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Journal of Information and Optimization Sciences cover
Hybrid ·Peer-reviewed·ISSN (Online): 2169-0103·ISSN (Print): 0252-2667

WoS  JIF 2026 : 0.4 (Q4)

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Monthly Journal: Publishes theoretical and applied research on topics in information and optimization sciences.

Issues up to 2022 co-published with and available at:Taylor & Francis
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Open Access Research Article

An optimization approach for real-time object detection in IoT devices through edge computing and deep learning

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pp. 1465–1475Vol. 45Issue 5July 2024DOI: 10.47974/JIOS-1768XML
Received:
10 Apr 2024
Published Online:
12 Aug 2024
Article type:
Research Article
Language:
EN
Article no.:
JIOS-1768
Pages:
1465–1475

Abstract

Real-time protest acknowledgment in IoT gadgets is vital for numerous employments, but it can be difficult to do since they do not have a part of computing control. To bargain with these issues, this ponder recommends a way to move forward things that employments edge computing and profound learning. By utilizing edge gadgets, handling is moved closer to the sources of information, which brings down delay and moves forward security. Convolutional neural networks (CNNs) are utilized to discover objects, but they got to be optimized some time recently they can be utilized on gadgets with restricted assets. Our strategy centers on diminishing the measure of the show, speeding up thinking, and utilizing less vitality. Edge computing and profound learning are utilized together to grant IoT gadgets the capacity to recognize objects in genuine time. This makes it conceivable for employments like observing, self-driving cars, and mechanical robotization. The recommended strategy precisely and rapidly finds objects, and comes about of tests appear that it works whereas diminishing the sum of work that ought to be done on central computers. By making adaptable and quick protest acknowledgment frameworks conceivable, this work makes a difference to create IoT situations more intelligent and more proficient.

Keywords

Subject Classifications

Primary 00A05Secondary 97P40

References

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