Forthcoming and Online First Articles

International Journal of Mobile Network Design and Innovation

International Journal of Mobile Network Design and Innovation (IJMNDI)

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International Journal of Mobile Network Design and Innovation (2 papers in press)

Regular Issues

  • Nature-Inspired Based Ensemble Feature Selection and Stacked Ensemble Classifier Fusion for Android Malware Detection   Order a copy of this article
    by Anuja A. Rajan, R. Durga 
    Abstract: Android is widely used for tablets and smartphones. Thus, Android app malware has grown quickly in recent years. Malware detection in these apps is effective with ML algorithms. A reliable and effective malware detection approach is still difficult due to the vast number of features and high-dimensional dataset that provides the lowest accuracy despite research and industry efforts. Feature selection involves finding and removing features from a dataset while retaining class label variance. Using dynamic analysis of Android malware samples, this study introduces the Nature-Inspired Ensemble Feature Selection (NIEFS) and Stacked Ensemble Classifier Fusion (SECF) multi-classification models. The NIEFS model uses evolutionary computation methods like FMGOA, LEFPIO, and COSSA to remove redundant or irrelevant features and select relevant ones to improve detection accuracy. Multilayer Stacked Ensemble Classifier Fusion (SECF) integrates EC-created malware outputs using a Mutual Information (MI)-based ensemble approach, which has good detection accuracy. Machine learning methods like J48, mean weight deep belief network (MWDBN), REPTree, and Voted Perceptron can be combined. Finally, classifier performance was tested using MATLABR2020a, Precision (Pre), Recall (Rec), F-measure (FM), and Weighted F-measure.
    Keywords: Android Malware Detection (AMD); Nature-Inspired Based Ensemble Feature Selection (NIEFS); Mean Weight Deep Belief Network (MWDBN); Ensemble Technique;.
    DOI: 10.1504/IJMNDI.2025.10071599
     
  • Machine Learning-Based Prediction of Allocative Localization Error in Wireless Sensor Networks   Order a copy of this article
    by Guo LI, Hongyu SHENG 
    Abstract: There may be a better way to deal with and write about any mistakes that happen in WSNs if they happen in a style called Allocative Localization Errors, or ALEs. Three important schemes are Adaptive Neuro-Fuzzy Inference Systems, Stacking Regression, and AdaBoostRegressors. Multiple regression methods are used by the stacking regression model to guess wrong traits in more than one variable. As part of the study, MOA was used to make some well-known ways of making predictions more correct. Someone came up with a brand-new ensemble model called STASA+MFO for this work. It takes the Stacking R model and adds ADA and MOA to it. With an R2 value of 0.9913, this method did better than the others that were tried. The ADA+ANFIS model had an R2 value of 0.9810, and the ANMF model (ADA with MO) had an R2 value of 0.9824.
    Keywords: wireless sensor networks; allocative localization errors; machine learning algorithm; metaheuristic algorithms.
    DOI: 10.1504/IJMNDI.2025.10071735
     

 

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