DEEP LEARNING BASED VIDEO STREAM ANALYTICS WITH CLOUD UPDATES

Registration ID: IJNRD_226316

Published ID: IJNRD2408079

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Murali C, Mr. N. Praveen, M.E.

MPPT, SEPIC converter, PID controller, Photo Voltaic System

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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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

Volume 9 Issue 8, August-2024

Pages : a873-a875

Paper Reg. ID: IJNRD_226316

Published Paper Id: IJNRD2408079

Research Area: Engineering

Author Type: Indian Author

Country: Coimbatore, Tamil Nadu, India

Published Paper PDF: https://ijnrd.org/papers/IJNRD2408079.pdf

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Journal Name: INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT(IJNRD)

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Call For Paper - Volume 10 | Issue 12 | December 2025

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