Applicability of Crowd Sourcing to Determine the Best Transportation Method by Analysing User Mobility

The use of mobile devices is rising daily in this technological era. A continuous and increasing number of mobile applications are constantly offered on mobile marketplaces to fulfil the needs of smartphone users. Many Android applications do not address the security aspects appropriately. This is often due to a lack of automated mechanisms to identify, test, and fix source code vulnerabilities at the early stages of design and development. Therefore, the need to fix such issues at the initial stages rather than providing updates and patches to the published applications is widely recognized. Researchers have proposed several methods to improve the security of applications by detecting source code vulnerabilities and malicious codes. This Systematic Literature Review (SLR) focuses on Android application analysis and source code vulnerability detection methods and tools by critically evaluating 118 carefully selected technical studies published between 2016 and 2022. It highlights the advantages, disadvantages, applicability of the proposed techniques and potential improvements of those studies. Both Machine Learning (ML) based methods and conventional methods related to vulnerability detection are discussed while focusing more on ML-based methods since many recent studies conducted experiments with ML. Therefore, this paper aims to enable researchers to acquire in-depth knowledge in secure mobile application development while minimizing the vulnerabilities by applying ML methods. Furthermore, researchers can use the discussions and findings of this SLR to identify potential future research and development directions.

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Understanding Places Exploration and Visitation via Human Mobility Mining

Shad,

Usman,

Kumar

et al. 2024

International Journal of Intelligent Information Technologies

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Spatial-temporal data is widely available because of advancement in location acquisition technologies (GPS, GSM, Wifi, etc.) over past decades. This spatial-temporal data can easily be used to leverage user's trajectories, history, and habits to develop location-based services (LBS). But to leverage user's history based on location is bit challenging, in this paper we used the user's GPS log data to discover trajectories and analyze different life patterns over it. We proposed that a human spends 80% of his life on known places or safe places. We converted GPS patterns into visitation locations, converted them into daily and periodic trends and analyzed them over time to prove our assumption. We used the GPS data collected by Microsoft Research Asia, this data has 182 users with 73 users with their mode of movement information i.e., Taxi, Walk, Train etc.

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Applicability of Crowd Sourcing to Determine the Best Transportation Method by Analysing User Mobility

Cited by 3 publications

References 8 publications

LYZGen: A mechanism to generate leads from Generation Y and Z by analysing web and social media data

LYZGen: A mechanism to generate leads from Generation Y and Z by analysing web and social media data

Android Source Code Vulnerability Detection: A Systematic Literature Review

Understanding Places Exploration and Visitation via Human Mobility Mining

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