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[Figs 8](#fig8){ref-type=”fig”} and [9](#fig9){ref-type=”fig”} summarize the proposed model and show the built up layers and parameterization. The hidden layers in the first phase is used to construct a common representation between the input and output while the second phase consists of a histogram representation to increase the detail of the output. The model is then trained with a back-propagation algorithm to extract the feature representation for the predicted output. A custom MATLAB script is used to classify the applications. The results are compared to the output generated by hand and no difference could be found. The top score applications are shown in [Fig. 9](#fig9){ref-type=”fig”} with most of the apps in the top ten scoring applications being academic apps (i.e. mathematics, calculus, and computer science).Fig. 8Proposed model to classify text documents with the proposed histogram based approach. (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)Fig. 8Fig. 9Most scored apps on the Apple App Store. (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)Fig. 9
The research attempts to address an important problem (i.e. that of the timely repair and un-installation of malware) that has not been addressed before. The proposed algorithm is simple and generic, and the evaluation shows that the proposed model is able to produce accurate results. However, the proposed model is restricted to specific app categories and the users that are trained to extract the feature representation of the apps. A weakness in the proposed system is that it uses a small dataset. The proposed model is also dependent on the data available in the app store and the time available to complete the extraction of feature representation for the classification system. In future work, we plan to expand the dataset to increase the number of apps used to evaluate the model to include other categories of apps available in the store. The next stage of the work will involve extending the proposed model to more applications available in the Apple App store. The more apps used to evaluate the proposed model, the more advanced the model will be. However, extracting
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