Mobile Data Science and Intelligent Apps: Concepts, AI-Based Modeling and Research Directions
Open accessAbstract
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-to-day 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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Behavioral analytics is a recent trend that typically reveals new insights into e-commerce sites, online gaming, mobile and smartphone applications, IoT user behavior, and many more.
“Behavioral analytics is a recent trend that typically reveals new insights into e-commerce sites, online gaming, mobile and smartphone applications, IoT user behavior, and many more [112].”
From Data Science and Analytics: An Overview from Data-Driven Smart Computing, Decision-Making and Applications Perspective (2021) · cites this paper as [112]
Section 6.5 (Mobile business; page unavailable) says data from online traffic and mobile devices can reveal customer behavior and improve user experience. Sections 3.4 and 4.1 discuss mobile-game recommendations and IoT data, but the copy does not establish behavioral analytics as a recent trend across online gaming, e-commerce sites, and IoT user behavior specifically. (LLM, conf=0.82)
Experimental — accuracy not yet measuredThe current electronic world is a wealth of various kinds of data, such as business data, financial data, healthcare data, multimedia data, internet of things (IoT) data, cybersecurity data, social media data, etc.
“Thus the current electronic world is a wealth of various kinds of data, such as business data, financial data, healthcare data, multimedia data, internet of things (IoT) data, cybersecurity data, social media data, etc [112].”
From Data Science and Analytics: An Overview from Data-Driven Smart Computing, Decision-Making and Applications Perspective (2021) · cites this paper as [112]
Section 4.1 (Understanding Mobile data; page unavailable) describes data from diverse sources, explicitly including IoT, business, health, and mobile-security data. The paper also discusses social-media use, but the passages located do not clearly identify financial or multimedia data as data-source categories, so the full illustrative list is not supported. (LLM, conf=0.79)
Experimental — accuracy not yet measuredMachine learning is one of the major techniques used in advanced analytics and can automate analytical model building.
“'Machine learning', a branch of artificial intelligence (AI), is one of the major techniques used in advanced analytics which can automate analytical model building [112].”
From Data Science and Analytics: An Overview from Data-Driven Smart Computing, Decision-Making and Applications Perspective (2021) · cites this paper as [112]
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References 134 · Cited by 207
Research works printed but not linked: 6
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- Authors
- Sarker, I. H., Hoque, M. M., Uddin, M. K., & Alsanoosy, T.
- Affiliations
- Chittagong University of Engineering & Technology (BD)
- Swinburne University of Technology (AU)
- RMIT University (AU)
- Corresponding author
- Sarker, Iqbal H.
- Venue
- Mobile Networks and Applications
- Published
- 2020-09-14
- Volume
- 26
- Issue
- 1
- Pages
- 285-303
- ISSN
- 1383-469X (ISSN-L)
- 1572-8153
- PMC
- PMC7489576
- OpenAlex
- W3086286002
- Cited by
- 207
- References
- 134
- Citations by year
- 2026: 25 · 2025: 41 · 2024: 42 · 2023: 27 · 2022: 43 · 2021: 28 · 2020: 1
- FWCI
- 9.73
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Mobile Networks and Applications (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- RMIT Research Repository (RMIT University Library) (submittedVersion)
- Figshare (submittedVersion, cc-by)
- Swinburne figshare (Swinburne University of Technology) (submittedVersion)
- Funders
- Swinburne University of Technology
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Artificial Intelligence Applications
- Physical Sciences › Computer Science › Information Systems › Mobile and Web Applications
- Physical Sciences › Computer Science › Information Systems › Internet of Things and AI
- Keywords
- personalized recommendation, medical applications, COVID-19 pandemic management, machine learning, deep learning, natural language processing, knowledge representation, expert systems, mobile application development
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