target="_blank" >https://www.mdpi.com/1099-4300/21/5/487</a>

  • DOI - Digital Object Identifier

    <a href="http://dx.doi.org/10.3390/e21050487" target="_blank" >10.3390/e21050487</a>

Alternative languages

  • Result language

    angličtina

  • Original language name

    Enhanced Approach Using Reduced SBTFD Features and Modified Individual Behavior Estimation for Crowd Condition Prediction

  • Original language description

    Sensor technology provides the real-time monitoring of data in several scenarios that contribute to the improved security of life and property. Crowd condition monitoring is an area that has benefited from this. The basic context-aware framework (BCF) uses activity recognition based on emerging intelligent technology and is among the best that has been proposed for this purpose. However, accuracy is low, and the false negative rate (FNR) remains high. Thus, the need for an enhanced framework that offers reduced FNR and higher accuracy becomes necessary. This article reports our work on the development of an enhanced context-aware framework (EHCAF) using smartphone participatory sensing for crowd monitoring, dimensionality reduction of statistical-based time-frequency domain (SBTFD) features, and enhanced individual behavior estimation (IBEenhcaf). The experimental results achieved 99.1% accuracy and an FNR of 2.8%, showing a clear improvement over the 92.0% accuracy, and an FNR of 31.3% of the BCF.

  • Czech name

  • Czech description

Classification

  • Type

    J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database

  • 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

    2019

  • Confidentiality

    S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Data specific for result type

  • Name of the periodical

    ENTROPY

  • ISSN

    1099-4300

  • e-ISSN

  • Volume of the periodical

    21

  • Issue of the periodical within the volume

    5

  • Country of publishing house

    CH - SWITZERLAND

  • Number of pages

    27

  • Pages from-to

    "Article Number: 487"

  • UT code for WoS article

    000472675900051

  • EID of the result in the Scopus database

    2-s2.0-85066614896