Forthcoming Articles

International Journal of Intelligent Systems Design and Computing

International Journal of Intelligent Systems Design and Computing (IJISDC)

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 Articles will not be published until the final proofs are validated by their authors.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 Intelligent Systems Design and Computing (2 papers in press)

Regular Issues

  • The conceptual frameworks for global acceptance of artificial intelligence in the post-pandemic era   Order a copy of this article
    by Efosa Carroll Idemudia 
    Abstract: In 2024, the generative AI market was valued at $128 billion. To date, numerous firms, organisations, governments, and institutions have invested millions and billions of dollars in artificial intelligence to make informed decisions and gain a competitive advantage. To provide a holistic view and insights on how companies and institutions can utilise artificial intelligence to solve complex real-world problems, make informed decisions, and gain a competitive advantage in the post-pandemic era, we conducted our study. The theoretical background for our models and frameworks is the stakeholder theory. Our models and frameworks provide insights and understanding into how firms, companies, organisations, governments, and institutions can adapt to and adopt a wide range of artificial intelligence platforms. Our frameworks identify the key variables that influence the acceptance and usage of artificial intelligence. Our study has many managerial and research implications.
    Keywords: stakeholder theory; post-pandemic era; data; artificial intelligence; diverse team; algorithm design; public perception; environmental factors.
    DOI: 10.1504/IJISDC.2026.10077585
     
  • A survey of textual information extraction methods for knowledge graph construction   Order a copy of this article
    by Rui Chen, Hang Li 
    Abstract: Knowledge graphs play an increasingly important role in cognitive intelligence applications, making efficient and accurate extraction of structured knowledge from unstructured text a core challenge in natural language processing. This paper presents a systematic review of textual information extraction methods for knowledge graph construction, covering key subtasks such as named entity recognition, relation extraction, attribute extraction, and event extraction. We review the evolution of extraction techniques from rule-based and statistical approaches to deep learning models and pretrained language model-based paradigms. Particular attention is given to joint extraction methods designed to alleviate error propagation in traditional pipeline architectures. Furthermore, the survey discusses emerging extraction frameworks based on large language models, highlighting the potential of prompt-based and in-context learning for more flexible and generalised knowledge extraction. Finally, we summarise representative application scenarios and analyse major challenges, including low-resource adaptability, complex semantic modelling, and dynamic knowledge graph construction.
    Keywords: knowledge graph construction; information extraction; named entity recognition; NER; relation extraction; pretrained language models; PLMs.
    DOI: 10.1504/IJISDC.2026.10078923
     

 

Return to top