TY - JOUR
T1 - Neurocomputing for internet of things
T2 - Object recognition and detection strategy
AU - Qureshi, Kashif Naseer
AU - Kaiwartya, Omprakash
AU - Jeon, Gwanggil
AU - Piccialli, Francesco
N1 - Publisher Copyright:
© 2021 Elsevier B.V.
PY - 2022/5/7
Y1 - 2022/5/7
N2 - 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.
AB - 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.
KW - Classification
KW - Convolutional neural network
KW - Deep learning
KW - Image processing
KW - Localization
KW - Neural network
KW - Object detection
UR - http://www.scopus.com/inward/record.url?scp=85122356642&partnerID=8YFLogxK
U2 - 10.1016/j.neucom.2021.04.140
DO - 10.1016/j.neucom.2021.04.140
M3 - Article
AN - SCOPUS:85122356642
SN - 0925-2312
VL - 485
SP - 263
EP - 273
JO - Neurocomputing
JF - Neurocomputing
ER -