ContextPCA: Predicting Context-Aware Smartphone Apps Usage Based On Machine Learning Techniques
This paper mainly formulates the problem of predicting context-aware smartphone <i>apps usage</i> based on machine learning techniques. In the real world, people use various kinds of smartphone apps differently in different contexts that include both the <i>user-centric</i> c...
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doaj-d66ca2331f9545bcb56ba2212d89c6f22020-11-25T02:28:44ZengMDPI AGSymmetry2073-89942020-04-011249949910.3390/sym12040499ContextPCA: Predicting Context-Aware Smartphone Apps Usage Based On Machine Learning TechniquesIqbal H. Sarker0Yoosef B. Abushark1Asif Irshad Khan2Department of Computer Science and Software Engineering, Swinburne University of Technology, Melbourne VIC-3122, AustraliaComputer Science Department, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi ArabiaComputer Science Department, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi ArabiaThis paper mainly formulates the problem of predicting context-aware smartphone <i>apps usage</i> based on machine learning techniques. In the real world, people use various kinds of smartphone apps differently in different contexts that include both the <i>user-centric</i> context and <i>device-centric</i> context. In the area of artificial intelligence and machine learning, <i>decision tree</i> model is one of the most popular approaches for predicting context-aware smartphone usage. However, real-life smartphone apps usage data may contain <i>higher dimensions of contexts</i>, which may cause several issues such as increases model <i>complexity</i>, may arise <i>over-fitting</i> problem, and consequently decreases the <i>prediction accuracy</i> of the context-aware model. In order to address these issues, in this paper, we present an effective <i>principal component analysis (PCA)</i> based context-aware smartphone apps prediction model, <i>“ContextPCA”</i> using decision tree machine learning classification technique. PCA is an <i>unsupervised machine learning</i> technique that can be used to separate symmetric and asymmetric components, and has been adopted in our “ContextPCA” model, in order to reduce the context dimensions of the original data set. The experimental results on smartphone apps usage datasets show that “ContextPCA” model effectively predicts context-aware smartphone apps in terms of precision, recall, f-score and ROC values in various test cases.https://www.mdpi.com/2073-8994/12/4/499mobile data analyticsmachine learningprincipal component analysisclassificationdecision treecontext-aware computing |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Iqbal H. Sarker Yoosef B. Abushark Asif Irshad Khan |
spellingShingle |
Iqbal H. Sarker Yoosef B. Abushark Asif Irshad Khan ContextPCA: Predicting Context-Aware Smartphone Apps Usage Based On Machine Learning Techniques Symmetry mobile data analytics machine learning principal component analysis classification decision tree context-aware computing |
author_facet |
Iqbal H. Sarker Yoosef B. Abushark Asif Irshad Khan |
author_sort |
Iqbal H. Sarker |
title |
ContextPCA: Predicting Context-Aware Smartphone Apps Usage Based On Machine Learning Techniques |
title_short |
ContextPCA: Predicting Context-Aware Smartphone Apps Usage Based On Machine Learning Techniques |
title_full |
ContextPCA: Predicting Context-Aware Smartphone Apps Usage Based On Machine Learning Techniques |
title_fullStr |
ContextPCA: Predicting Context-Aware Smartphone Apps Usage Based On Machine Learning Techniques |
title_full_unstemmed |
ContextPCA: Predicting Context-Aware Smartphone Apps Usage Based On Machine Learning Techniques |
title_sort |
contextpca: predicting context-aware smartphone apps usage based on machine learning techniques |
publisher |
MDPI AG |
series |
Symmetry |
issn |
2073-8994 |
publishDate |
2020-04-01 |
description |
This paper mainly formulates the problem of predicting context-aware smartphone <i>apps usage</i> based on machine learning techniques. In the real world, people use various kinds of smartphone apps differently in different contexts that include both the <i>user-centric</i> context and <i>device-centric</i> context. In the area of artificial intelligence and machine learning, <i>decision tree</i> model is one of the most popular approaches for predicting context-aware smartphone usage. However, real-life smartphone apps usage data may contain <i>higher dimensions of contexts</i>, which may cause several issues such as increases model <i>complexity</i>, may arise <i>over-fitting</i> problem, and consequently decreases the <i>prediction accuracy</i> of the context-aware model. In order to address these issues, in this paper, we present an effective <i>principal component analysis (PCA)</i> based context-aware smartphone apps prediction model, <i>“ContextPCA”</i> using decision tree machine learning classification technique. PCA is an <i>unsupervised machine learning</i> technique that can be used to separate symmetric and asymmetric components, and has been adopted in our “ContextPCA” model, in order to reduce the context dimensions of the original data set. The experimental results on smartphone apps usage datasets show that “ContextPCA” model effectively predicts context-aware smartphone apps in terms of precision, recall, f-score and ROC values in various test cases. |
topic |
mobile data analytics machine learning principal component analysis classification decision tree context-aware computing |
url |
https://www.mdpi.com/2073-8994/12/4/499 |
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