Neurocomputing for internet of things: object recognition and detection strategy

Qureshi, KN, Kaiwartya, O ORCID: https://orcid.org/0000-0001-9669-8244, Jeon, G and Piccialli, F, 2022. Neurocomputing for internet of things: object recognition and detection strategy. Neurocomputing, 485, pp. 263-273. ISSN 0925-2312

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Abstract

Modern and new integrated technologies have changed the traditional systems by using more advanced machine learning, artificial intelligence methods, new generation standards, and smart and intelligent devices. The new integrated networks like the Internet of Things (IoT) and 5G standards offer various benefits and services. However, these networks have suffered from multiple object detection, localization, and classification issues. Conventional Neural Networks (CNN) and their variants have been adopted for object detection, classification, and localization in IoT networks to create autonomous devices to make decisions and perform tasks without human intervention and helpful to learn in-depth features. Motivated by these facts, this paper investigates existing object detection and recognition techniques by using CNN models used in IoT networks. This paper presents a Conventional Neural Networks for 5G-Enabled Internet of Things Network (CNN-5GIoT) model for moving and static objects in IoT networks after a detailed comparison. The proposed model is evaluated with existing models to check the accuracy of real-time tracking. The proposed model is more efficient for real-time object detection and recognition than conventional methods.

Item Type:	Journal article
Publication Title:	Neurocomputing
Creators:	Qureshi, K.N., Kaiwartya, O., Jeon, G. and Piccialli, F.
Publisher:	Elsevier
Date:	7 May 2022
Volume:	485
ISSN:	0925-2312
Identifiers:	Number Type 10.1016/j.neucom.2021.04.140 DOI S0925231221016398 Publisher Item Identifier 1630700 Other
Divisions:	Schools > School of Science and Technology
Record created by:	Jonathan Gallacher
Date Added:	06 Jan 2023 09:31
Last Modified:	06 Jan 2023 09:32
URI:	https://irep.ntu.ac.uk/id/eprint/47761

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