AppsPred: Predicting Context-Aware Smartphone Apps using Random Forest Learning

08/26/2019
by   Iqbal H. Sarker, et al.
0

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 ZeroR, Naive Bayes, Decision Tree, Support Vector Machines, Logistic Regression while predicting smartphone apps in various context-aware test cases.

READ FULL TEXT
research
08/25/2019

E-MIIM: An Ensemble Learning based Context-Aware Mobile Telephony Model for Intelligent Interruption Management

Nowadays, mobile telephony interruptions in our daily life activities ar...
research
07/04/2018

Context Data Categories and Privacy Model for Mobile Data Collection Apps

Context-aware applications stemming from diverse fields like mobile heal...
research
09/02/2019

CalBehav: A Machine Learning based Personalized Calendar Behavioral Model using Time-Series Smartphone Data

The electronic calendar is a valuable resource nowadays for managing our...
research
01/12/2018

Predicting Smartphone Battery Life based on Comprehensive and Real-time Usage Data

Smartphones and smartphone apps have undergone an explosive growth in th...
research
07/03/2023

Towards Real Smart Apps: Investigating Human-AI Interactions in Smartphone On-Device AI Apps

With the emergence of deep learning techniques, smartphone apps are now ...
research
08/15/2017

Continuous User Authentication via Unlabeled Phone Movement Patterns

In this paper, we propose a novel continuous authentication system for s...
research
08/16/2020

Prediction of Homicides in Urban Centers: A Machine Learning Approach

Relevant research has been standing out in the computing community aimin...

Please sign up or login with your details

Forgot password? Click here to reset