Cargando…

Porting Rulex Software to the Raspberry Pi for Machine Learning Applications on the Edge †

Edge Computing enables to perform measurement and cognitive decisions outside a central server by performing data storage, manipulation, and processing on the Internet of Things (IoT) node. Also, Artificial Intelligence (AI) and Machine Learning applications have become a rudimentary procedure in vi...

Descripción completa

Detalles Bibliográficos
Autores principales: Daher, Ali Walid, Rizik, Ali, Muselli, Marco, Chible, Hussein, Caviglia, Daniele D.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8512253/
https://www.ncbi.nlm.nih.gov/pubmed/34640846
http://dx.doi.org/10.3390/s21196526
_version_ 1784582946855845888
author Daher, Ali Walid
Rizik, Ali
Muselli, Marco
Chible, Hussein
Caviglia, Daniele D.
author_facet Daher, Ali Walid
Rizik, Ali
Muselli, Marco
Chible, Hussein
Caviglia, Daniele D.
author_sort Daher, Ali Walid
collection PubMed
description Edge Computing enables to perform measurement and cognitive decisions outside a central server by performing data storage, manipulation, and processing on the Internet of Things (IoT) node. Also, Artificial Intelligence (AI) and Machine Learning applications have become a rudimentary procedure in virtually every industrial or preliminary system. Consequently, the Raspberry Pi is adopted, which is a low-cost computing platform that is profitably applied in the field of IoT. As for the software part, among the plethora of Machine Learning (ML) paradigms reported in the literature, we identified Rulex, as a good ML platform, suitable to be implemented on the Raspberry Pi. In this paper, we present the porting of the Rulex ML platform on the board to perform ML forecasts in an IoT setup. Specifically, we explain the porting Rulex’s libraries on Windows 32 Bits, Ubuntu 64 Bits, and Raspbian 32 Bits. Therefore, with the aim of carrying out an in-depth verification of the application possibilities, we propose to perform forecasts on five unrelated datasets from five different applications, having varying sizes in terms of the number of records, skewness, and dimensionality. These include a small Urban Classification dataset, three larger datasets concerning Human Activity detection, a Biomedical dataset related to mental state, and a Vehicle Activity Recognition dataset. The overall accuracies for the forecasts performed are: 84.13%, 99.29% (for SVM), 95.47% (for SVM), and 95.27% (For KNN) respectively. Finally, an image-based gender classification dataset is employed to perform image classification on the Edge. Moreover, a novel image pre-processing Algorithm was developed that converts images into Time-series by relying on statistical contour-based detection techniques. Even though the dataset contains inconsistent and random images, in terms of subjects and settings, Rulex achieves an overall accuracy of 96.47% while competing with the literature which is dominated by forward-facing and mugshot images. Additionally, power consumption for the Raspberry Pi in a Client/Server setup was compared with an HP laptop, where the board takes more time, but consumes less energy for the same ML task.
format Online
Article
Text
id pubmed-8512253
institution National Center for Biotechnology Information
language English
publishDate 2021
publisher MDPI
record_format MEDLINE/PubMed
spelling pubmed-85122532021-10-14 Porting Rulex Software to the Raspberry Pi for Machine Learning Applications on the Edge † Daher, Ali Walid Rizik, Ali Muselli, Marco Chible, Hussein Caviglia, Daniele D. Sensors (Basel) Article Edge Computing enables to perform measurement and cognitive decisions outside a central server by performing data storage, manipulation, and processing on the Internet of Things (IoT) node. Also, Artificial Intelligence (AI) and Machine Learning applications have become a rudimentary procedure in virtually every industrial or preliminary system. Consequently, the Raspberry Pi is adopted, which is a low-cost computing platform that is profitably applied in the field of IoT. As for the software part, among the plethora of Machine Learning (ML) paradigms reported in the literature, we identified Rulex, as a good ML platform, suitable to be implemented on the Raspberry Pi. In this paper, we present the porting of the Rulex ML platform on the board to perform ML forecasts in an IoT setup. Specifically, we explain the porting Rulex’s libraries on Windows 32 Bits, Ubuntu 64 Bits, and Raspbian 32 Bits. Therefore, with the aim of carrying out an in-depth verification of the application possibilities, we propose to perform forecasts on five unrelated datasets from five different applications, having varying sizes in terms of the number of records, skewness, and dimensionality. These include a small Urban Classification dataset, three larger datasets concerning Human Activity detection, a Biomedical dataset related to mental state, and a Vehicle Activity Recognition dataset. The overall accuracies for the forecasts performed are: 84.13%, 99.29% (for SVM), 95.47% (for SVM), and 95.27% (For KNN) respectively. Finally, an image-based gender classification dataset is employed to perform image classification on the Edge. Moreover, a novel image pre-processing Algorithm was developed that converts images into Time-series by relying on statistical contour-based detection techniques. Even though the dataset contains inconsistent and random images, in terms of subjects and settings, Rulex achieves an overall accuracy of 96.47% while competing with the literature which is dominated by forward-facing and mugshot images. Additionally, power consumption for the Raspberry Pi in a Client/Server setup was compared with an HP laptop, where the board takes more time, but consumes less energy for the same ML task. MDPI 2021-09-29 /pmc/articles/PMC8512253/ /pubmed/34640846 http://dx.doi.org/10.3390/s21196526 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Daher, Ali Walid
Rizik, Ali
Muselli, Marco
Chible, Hussein
Caviglia, Daniele D.
Porting Rulex Software to the Raspberry Pi for Machine Learning Applications on the Edge †
title Porting Rulex Software to the Raspberry Pi for Machine Learning Applications on the Edge †
title_full Porting Rulex Software to the Raspberry Pi for Machine Learning Applications on the Edge †
title_fullStr Porting Rulex Software to the Raspberry Pi for Machine Learning Applications on the Edge †
title_full_unstemmed Porting Rulex Software to the Raspberry Pi for Machine Learning Applications on the Edge †
title_short Porting Rulex Software to the Raspberry Pi for Machine Learning Applications on the Edge †
title_sort porting rulex software to the raspberry pi for machine learning applications on the edge †
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8512253/
https://www.ncbi.nlm.nih.gov/pubmed/34640846
http://dx.doi.org/10.3390/s21196526
work_keys_str_mv AT daheraliwalid portingrulexsoftwaretotheraspberrypiformachinelearningapplicationsontheedge
AT rizikali portingrulexsoftwaretotheraspberrypiformachinelearningapplicationsontheedge
AT musellimarco portingrulexsoftwaretotheraspberrypiformachinelearningapplicationsontheedge
AT chiblehussein portingrulexsoftwaretotheraspberrypiformachinelearningapplicationsontheedge
AT cavigliadanieled portingrulexsoftwaretotheraspberrypiformachinelearningapplicationsontheedge