Paper Title
DEEP LEARNING BASED VIDEO STREAM ANALYTICS WITH CLOUD UPDATES
Article Identifiers
Authors
Murali C , Mr. N. Praveen, M.E.
Keywords
MPPT, SEPIC converter, PID controller, Photo Voltaic System
Abstract
The aim of this work is to design and investigates the problem of video stream analytics by proposing recommended solutions. In today’s world, The Internet of Things (IoT), which connects gadgets like sensors and video cameras, is becoming more and more popular as people use the internet recurrently. As a result, massive volumes of streaming data are being generated quickly. A quick and effective analysis of this data is necessary for applications that need low latency response, like augmented reality, video surveillance, and autonomous cars. Current methods store and process this data using machine learning-based analytics by utilizing cloud infrastructure. Restrictions on network capacity and latency between the data source and cloud make it difficult for this centralized strategy to offer real-time analysis of large-scale streaming data. A system for large-scale, high-performance data analytics, called Real Edge Stream (RES), is the work that is being suggested.
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How To Cite (APA)
Murali C & Mr. N. Praveen, M.E. (August-2024). DEEP LEARNING BASED VIDEO STREAM ANALYTICS WITH CLOUD UPDATES. INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 9(8), a873-a875. https://ijnrd.org/papers/IJNRD2408079.pdf
Issue
Volume 9 Issue 8, August-2024
Pages : a873-a875
Other Publication Details
Paper Reg. ID: IJNRD_226316
Published Paper Id: IJNRD2408079
Downloads: 000121979
Research Area: Engineering
Country: Coimbatore, Tamil Nadu, India
Published Paper PDF: https://ijnrd.org/papers/IJNRD2408079.pdf
Published Paper URL: https://ijnrd.org/viewpaperforall?paper=IJNRD2408079
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This work is licensed under a Creative Commons Attribution 4.0 International License and The Open Definition
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