Smart City Gnosys

Smart city article details

Title	The Smart In Smart Cities: A Framework For Image Classification Using Deep Learning
ID_Doc	56855
Authors	Al-Qudah R.; Khamayseh Y.; Aldwairi M.; Khan S.
Year	2022
Published	Sensors, 22, 12
DOI	http://dx.doi.org/10.3390/s22124390
Abstract	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. © 2022 by the authors. Licensee MDPI, Basel, Switzerland.
Author Keywords	automation; deep learning; images; smart city; transfer learning; zoning

Similar Articles

Id	Similarity	Authors	Title	Published
14664	0.891	Lima S.; Teran L.	Cognitive Smart Cities And Deep Learning: A Classification Framework	2019 6th International Conference on eDemocracy and eGovernment, ICEDEG 2019 (2019)
50360	0.887	Nikolaev E.; Konyrkhanova A.; Zakharov V.	Smart City Management System Based On Multi-Purpose Deep Neural Network	Proceedings - 2022 International Russian Automation Conference, RusAutoCon 2022 (2022)
17887	0.885	Jalil N.A.; Leen M.W.E.; Salleh N.M.; Jafry N.H.A.	Deep Learning For Smart Cities: Innovations, Challenges, And Future Directions	2024 3rd International Conference on Sustainable Mobility Applications, Renewables and Technology, SMART 2024 (2024)
1183	0.882	Constantinou G.; Sankar Ramachandran G.; Alfarrarjeh A.; Kim S.H.; Krishnamachari B.; Shahabi C.	A Crowd-Based Image Learning Framework Using Edge Computing For Smart City Applications	Proceedings - 2019 IEEE 5th International Conference on Multimedia Big Data, BigMM 2019 (2019)
17818	0.882	Muhammad A.N.; Aseere A.M.; Chiroma H.; Shah H.; Gital A.Y.; Hashem I.A.T.	Deep Learning Application In Smart Cities: Recent Development, Taxonomy, Challenges And Research Prospects	Neural Computing and Applications, 33, 7 (2021)
17900	0.88	Sowmiya M.; Rekha B.B.; Kanthavel R.	Deep Learning In Smart Applications: Approaches And Challenges	EAI/Springer Innovations in Communication and Computing (2021)
55194	0.878	Nazari H.; Alkhader H.; Akhter A.F.M.S.; Hizal S.	The Contribution Of Deep Learning For Future Smart Cities	Advanced Sciences and Technologies for Security Applications (2023)
44714	0.874	Filus K.; Domanska J.	Recycling Of Generic Imagenet-Trained Models For Smart-City Applications	2023 IEEE 10th International Conference on Data Science and Advanced Analytics, DSAA 2023 - Proceedings (2023)
18081	0.874	Alajlan N.N.; Ibrahim D.M.	Deep Smart Cities: A Review Of Smart Cities Applications-Based An Inference Of Deep Learning Upon Iot Devices	Journal of Advanced Research in Applied Sciences and Engineering Technology, 47, 2 (2025)
38249	0.873	Nikolaev E.; Zakharov V.; Zakharova N.	Multi-Layered Smart City System Based On Multi-Purpose Deep Model	AIP Conference Proceedings, 2812, 1 (2023)