Forthcoming articles

 


International Journal of Knowledge Engineering and Data Mining

 

These articles have been peer-reviewed and accepted for publication in IJKEDM, 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 Articles will not be published until the final proofs are validated by their authors.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

 

  • Designing Sales Budget Forecasting and Revision System by Using Optimization Methods   Order a copy of this article
    by Kaban Koochakpour, M.J. Tarokh 
    Abstract: The sales procedures are the most important factors for keeping companies profitable. Sales and budget sales are considered as important parameters influencing all other decision variables in an organization. Therefore, in this research a coherent solution has been proposed for forecasting sales besides revising it continuously by ANFIS model with consideration of time series relations. For more accuracy in forecasting, the solution has been examined by BPN and PSO as optimization methods. The comparison between taken prediction and the real data shows that PSO method can optimize some parts of prediction in contrast to the rest which is more coincident to the output of BPN analysis. As a consequence, an integrated system including them both, has been designed. This system uses them depending on their abilities to optimize each part, so it can produce more precise results relatively.
    Keywords: sales forecast; adaptive neuro fuzzy inference system; ANFIS; time series analysis; PSO and BPN methods; hybrid method.
    DOI: 10.1504/IJKEDM.2017.10004265
     
  • Pattern Mining and Process Modelling of Collaborative Interaction Data in an Online Multi-Tabletop Learning Environment   Order a copy of this article
    by Parham Porouhan, Wichian Premchaiswadi 
    Abstract: This research builds on the intersection of a web-based (online) multi-interactive multi-tabletop collaborative environment (so-called M-ITCL) and process mining process discovery algorithms applied on the collaborative interaction data (event logs) previously collected from an authentic learning classroom. The main focus of the study was to investigate which process mining algorithm could lead to generation of process models that differentiate (replay) the events correctly with 100% level of fitness, precision, generalization and simplicity. The results showed that Alpha algorithm resulted in the generation of process models with good simplicity but with poor precision and generalization. Heuristic algorithm resulted in the generation of process models with good precision but with poor generalization and simplicity. Fuzzy algorithm resulted in generation of rather simple process models with good precision and generalization. Moreover, the models/graphs generated through Fuzzy algorithm could differentiate all of the cases correctly with 100% level of fitness as a validation measure.
    Keywords: human-computer interaction; process mining; computer-supported collaborative learning; educational data mining; alpha algorithm; heuristic miner algorithm; fuzzy miner; analysis of collaborative interactions; interactive table computers; tabletops; concept mapping.
    DOI: 10.1504/IJKEDM.2017.10004266