Effectiveness analysis of machine learning classification models for predicting personalized context-aware smartphone usage

Sarker, Iqbal H.; Kayes, A. S. M.; Watters, Paul A.

doi:10.1186/s40537-019-0219-y

Cited by 215 publications

(140 citation statements)

References 69 publications

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“…In these situations, AI can analyse such different activities which can reveal the patterns of mobile users accordingly [74]. For instance, ML is able to model and predict the personalized diverse activities of a user through learning from his phone usage records [75]. Furthermore, DL would be a promising AI technique that relies on deep data learning and highperformance model prediction, aiming to estimate accurately the mobile application usage, i.e.…”

Section: A Estimation Of Coronavirus Outbreak Sizementioning

confidence: 99%

Blockchain and AI-based Solutions to Combat Coronavirus (COVID-19)-like Epidemics: A Survey

Nguyen¹,

Ding²,

Pathirana³

et al. 2020

Preprint

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The beginning of 2020 has seen the emergence of coronavirus outbreak caused by a novel virus called SARS-CoV-2. The sudden explosion and uncontrolled worldwide spread of COVID-19 show the limitations of existing healthcare systems to timely handle public health emergencies. In such contexts, innovative technologies such as blockchain and Artificial Intelligence (AI) have emerged as promising solutions for fighting coronavirus epidemic. On the one hand, blockchain can combat pandemics by enabling early detection of outbreaks, protecting user privacy, and ensuring reliable medical supply chain during the outbreak tracking. On the other hand, AI provides intelligent solutions for identifying symptoms caused by coronavirus for treatments and supporting drug manufacturing. Motivated by these, in this paper we present an extensive survey on the use of blockchain and AI for combating coronavirus (COVID-19) epidemics based on the rapidly emerging literature. First, we introduce a new conceptual architecture which integrates blockchain and AI specific for COVID-19 fighting. Particularly, we highlight the key solutions that blockchain and AI can provide to combat the COVID-19 outbreak. Then, we survey the latest research efforts on the use of blockchain and AI for COVID-19 fighting in a wide range of applications. The newly emerging projects and use cases enabled by these technologies to deal with coronavirus pandemic are also presented. Finally, we point out challenges and future directions that motivate more research efforts to deal with future coronavirus-like epidemics.

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Section: A Estimation Of Coronavirus Outbreak Sizementioning

confidence: 99%

Blockchain and AI-based Solutions to Combat Coronavirus (COVID-19)-like Epidemics: A Survey

Nguyen¹,

Ding²,

Pathirana³

et al. 2020

Preprint

View full text Add to dashboard Cite

show abstract

“…Effectively modelling and predicting smartphone usage behaviour various machine learning techniques can be used. For instance, to build the prediction model in the area of mobile environment, ZeroR as base classifier, probability based naive Bayes classifier, support vector machines, instance based k-nearest neighbours, logistic regression, artificial neural network or deep learning, rule-based learning like decision trees, ensemble learning like random forest have been used [6,12]. These machine learning classifiers are frequently used in context-aware mobile analytics [12].…”

Section: Background and Related Workmentioning

confidence: 99%

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

2020

Self Cite

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This paper mainly formulates the problem of predicting context-aware smartphone apps usage based on machine learning techniques. In the real world, people use various kinds of smartphone apps differently in different contexts that include both the user-centric context and device-centric context. In the area of artificial intelligence and machine learning, decision tree model is one of the most popular approaches for predicting context-aware smartphone usage. However, real-life smartphone apps usage data may contain higher dimensions of contexts, which may cause several issues such as increases model complexity, may arise over-fitting problem, and consequently decreases the prediction accuracy of the context-aware model. In order to address these issues, in this paper, we present an effective principal component analysis (PCA) based context-aware smartphone apps prediction model, “ContextPCA” using decision tree machine learning classification technique. PCA is an unsupervised machine learning 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.

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“…ZeroR: In the area of machine learning, this is the simplest approach for predictive analytics among the classification techniques [31]. According to [9], it can be used for deciding a standard execution as a benchmark for other classification techniques. For comparison purpose, we denote ZeroR leaning based model as BM1.…”

Section: Effectiveness Comparisonmentioning

confidence: 99%

“…In the area of machine learning and predictive analytics, ZeroR, Naive Bayes, Decision Tree, Support Vector Machines, Logistic Regression, and Random Forest are the most popular classification algorithms that can be used to build data-driven contextaware models [9] [10]. Among these techniques, tree based context-aware model is more effective to intelligently predict mobile user activity in different contexts [9]. In particular, a number of researchers [11] [12] [13] [14] have used decision tree classifier to model mobile phone users' behavior.…”

mentioning

confidence: 99%

AppsPred: Predicting context-aware smartphone apps using random forest learning

Sarker

2019

Internet of Things

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Due to the popularity of context-awareness in the Internet of Things (IoT) and the recent advanced features in the most popular IoT device, i.e., smartphone, modeling and predicting personalized usage behavior based on relevant contexts can be highly useful in assisting them to carry out daily routines and activities.Usage patterns of different categories smartphone apps such as social networking, communication, entertainment, or daily life services related apps usually vary greatly between individuals. People use these apps differently in different contexts, such as temporal context, spatial context, individual mood and preference, work status, Internet connectivity like Wifi status, or device related status like phone profile, battery level etc. Thus, we consider individuals' apps usage as a multi-class context-aware problem for personalized modeling and prediction. Random Forest learning is one of the most popular machine learning techniques to build a multi-class prediction model. Therefore, in this paper, we present an effective context-aware smartphone apps prediction model, and name it "AppsPred" using random forest machine learning technique that takes into account optimal number of trees based on such multi-dimensional contexts to build the resultant forest. The effectiveness of this model is examined by conducting experiments on smartphone apps usage datasets collected from individual users. The experimental results show that our AppsPred significantly outperforms other popular machine learning classification approaches like Ze-roR, Naive Bayes, Decision Tree, Support Vector Machines, Logistic Regression while predicting smartphone apps in various context-aware test cases.

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Effectiveness analysis of machine learning classification models for predicting personalized context-aware smartphone usage

Cited by 215 publications

References 69 publications

Blockchain and AI-based Solutions to Combat Coronavirus (COVID-19)-like Epidemics: A Survey

Blockchain and AI-based Solutions to Combat Coronavirus (COVID-19)-like Epidemics: A Survey

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

AppsPred: Predicting context-aware smartphone apps using random forest learning

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