A Systematic Review on Applying Machine Learning and Deep Learning on SBCs-Reference-Cited by-同舟云学术

A Systematic Review on Applying Machine Learning and Deep Learning on SBCs

Published:2024-05-15 Issue: Volume:21 Page:272-281
ISSN:2224-3402
Container-title:WSEAS TRANSACTIONS ON INFORMATION SCIENCE AND APPLICATIONS
language:en
Short-container-title:

Author:

Aldahoud Ali¹,Fezari Mohamed²,Al-Dahoud Ahmad³,Aqel Darah¹

Affiliation:

1. Faculty of Science and IT, Al-Zaytoonah University of Jordan, Amman, JORDAN

2. Electronics and Computer Architecture, University of Badji Mokhtar, Annaba, ALGERIA

3. Faculty of Architecture and Design, Al-Zaytoonah University of Jordan, Amman, JORDAN

Abstract

This research introduces a comprehensive study of the most robust Single-Board Computers (SBCs) implemented recently, where most of them are built on the system-on-chip architecture. This study also presents the main characteristics of each of these SBCs, as well as their prices and applications. Additionally, the study reviews some machine learning (ML) and deep learning (DL) techniques, exploring their implementation on SBCs. Finally, it displays some software tools on how to implement DL and ML projects on SBCs.

Publisher

World Scientific and Engineering Academy and Society (WSEAS)

Reference25 articles.

1. Johnston, S.J., Basford, P.J., Perkins, C.S., Herry, H., Tso, F.P., Pezaros, D., Mullins, R.D., Yoneki, E., Cox, S.J. and Singer, J. Commodity single board computer clusters and their applications. Future Generation Computer Systems, Vol. 89, pp.201-212, 2018.

2. Russell, Stuart J.; Norvig, Peter. Artificial Intelligence: A Modern Approach (4th ed.). Hoboken: Pearson, 2021.

3. Aqel, D. and Hawashin, B. Arabic relative clauses parsing based on inductive logic programming. Recent Patents on Computer Science, Vol. 11, No.2, 2018, pp.121-133.

4. Hawashin, B. and Abusukhon, A., An Efficient Course Recommender Using DeepEnriched Hidden Student Aptitudes. ICIC Express Letters, Part B: Applications, pp.1331-1338, 2022.

5. Aqel, D., Al-Zubi, S., Mughaid, A. and Jararweh, Y., Extreme learning machine for plant diseases classification: a sustainable approach for smart agriculture. Cluster Computing, pp.1-14, 2022.