Forthcoming Articles

International Journal of Manufacturing Technology and Management

International Journal of Manufacturing Technology and Management (IJMTM)

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 Articles will not be published until the final proofs are validated by their authors.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 also listed here. 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 Manufacturing Technology and Management (One paper in press)

Special Issue on: Big Data and AI for Process Innovation in the Industry 4.0 Era

  • Research on total amount prediction of import and export trade data based on combined predicting model   Order a copy of this article
    by Jianxin Chen, Xiaoke Zhao 
    Abstract: In order to overcome the problems of low index significance, low prediction efficiency and low prediction accuracy in the process of prediction of import and export trade data, a new research method for total amount prediction of import and export trade data based on combined prediction model. Combine neural network prediction method and support vector machine prediction method to build a combined prediction model. Through the back propagation process, it can reduce the error between the expected and the actual output, and modify the network threshold and weight, so as to realize the classification of samples in high-dimensional space in support vector machine, and the two methods are combined to complete the prediction of total import and export trade data. The experimental results show that the significance of the proposed method is close to 100%, the prediction time is within 0.5 min, and the prediction accuracy is more than 90%.
    Keywords: Combination predicting model; Import and export trade; Data predicting; Influencing factors.

 

Return to top