Forthcoming Articles

International Journal of Data Science

International Journal of Data Science (IJDS)

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 Articles will not be published until the final proofs are validated by their authors.

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International Journal of Data Science (2 papers in press)

Regular Issues

  • Research on Carbon Emission Prediction Method of Cement Industry based on Electricity Data   Order a copy of this article
    by Xuejun Li, Yi Zhang, Xingwei Liao, Jinlin Xie, Shu Zhang, Yuanlin Cheng, Hu Yu 
    Abstract: In response to Chinas double carbon goal, the countrys national emissions trading system has proposed to include the cement industry. This paper proposes using machine learning techniques to predict cement energy consumption carbon emissions and production process carbon emissions respectively. The paper shows that these emissions can be predicted accurately based on the electricity purchase volume of the cement industry and thewaste heat power generated. The electricity-carbon emission prediction model of cement industry is established based on the least squares optimisation support vector machine (SVM), and Bayes linear regression, K-nearest neighbour (KNN), SVM, multiple linear regression and BP neural network are used for comparison. Through example simulation, the electrical input variables are reasonably selected, and the advantages of using machine learning to predict the carbon of cement industry through electricity data are analysed. The feasibility and reliability of the proposed algorithm are verified by taking the electricity data and carbon emission data of a cement factory in Hunan province as an example.
    Keywords: Cement Carbon Emission; Machine Learning Algorithms; Electricity Data.
    DOI: 10.1504/IJDS.2025.10075580
     
  • Monitoring and Analysis of Public Opinion in Social Networks Based on Weibo Data Mining   Order a copy of this article
    by Wang Ning, Yulin Zhou, Yutao Li, Yingcai Ouyang 
    Abstract: This study addresses fragmented, multimodal Weibo comments and the frequent neglect of non-text elements in public-opinion analysis. Using DeepSeek-related hot-search events in early 2025 as a case, we propose a framework combining LDA topic modelling with Naive Bayes sentiment classification. Comments were collected via the Weibo API and web crawlers, then cleaned and segmented. Perplexity analysis set the number of LDA topics to five, revealing key themes such as ChinaUS science and technology interaction and cultural translation/communication. A baseline Naive Bayes classifier was then enhanced by adding emoji features, sentiment-word weighting, and neutral-decision rules. Experiments show clear gains: accuracy improves from 0.86 to 0.94 and F1 from 0.81 to 0.92. Negative-sentiment precision increases from 0.48 to 0.75, and its F1 rises from 0.65 to 0.85. The framework supports multimodal emotion recognition and social-media opinion monitoring.
    Keywords: Weibo comments; LDA topic model; Naive Bayes; sentiment analysis; emoji features.
    DOI: 10.1504/IJDS.2026.10077166
     

 

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