Forthcoming articles

International Journal of Knowledge Engineering and Data Mining

International Journal of Knowledge Engineering and Data Mining (IJKEDM)

These 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 Articles will not be published until the final proofs are validated by their authors.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 Knowledge Engineering and Data Mining (2 papers in press)

Regular Issues

  • Using cognitive radio to deliver green communications: A reinforcement learning approach   Order a copy of this article
    by Badr Benmammar, Mohammed Salih BENDELLA, FRANCINE KRIEF 
    Abstract: In this paper, we are interested in the concept of green networking and the solutions brought by cognitive radio technology in this field. The purpose of this work is to find a mechanism that minimises energy consumption by integrating it in a cognitive radio network. For this, we have used the Q-learning algorithm, a reinforcement learning technique that will help the cognitive users to find the optimal channel that has a low transmission power by guaranteeing the needs of their application and therefore a reduction in the energy consumption of their batteries while minimising interference in the network. The obtained results are very satisfactory because we have shown that through the integration of the Q-learning algorithm in a cognitive radio network, we have been able to significantly reduce the energy consumption and the interferences of the cognitive radio terminals and therefore we have satisfied the service of green networking.
    Keywords: Green Networking; Cognitive Radio; Energy Efficiency; Artificial Intelligence; Reinforcement Learning; Markov Decision Process; Knowledge Base.
    DOI: 10.1504/IJKEDM.2019.10023762
     
  • Supplier Selection on Agrifood Supply Chain: A Delphi-AHP-TOPSIS methodology   Order a copy of this article
    by Mohamed Amine CHERIER, Sidi Mohammed MELIANI 
    Abstract: Supplier selection is important strategic decision for agrifood supply chain management. This decision problem is frequently complex and unstructured. This paper aim presents a Delphi-AHP-TOPSIS framework for supplier selection problem. The making-decision methodology has been divided into three steps. Firstly, Delphi method is used to identify the main criteria and sub-criteria for the evaluation process. Secondly, analytic hierarchy process (AHP), where the evaluation of criteria is applied in order to prioritise each criterion based to expert's judgements. Thirdly, the technique for order preference by similarity to ideal solution (TOPSIS) has been used to rank the alternatives for final decision. Finally, in order to clarify the use of proposed combined method, a numerical example was applied to select supplier on Algerian tomato processing company. The analyses of the results have shown that the proposed framework is an appropriate tool for the problem of supplier selection.
    Keywords: supplier selection; Delphi; AHP; TOPSIS; agrifood supply chain; tomato.
    DOI: 10.1504/IJKEDM.2019.10024042