Adaptation of applications to compare development frameworks in deep learning for decentralized android applications

  1. Beatriz Sainz-de-Abajo 1
  2. Sergio Laso
  3. Jose Garcia-Alonso 2
  4. Javier Berrocal 2
  1. 1 Universidad de Valladolid
    info

    Universidad de Valladolid

    Valladolid, España

    ROR https://ror.org/01fvbaw18

  2. 2 Universidad de Extremadura
    info

    Universidad de Extremadura

    Badajoz, España

    ROR https://ror.org/0174shg90

Revista:
IJIMAI

ISSN: 1989-1660

Año de publicación: 2023

Volumen: 8

Número: 2

Páginas: 224-231

Tipo: Artículo

DOI: 10.9781/IJIMAI.2023.04.006 DIALNET GOOGLE SCHOLAR lock_openDialnet editor

Otras publicaciones en: IJIMAI

Resumen

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.

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Fuente de los datos: Dialnet