TY - JOUR
KW - Android
KW - Deep Learning
KW - Framework
KW - Images
KW - TensorFlow
AU - Beatriz Sainz-de-Abajo
AU - Sergio Laso
AU - Jose Garcia-Alonso
AU - Javier Berrocal
AB - 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. 
IS - Regular Issue
M1 - 2
N2 - 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. 
PY - 2023
SE - 224
SP - 224
EP - 231
T2 - International Journal of Interactive Multimedia and Artificial Intelligence
TI - Adaptation of Applications to Compare Development Frameworks in Deep Learning for Decentralized Android Applications
UR - https://www.ijimai.org/journal/sites/default/files/2023-05/ijimai8_2_21.pdf
VL - 8
SN - 1989-1660
ER -