Adaptation of Applications to Compare Development Frameworks in Deep Learning for Decentralized Android Applications

• Published in: International journal of interactive multimedia and artificial intelligence Vol. 8; no. 2; pp. 224 - 231
• Main Authors: Sainz-De-Abajo, Beatriz, Laso, Sergio, Garcia-Alonso, Jose, Berrocal, Javier
• Format: Journal Article
• Language: English
• Published: IMAI Software 01-06-2023; Universidad Internacional de La Rioja (UNIR)
• Subjects: android; Android Deep Learning Framework Images TensorFlow; Batteries; Comparative analysis; Computational linguistics; deep learning; framework; Information storage and retrieval; Language processing; Machine learning; Mobile applications; Natural language interfaces; Technology application; tensorflow; Spain
• ISSN: 1989-1660; 1989-1660
• DOI: 10.9781/ijimai.2023.04.006

Not all frameworks used in machine learning and deep learning integrate with Android, which requires some prerequisites. The primary objective of this paper is to present the results of the analysis and a comparison of deep learning development frameworks, which can be adapted into fully decentralized Android apps from a cloud server. As a work methodology, we develop and/or modify the test applications that these frameworks offer us a priori in such a way that it allows an equitable comparison of the analysed characteristics of interest. These parameters are related to attributes that a user would consider, such as (1) percentage of success; (2) battery consumption; and (3) power consumption of the processor. After analysing numerical results, the proposed framework that best behaves in relation to the analysed characteristics for the development of an Android application is TensorFlow, which obtained the best score against Caffe2 and Snapdragon NPE in the percentage of correct answers, battery consumption, and device CPU power consumption. Data consumption was not considered because we focus on decentralized cloud storage applications in this study. KEYWORDS Android Applications, Decentralized, Deep Learning, Framework, Images, TensorFlow.