Forthcoming articles


International Journal of Artificial Intelligence and Soft Computing


These articles have been peer-reviewed and accepted for publication in IJAISC, 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 Articles will not be published until the final proofs are validated by their authors.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 Artificial Intelligence and Soft Computing (2 papers in press)


Regular Issues


  • A Novel Method for Network Intrusion Detection based on Nonlinear SNE and SVM   Order a copy of this article
    by Yasir Hamid, Ludovic Journaux, John Aldo Lee, M. Sugumaran 
    Abstract: In the case of Network Intrusion Detection data, pre-processing techniques have been extensively used to enhance the accuracy of the model. An ideal Intrusion Detection System (IDS) is one that has appreciable detection capability overall the group of attacks. An open research problem in this area is the lower detection rate for less frequent attacks, which result from the curse of dimensionality and imbalanced class distribution of the benchmark datasets. This work attempts to minimize the effects of imbalanced class distribution by applying random under-sampling of the majority classes and SMOTE based oversampling of minority classes. In order to alleviate the issue arising from the curse of dimensionality, this model makes use of Stochastic Neighbor Embedding a Non-Linear Dimension Reduction technique to embed the higher dimensional feature vectors in low-dimensional embedding spaces. A nonlinear Support Vector Machine with a radial basis function on a series of Gamma values was used to build the model. The results demonstrate that the proposed model with the dimension reduction has higher detection coverage for all the attack groups in the data set as well as the normal data. Results are evaluated on two benchmark datasets KDD99 and UNSW-NB15.
    Keywords: Accuracy; Classification; Dimension Reduction; Intrusion Detection; KDD99; NLDR; SNE; SVM; UNSW-N15.

  • Hierarchical Classification of Web Search Results to detect user's   Order a copy of this article
    by Salma Gaou, Pedro A. Castillo-Valdivieso 
    Abstract: User understanding in a web browsing session is a difficult subject, which attracts the attention of many researchers in this field. This article will facilitate a great impact on many Internet-based applications. For example, if a search engine has the ability to detect users intent; it could be better following the order of the search results to user needs. In this context, research has focused on the analysis for users with Result Pages Search Engine resulting from a Web application, but most methods ignore the intent of the user during the exploration of Web pages associated with the links of the resulting page that it decides to visit. In this article, we focus on the detection and understanding of the user's intent that motivate a user to search on the web. The calculation of the similarity is preceded by the formation of the vicinity of the target item, the first method used is that of the single k-means clustering approach for items in different groups. This method had limitations because of sparsity problem. To overcome this limitation and increase the accuracy of our model, we opted for a systemic approach outcome of Group Technology. This approach provides the BEA algorithm to improve communities search. It's a new way to identify the neighbourhood and solve the problem of scalability.
    Keywords: Intent user; Information search; ranking of search results; search retrieval; Group Technology; Co-Clustering; BEA algorithm.