Forthcoming articles


International Journal of Convergence Computing


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 Articles will not be published until the final proofs are validated by their authors.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


Articles marked with this Open Access icon 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 of IJConvC are published online.


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


International Journal of Convergence Computing (3 papers in press)


Regular Issues


  • A New Feature Extraction Technique For Classifiers Using Self-Organizing Map   Order a copy of this article
    by Prasenjit Dey, Tandra Pal 
    Abstract: Neural network classifiers suffer from the overfitting problem which reduces its generalization capability. Due to generalization, a trained classifier will always have good test accuracy. The objective of the proposed work is to improve the input space of the data set which in consequence will increase the generalization of the classifiers. For this purpose, we have proposed a new feature extraction technique based on self-organizing map (SOM). Initially, two-dimensional SOM network is trained to achieve topological ordering in the input data set. Then, a Gaussian function is used in each node of the output map of SOM network. It processes the input patterns sequentially and produces $m^2$ dimensional new representation corresponding to each input pattern, where $m^2$ is the number of nodes present in the output layer. Thereafter, classifiers like probabilistic neural network (PNN) or multilayer perceptron (MLP) is used over this new representation of the input patterns. The whole process creates a new classifier based on hybrid neural network model. We have used twelve standard classification data sets and compared proposed model with conventional PNN and MLP classifiers. Comparison of results shows the superiority of the proposed method. Wilcoxon signed rank test also shows that our SOM-based model used for transformation of feature representation improves the performance of the classifiers.
    Keywords: Feature extraction; Gaussian function; generalization; multilayer perceptron; probabilistic neural network; self-organizing map.

  • Exploration of Neural Network Models for Defect Detection and Classification   Order a copy of this article
    by B. V. Ajay Prakash, D.V. Ashoka, V.N. Manjunath Aradhya 
    Abstract: In order to overcome the software development challenges like delivering a project on time`, developing quality software products and reducing development cost, software industries commonly uses defect detection software tools to manage quality in software products. Defects are detected and classified based on their severity, this can be automated in order to reduce the development time and cost. Nowadays to extract useful knowledge from large software repositories engineers and researchers are using data mining techniques. In this paper, software defect detection and classification method is proposed and neural network models such as Generalised Regression Neural Network (GRNN) and Probabilistic Neural Network model (PNN) are integrated to identify, classify the defects from large software repository. Based on defects severity proposed method discussed in this paper focuses on three layers: core, abstraction and application layer. The performance accuracy of the proposed model is compared with MLP and J48 classifiers.
    Keywords: Software Bugs Tracking; Neural Network Models; Software Quality Assurance; Bug Tracking; Defect Classification.

  • Identification of AIDS Disease Severity Based on Computational Intelligence TechniquesUsing Clonal Selection Algorithm   Order a copy of this article
    by Dharmaiah Devarapalli, Srikanth Panigrahi, M.R. Narasinga Rao, Jonnadula Venkata Rao 
    Abstract: Motivation: The mining bioinformatics data is newly formed area in process of between bioinformatics and data mining. The process of developing the algorithmsmanipulated based on computational intelligence. The agitation cases be created everywhere in the world concerning of the Acquired Immunodeficiency syndrome (AIDS) disease which are complex. Data were collected throughout self-administered mail survey share nationwide at clinical and drug treatment centers and AIDS service organizations. Every country is facing the problem about AIDS. According to the occurring at a time immediately before the present survey of world health organization (WHO).Human Immunodeficiency Virus (HIV) is the retro virus which is the family retro viridae and it is a group of viruses that deficiency of the immune system, Especially that causing the death of many CD4 count cells, which coordinates the human immune system response extremely without invitation like intrudes. This project is identifying Patients severity and it is useful Doctors. Results: In this present machine learning algorithm of the clonal selection algorithm more effective diagnosis and optimize the data of AIDS disease. Computational intelligence techniques based on test of the performed the techniques used as AIDS data set. Calculated amore effective of rule length based on fitness function using sensitivity, specificity, comprehensibility are computed. Those techniques achievedto promising accurate results.
    Keywords: AIDS data; CD4 Cells count,fitness function; intelligence technique; Clonal Selection Algorithm.