ContextPCA: Predicting Context-Aware Smartphone Apps Usage Based On Machine Learning Techniques

Sarker, Iqbal H.; Abushark, Yoosef B.; Khan, Asif Irshad

doi:10.3390/sym12040499

Cited by 39 publications

(32 citation statements)

References 16 publications

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“…3 Macquarie University, Sydney, NSW 2109, Australia. 4 Computer Science Department, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia. 5 School of Engineering and Sciences, Tecnologico de Monterrey, Av.…”

Section: Discussionmentioning

confidence: 99%

“…In our experiment, we take into account the PCA method, which is a popular and well-known feature extraction method used in the area of data science and machine learning. PCA method can produce new brand features or components by analyzing the characteristics of the contextual datasets [4]. Technically, PCA finds the eigenvectors of a covariance matrix with the highest eigenvalues and then uses those to project the data into a new subspace of equal or fewer dimensions.…”

Section: Contextual Feature Generation and Extractionmentioning

confidence: 99%

“…Technically, PCA finds the eigenvectors of a covariance matrix with the highest eigenvalues and then uses those to project the data into a new subspace of equal or fewer dimensions. The below mathematical equations are relevant to the principal component analysis [4,12].…”

Section: Contextual Feature Generation and Extractionmentioning

confidence: 99%

“…Nowadays, machine learning classification techniques have been successfully used while building data-driven intelligent systems in various application areas including smartphone apps [3][4][5]. In the area of machine learning, a tree-like model is one of the most popular approaches for predicting context-aware smartphone usage [6,7].…”

Section: Introductionmentioning

confidence: 99%

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Context pre-modeling: an empirical analysis for classification based user-centric context-aware predictive modeling

et al. 2020

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Nowadays, machine learning classification techniques have been successfully used while building data-driven intelligent predictive systems in various application areas including smartphone apps. For an effective context-aware system, context pre-modeling is considered as a key issue and task, as the representation of contextual data directly influences the predictive models. This paper mainly explores the role of major context pre-modeling tasks, such as context vectorization by defining a good numerical measure through transformation and normalization, context generation and extraction by creating new brand principal components, context selection by taking into account a subset of original contexts according to their correlations, and eventually context evaluation, to build effective context-aware predictive models utilizing multi-dimensional contextual data. For creating models, various popular machine learning classification techniques such as decision tree, random forest, k-nearest neighbor, support vector machines, naive Bayes classifier, and deep learning by constructing a neural network of multiple hidden layers, are used in our study. Based on the context pre-modeling tasks and classification methods, we experimentally analyze user-centric smartphone usage behavioral activities utilizing their contextual datasets. The effectiveness of these machine learning context-aware models is examined by considering prediction accuracy, in terms of precision, recall, f-score, and ROC values, and has been made an empirical discussion in various dimensions within the scope of our study.

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Section: Discussionmentioning

confidence: 99%

Section: Contextual Feature Generation and Extractionmentioning

confidence: 99%

Section: Contextual Feature Generation and Extractionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Context pre-modeling: an empirical analysis for classification based user-centric context-aware predictive modeling

et al. 2020

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“…Moreover, a significant amount of research [72,[94][95][96][97] have been done on deep learning for various purposes in the area of mobile analytics. Moreover, context engineering including principal component analysis, or context correlation analysis [77,78] is another important issue to work in this area. In Table 1, we have summarized this research based on the most popular approaches and data-driven tasks within the scope of our analysis.…”

Section: Mobile Systems and Servicesmentioning

confidence: 99%

Mobile Data Science and Intelligent Apps: Concepts, AI-Based Modeling and Research Directions

et al. 2020

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Artificial intelligence (AI) techniques have grown rapidly in recent years in the context of computing with smart mobile phones that typically allows the devices to function in an intelligent manner. Popular AI techniques include machine learning and deep learning methods, natural language processing, as well as knowledge representation and expert systems, can be used to make the target mobile applications intelligent and more effective. In this paper, we present a comprehensive view on "mobile data science and intelligent apps" in terms of concepts and AI-based modeling that can be used to design and develop intelligent mobile applications for the betterment of human life in their diverse day-today situation. This study also includes the concepts and insights of various AI-powered intelligent apps in several application domains, ranging from personalized recommendation to healthcare services, including COVID-19 pandemic management in recent days. Finally, we highlight several research issues and future directions relevant to our analysis in the area of mobile data science and intelligent apps. Overall, this paper aims to serve as a reference point and guidelines for the mobile application developers as well as the researchers in this domain, particularly from the technical point of view.

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An Efficient K-Means Clustering Algorithm for Analysing COVID-19

Zubair

Iqbal

Shil

et al. 2021

Hybrid Intelligent Systems

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COVID-19 hits the world like a storm by arising pandemic situations for most of the countries around the world. The whole world is trying to overcome this pandemic situation. A better health care quality may help a country to tackle the pandemic. Making clusters of countries with similar types of health care quality provides an insight into the quality of health care in different countries. In the area of machine learning and data science, the K-means clustering algorithm is typically used to create clusters based on similarity. In this paper, we propose an efficient K-means clustering method that determines the initial centroids of the clusters efficiently. Based on this proposed method, we have determined health care quality clusters of countries utilizing the COVID-19 datasets. Experimental results show that our proposed method reduces the number of iterations and execution time to analyze COVID-19 while comparing with the traditional k-means clustering algorithm.

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ContextPCA: Predicting Context-Aware Smartphone Apps Usage Based On Machine Learning Techniques

Cited by 39 publications

References 16 publications

Context pre-modeling: an empirical analysis for classification based user-centric context-aware predictive modeling

Context pre-modeling: an empirical analysis for classification based user-centric context-aware predictive modeling

Mobile Data Science and Intelligent Apps: Concepts, AI-Based Modeling and Research Directions

An Efficient K-Means Clustering Algorithm for Analysing COVID-19

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