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The Smart in Smart Cities: A Framework for Image Classification Using Deep Learning

The need for a smart city is more pressing today due to the recent pandemic, lockouts, climate changes, population growth, and limitations on availability/access to natural resources. However, these challenges can be better faced with the utilization of new technologies. The zoning design of smart c...

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Autores principales: Al-qudah, Rabiah, Khamayseh, Yaser, Aldwairi, Monther, Khan, Sarfraz
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9229030/
https://www.ncbi.nlm.nih.gov/pubmed/35746171
http://dx.doi.org/10.3390/s22124390
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author Al-qudah, Rabiah
Khamayseh, Yaser
Aldwairi, Monther
Khan, Sarfraz
author_facet Al-qudah, Rabiah
Khamayseh, Yaser
Aldwairi, Monther
Khan, Sarfraz
author_sort Al-qudah, Rabiah
collection PubMed
description The need for a smart city is more pressing today due to the recent pandemic, lockouts, climate changes, population growth, and limitations on availability/access to natural resources. However, these challenges can be better faced with the utilization of new technologies. The zoning design of smart cities can mitigate these challenges. It identifies the main components of a new smart city and then proposes a general framework for designing a smart city that tackles these elements. Then, we propose a technology-driven model to support this framework. A mapping between the proposed general framework and the proposed technology model is then introduced. To highlight the importance and usefulness of the proposed framework, we designed and implemented a smart image handling system targeted at non-technical personnel. The high cost, security, and inconvenience issues may limit the cities’ abilities to adopt such solutions. Therefore, this work also proposes to design and implement a generalized image processing model using deep learning. The proposed model accepts images from users, then performs self-tuning operations to select the best deep network, and finally produces the required insights without any human intervention. This helps in automating the decision-making process without the need for a specialized data scientist.
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spelling pubmed-92290302022-06-25 The Smart in Smart Cities: A Framework for Image Classification Using Deep Learning Al-qudah, Rabiah Khamayseh, Yaser Aldwairi, Monther Khan, Sarfraz Sensors (Basel) Article The need for a smart city is more pressing today due to the recent pandemic, lockouts, climate changes, population growth, and limitations on availability/access to natural resources. However, these challenges can be better faced with the utilization of new technologies. The zoning design of smart cities can mitigate these challenges. It identifies the main components of a new smart city and then proposes a general framework for designing a smart city that tackles these elements. Then, we propose a technology-driven model to support this framework. A mapping between the proposed general framework and the proposed technology model is then introduced. To highlight the importance and usefulness of the proposed framework, we designed and implemented a smart image handling system targeted at non-technical personnel. The high cost, security, and inconvenience issues may limit the cities’ abilities to adopt such solutions. Therefore, this work also proposes to design and implement a generalized image processing model using deep learning. The proposed model accepts images from users, then performs self-tuning operations to select the best deep network, and finally produces the required insights without any human intervention. This helps in automating the decision-making process without the need for a specialized data scientist. MDPI 2022-06-10 /pmc/articles/PMC9229030/ /pubmed/35746171 http://dx.doi.org/10.3390/s22124390 Text en © 2022 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
Al-qudah, Rabiah
Khamayseh, Yaser
Aldwairi, Monther
Khan, Sarfraz
The Smart in Smart Cities: A Framework for Image Classification Using Deep Learning
title The Smart in Smart Cities: A Framework for Image Classification Using Deep Learning
title_full The Smart in Smart Cities: A Framework for Image Classification Using Deep Learning
title_fullStr The Smart in Smart Cities: A Framework for Image Classification Using Deep Learning
title_full_unstemmed The Smart in Smart Cities: A Framework for Image Classification Using Deep Learning
title_short The Smart in Smart Cities: A Framework for Image Classification Using Deep Learning
title_sort smart in smart cities: a framework for image classification using deep learning
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9229030/
https://www.ncbi.nlm.nih.gov/pubmed/35746171
http://dx.doi.org/10.3390/s22124390
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