• Mi Re-Unir
    Búsqueda Avanzada
    JavaScript is disabled for your browser. Some features of this site may not work without it.
    Ver ítem 
    •   Inicio
    • RESULTADOS DE INVESTIGACIÓN
    • Artículos Científicos WOS y SCOPUS
    • Ver ítem
    •   Inicio
    • RESULTADOS DE INVESTIGACIÓN
    • Artículos Científicos WOS y SCOPUS
    • Ver ítem

    mlCAF: Multi-Level Cross-Domain Semantic Context Fusioning for Behavior Identification

    Autor: 
    Asif Razzaq, Muhammad
    ;
    Villalonga, Claudia (1)
    ;
    Sungyoung, Lee
    ;
    Akhtar, Usman
    ;
    Ali, Maqbool
    ;
    Kim, Eun-Soo
    ;
    Masood Khattak, Asad
    ;
    Seung, Hyonwoo
    ;
    Hur, Taeho
    ;
    Bang, Jaehun
    ;
    Kim, Dohyeong
    ;
    Ali Khan, Wajahat
    Fecha: 
    10/2017
    Palabra clave: 
    context-awareness; ontologies; reasoning; fusioning; human behavior identification; Scopus; JCR
    Tipo de Ítem: 
    Articulo Revista Indexada
    URI: 
    https://reunir.unir.net/handle/123456789/6322
    DOI: 
    https://doi.org/10.3390/s17102433
    Dirección web: 
    http://www.mdpi.com/1424-8220/17/10/2433
    Resumen:
    The emerging research on automatic identification of user’s contexts from the cross-domain environment in ubiquitous and pervasive computing systems has proved to be successful. Monitoring the diversified user’s contexts and behaviors can help in controlling lifestyle associated to chronic diseases using context-aware applications. However, availability of cross-domain heterogeneous contexts provides a challenging opportunity for their fusion to obtain abstract information for further analysis. This work demonstrates extension of our previous work from a single domain (i.e., physical activity) to multiple domains (physical activity, nutrition and clinical) for context-awareness. We propose multi-level Context-aware Framework (mlCAF), which fuses the multi-level cross-domain contexts in order to arbitrate richer behavioral contexts. This work explicitly focuses on key challenges linked to multi-level context modeling, reasoning and fusioning based on the mlCAF open-source ontology. More specifically, it addresses the interpretation of contexts from three different domains, their fusioning conforming to richer contextual information. This paper contributes in terms of ontology evolution with additional domains, context definitions, rules and inclusion of semantic queries. For the framework evaluation, multi-level cross-domain contexts collected from 20 users were used to ascertain abstract contexts, which served as basis for behavior modeling and lifestyle identification. The experimental results indicate a context recognition average accuracy of around 92.65% for the collected cross-domain contexts.
    Mostrar el registro completo del ítem
    Este ítem aparece en la(s) siguiente(s) colección(es)
    • Artículos Científicos WOS y SCOPUS

    Estadísticas de uso

    Año
    2012
    2013
    2014
    2015
    2016
    2017
    2018
    2019
    2020
    2021
    2022
    Vistas
    0
    0
    0
    0
    0
    0
    36
    129
    59
    49
    30
    Descargas
    0
    0
    0
    0
    0
    0
    0
    0
    0
    0
    0

    Ítems relacionados

    Mostrando ítems relacionados por Título, autor o materia.

    • Spectral phase estimation based on deep neural networks for single channel speech enhancement 

      Saleem, N.; Khattak, Muhammad Irfan; Verdú, Elena (1) (Journal of Communications Technology and Electronics, 12/2019)
      Majority of speech processing algorithms operate only with the spectral magnitude, leaving spectral phase unstructured and unexplored. With recent advancement in deep neural networks (DNNs), the phase processing became ...
    • On improvement of speech intelligibility and quality: a survey of unsupervised single channel speech enhancement algorithms 

      Saleem, Nasir; Khattak, Muhammad Irfan; Verdú, Elena (1) (International Journal of Interactive Multimedia and Artificial Intelligence, 06/2020)
      Many forms of human communication exist; for instance, text and nonverbal based. Speech is, however, the most powerful and dexterous form for the humans. Speech signals enable humans to communicate and this usefulness of ...
    • Automated Detection of COVID-19 using Chest X-Ray Images and CT Scans through Multilayer-Spatial Convolutional Neural Networks 

      Khattak, Muhammad Irfan; Al-Hasan, Mu'ath; Jan, Atif; Saleem, Nasir; Verdú, Elena (1); Khurshid, Numan (International Journal of Interactive Multimedia and Artificial Intelligence, 2021)
      The novel coronavirus-2019 (Covid-19), a contagious disease became a pandemic and has caused overwhelming effects on the human lives and world economy. The detection of the contagious disease is vital to avert further ...

    Mi cuenta

    AccederRegistrar

    ¿necesitas ayuda?

    Manual de UsuarioAutorización TFG-M

    Listar

    todo Re-UnirComunidades y coleccionesPor fecha de publicaciónAutoresTítulosPalabras claveTipo documentoTipo de accesoEsta colecciónPor fecha de publicaciónAutoresTítulosPalabras claveTipo documentoTipo de acceso






    Aviso Legal Política de Privacidad Política de Cookies Cláusulas legales RGPD
    © UNIR - Universidad Internacional de La Rioja
     
    Aviso Legal Política de Privacidad Política de Cookies Cláusulas legales RGPD
    © UNIR - Universidad Internacional de La Rioja