Forthcoming and Online First Articles

International Journal of Space-Based and Situated Computing

International Journal of Space-Based and Situated Computing (IJSSC)

Forthcoming articles have been peer-reviewed and accepted for publication but are pending final changes, are not yet published and may not appear here in their final order of publication until they are assigned to issues. Therefore, the content conforms to our standards but the presentation (e.g. typesetting and proof-reading) is not necessarily up to the Inderscience standard. Additionally, titles, authors, abstracts and keywords may change before publication. Articles will not be published until the final proofs are validated by their authors.

Forthcoming articles must be purchased for the purposes of research, teaching and private study only. These articles can be cited using the expression "in press". For example: Smith, J. (in press). Article Title. Journal Title.

Articles marked with this shopping trolley icon are available for purchase - click on the icon to send an email request to purchase.

Online First articles are published online here, before they appear in a journal issue. Online First articles are fully citeable, complete with a DOI. They can be cited, read, and downloaded. Online First articles are published as Open Access (OA) articles to make the latest research available as early as possible.

Open AccessArticles marked with this Open Access icon are Online First articles. They are freely available and openly accessible to all without any restriction except the ones stated in their respective CC licenses.

Register for our alerting service, which notifies you by email when new issues are published online.

International Journal of Space-Based and Situated Computing (2 papers in press)

Regular Issues

  • Research on smart maternal and child medical service platform based on artificial intelligence applications   Order a copy of this article
    by Furong Cheng, Biwei Liu, Li Wang, Kaiwen Wang 
    Abstract: This paper designed and constructed the intelligent maternal and child service platform based on the application of artificial intelligence, aggregated the maternal and child business data and social health business data within the region, explored the application of artificial intelligence algorithm in fetal monitoring and B-ultrasound imaging combining with online and offline business, improved the professional level of clinical diagnosis and treatment with the help of big data and artificial intelligence technology, and further innovated the primary service mode by deep learning. On the basis of illustrating maternal and child health care cloud public service platform requirements, this article introduces the function composition and key technologies of the platform system, and discusses the security deployment strategy and innovation points of the platform for reference.
    Keywords: Intelligent maternal and child service platform;Internet platform;Big data;Maternal and child health care;Cloud public service platform.
    DOI: 10.1504/IJSSC.2023.10060712
     
  • Value and limitations of Color Doppler Flow Imaging in the diagnosis of Fetal Growth Restriction   Order a copy of this article
    by Haiyun Lv, Biwei Liu, Siya Qiu, Yujie Li, Li Wang, Kaiwen Wang 
    Abstract: The definition of foetal growth restriction (FGR) refers to the situation where foetal growth fails to reach its genetic potential due to various pathological factors such as maternal, foetal, and placental influences (3). Currently, there is a lack of a definitive gold standard for diagnosing FGR (1). As a result, the diagnosis of FGR present certain challenges in the prenatal period. This study primarily focuses on the analysis of the diagnostic value of different gestational periods of colour Doppler flow imaging (CDFI) in detecting clinical parameters for FGR. Additionally, it aims to assess the accuracy of a neural network model used for early screening prediction of FGR. The results revealed that the clinical parameters during different gestational periods hold significant clinical relevance in aiding the diagnosis of FGR. Moreover, the RBF neural network predictive model shows potential in assisting the diagnosis of potential FGR in foetuses.
    Keywords: foetal growth restriction; Color Doppler Flow Imaging; umbilical artery blood flow parameters; RBF neural network.
    DOI: 10.1504/IJSSC.2023.10060743
     

 

Return to top