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Review

Analysis of Machine Learning Techniques for Information Classification in Mobile Applications

by
Sandra Pérez Arteaga
,
Ana Lucila Sandoval Orozco
and
Luis Javier García Villalba
*
Group of Analysis, Security and Systems (GASS), Department of Software Engineering and Artificial Intelligence (DISIA), Faculty of Computer Science and Engineering, Office 431, Universidad Complutense de Madrid (UCM), Calle Profesor José García Santesmases, 9, Ciudad Universitaria, 28040 Madrid, Spain
*
Author to whom correspondence should be addressed.
Appl. Sci. 2023, 13(9), 5438; https://doi.org/10.3390/app13095438
Submission received: 11 February 2023 / Revised: 13 April 2023 / Accepted: 23 April 2023 / Published: 27 April 2023

Abstract

Due to the daily use of mobile technologies, we live in constant connection with the world through the Internet. Technological innovations in smart devices have allowed us to carry out everyday activities such as communicating, working, studying or using them as a means of entertainment, which has led to smartphones displacing computers as the most important device connected to the Internet today, causing users to demand smarter applications or functionalities that allow them to meet their needs. Artificial intelligence has been a major innovation in information technology that is transforming the way users use smart devices. Using applications that make use of artificial intelligence has revolutionised our lives, from making predictions of possible words based on typing in a text box, to being able to unlock devices through pattern recognition. However, these technologies face problems such as overheating and battery drain due to high resource consumption, low computational capacity, memory limitations, etc. This paper reviews the most important artificial intelligence algorithms for mobile devices, emphasising the challenges and problems that can arise when implementing these technologies in low-resource devices.
Keywords: algorithms; architectures; artificial intelligence; challenges; classification; deep learning; federated learning; limited resources; mobile devices algorithms; architectures; artificial intelligence; challenges; classification; deep learning; federated learning; limited resources; mobile devices

Share and Cite

MDPI and ACS Style

Pérez Arteaga, S.; Sandoval Orozco, A.L.; García Villalba, L.J. Analysis of Machine Learning Techniques for Information Classification in Mobile Applications. Appl. Sci. 2023, 13, 5438. https://doi.org/10.3390/app13095438

AMA Style

Pérez Arteaga S, Sandoval Orozco AL, García Villalba LJ. Analysis of Machine Learning Techniques for Information Classification in Mobile Applications. Applied Sciences. 2023; 13(9):5438. https://doi.org/10.3390/app13095438

Chicago/Turabian Style

Pérez Arteaga, Sandra, Ana Lucila Sandoval Orozco, and Luis Javier García Villalba. 2023. "Analysis of Machine Learning Techniques for Information Classification in Mobile Applications" Applied Sciences 13, no. 9: 5438. https://doi.org/10.3390/app13095438

APA Style

Pérez Arteaga, S., Sandoval Orozco, A. L., & García Villalba, L. J. (2023). Analysis of Machine Learning Techniques for Information Classification in Mobile Applications. Applied Sciences, 13(9), 5438. https://doi.org/10.3390/app13095438

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