Forthcoming and Online First Articles

International Journal of Computational Intelligence Studies

International Journal of Computational Intelligence Studies (IJCIStudies)

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 Articles will not be published until the final proofs are validated by their authors.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

We also offer which provide timely updates of tables of contents, newly published articles and calls for papers.

International Journal of Computational Intelligence Studies (5 papers in press)

Regular Issues

  • Predictive Analytics of User Cognitive Styles in Online Shopping.   Order a copy of this article
    by Vijaya Bharathi Jagan, Jyothi M. Rao, Amiya K. Tripathy 
    Abstract: Revolution in online retail has led to a paradigm shift in customers’ shopping behaviour making customer retention relatively tougher. E-retailers need to understand more in depth about their e-customers to provide right offers to right people. Though click stream analysis has been solving e-business problems, still recommendation systems on e-commerce and digital marketing are far from perfect. Thus, a more perfect consumer behaviour model is the need of hour. This study finds that effective adoption of cognitive science in the click stream analysis can identify customers’ thinking patterns for decision-making. The proposed system adopted the conceptual framework of cognitive architecture namely adaptive control of thought rational (ACT-R) to identify various cognitive styles of online users using click stream data. The customer segments based on their cognitive styles provides a deeper insight to the e-retailers, which can be best leveraged to offer better personalised marketing advertisements and thereby increasing customer retention rate.
    Keywords: clickstream; cognitive model; decision making styles; web usage mining; customer retention; ACT-R.
    DOI: 10.1504/IJCISTUDIES.2022.10050222
     

Special Issue on: Innovative Methods and Applications in Computational Intelligence

  • Adaptive data-sharing methods for multi-agent systems using deep reinforcement learning   Order a copy of this article
    by Tomohiro Hayashida, Ichiro Nishizaki, Shinya Sekizaki, Liu Qi 
    Abstract: In general, the interaction between an agent and the environment can be described by a Markov decision process in a single-agent system (SAS). However, it is difficult to define them by a Markov decision process in a multi-agent system (MAS), and makes it difficult to learn appropriate action by the agents to avoid the difficulty, Lowe et al. have constructed data-sharing methods among the agents based on the actor-critic algorithm This paper improves the data-sharing method by limiting the data-sharing rather than all the empirical data possessed by the other agents. This paper proposes three types of training data-sharing methods and conducts simulation experiments using multiple maze environments of different complexity to indicate the effectiveness of the proposed methods. Based on the experimental results, the proposed methods have better performance than the existing methods. And this paper shows the appropriate method according to the characteristics of each target problem.
    Keywords: deep reinforcement learning; DRL; multi-agent system; MAS; data-sharing.
    DOI: 10.1504/IJCISTUDIES.2023.10049661
     
  • Modelling of Decarbonized Global and Local Supply Chain Network for Material-Based Greenhouse Gas Emission and Costs with COVID-19 Disruption and Trans-Pacific Partnership   Order a copy of this article
    by Takaki Nagao, Hiromasa Ijuin, Keisuke Nagasawa, Tetsuo Yamada 
    Abstract: COVID-19 has caused a negative impact and disruption on a global supply chain. The global supply chain is affected by many factors, such as disruption, customs duty, requirements for the reduction of greenhouse gas (GHG) emission, and trans-pacific partnership (TPP), which is a free trade agreement. A sustainable supply chain needs to reduce material-based GHG emission and total costs. However, GHG emission varies across countries because of the energy mix. Therefore, the impact of disruption and different GHG emission levels should be considered in the global supply chain design. This study models and analyses a decarbonised global and local supply chain network under conditions such as GHG regulation, COVID-19 supplier disruption and customs duty scheme by TPP, so as to minimise total material-based GHG emission and total cost by integer programming with ? constraint. Then, the results are discussed in terms of localisation, cost, GHG, and disruption.
    Keywords: global warming; integer programming; ? constraint method; life cycle assessment; LCA; customs duty; bill of materials; BOM.
    DOI: 10.1504/IJCISTUDIES.2022.10051780
     

Special Issue on: Interdisciplinary Applications and Technologies of Computer Vision

  • Performance prediction analysis of college aerobics course based on Back Propagation neural network   Order a copy of this article
    by Jianlin Su, Hao Zheng, Yanxi Chen 
    Abstract: To solve the problem of low accuracy of the performance prediction method of aerobics courses, the study proposes to combine the partial least squares regression (partial least squares
    Keywords: score prediction; BP neural network; relative error value; partial least squares regression.
    DOI: 10.1504/IJCISTUDIES.2022.10051976
     
  • Research on the optimal charging method of parallel power batteries for smart electric vehicles   Order a copy of this article
    by Yanlin Li, Zhen Li 
    Abstract: To save charging cost on the premise of ensuring stable and efficient charging of electric vehicles, an optimal charging method for parallel power batteries of intelligent electric vehicles was proposed. Through mathematical model analysis, the RC network branch is added to construct a second-order RC equivalent circuit model. Optimise battery parameters based on CRUISE simulation platform. By optimising the control of battery current, current sharing and voltage stability are achieved. Finally, the local voltage equalising charging principle of the inverter is used to redesign the equalising charging of the electric vehicle battery. The experimental results show that the relative cost and charging stability of the proposed charging method are 0.58 and 0.62 respectively, which are better than the other two charging methods. The results show that the method can meet the requirements of intelligent electric vehicles for charging stability and efficiency, and has high application value.
    Keywords: electric vehicle; battery charging; equalisation charging; equivalent circuit model; charging power.
    DOI: 10.1504/IJCISTUDIES.2022.10051977