Forthcoming articles


International Journal of Adaptive and Innovative Systems


These articles have been peer-reviewed and accepted for publication in IJAIS, 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 Articles will not be published until the final proofs are validated by their authors.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 Adaptive and Innovative Systems (2 papers in press)


Regular Issues


  • Multi-Domain Intelligent System for Document Image Retrieval   Order a copy of this article
    by Donato Barbuzzi, Alessandro Massaro, Angelo Galiano, Leonardo Pellicani, Giuseppe Pirlo, Matteo Saggese 
    Abstract: This paper presents an experimental analysis on document image retrieval using a multi-domain intelligent system. More specifically, on the same document image, the combination of three different domains: layout, logo and signature is discussed. This new method analyzes every single decision provided by multi-domain system so that, in the training phase, a new sample classified with a dissimilar confidence to the previous trained samples is used to update the system. DTW, Euclidean Distance and Cosine Similarity have been used, respectively for the analysis of layout, logo and signature. Finally, the weighted combination of individual decisions was considered. The experimental results, carried out on 30 rotated forms belonging to 13 different companies, demonstrate the superiority of the proposed approach with respect to single-domain retrieval systems, based on the ANR performance index. The ANR parameter is able to evaluate the multi-domain system.
    Keywords: Document Management System; Document Image Retrieval; Multi-Expert Intelligent System; Feedback-based strategy; Instance Selection.
    DOI: 10.1504/IJAIS.2016.10011128
  • Prediction of Instantaneous Heart Rate Using Adaptive Algorithms   Order a copy of this article
    by Sarita Kansal, P.P. Bansod, Abhay Kumar 
    Abstract: In this paper, adaptive filter based on adaptive algorithms like Least Mean Square (LMS), Normalized Least Mean Square (NLMS) and Recursive Least Square (RLS) are used for the prediction of instantaneous heart rate in ECG signal. The adaptive algorithms works on the principle of optimizing the least square error by achieving wiener solution. The weights of the filter coefficients are changing, as per the changes in the signal. There is always the issue of selecting parameters of adaptive filter for adaptation, which affects the performance of it. The parameters are like step size, filter length, minimum number of iterations etc. Therefore, the performance of adaptive filter is analyzed by Mean Square Error (MSE) with varying parameters and using different adaptive algorithms. The prediction accuracy is observed by measuring parameter Mean Absolute Error (MAE). The total ten healthy records of ECG are considered to evaluate the performance of adaptive algorithms and results are presented after averaging the results of each record. The simulation results show that the adaptive algorithms NLMS and RLS have faster convergence rate with less number of iteration than LMS but the forecasting accuracy is higher in LMS compared to NLMS and RLS algorithms. The complexity of RLS algorithm is more as it takes the average value of past error, whereas in LMS and NLMS only instantaneous error is considered.
    Keywords: ECG; Instantaneous Heart Rate; Adaptive Algorithm; LMS; NLMS; RLS.