• Mayuresh Kulkarni
• Dr. Satvik Khara
• Dr. Jay Dave

In recent years, the exponential growth of online video content has driven a significant need for efficient video matching techniques to address issues like copyright infringement, inappropriate content distribution, and digital piracy. This research paper offers an in-depth examination of the application of machine learning algorithms for video matching on digital platforms. We provide a comprehensive review of existing methodologies, covering both traditional approaches and cutting-edge deep learning techniques. Moreover, the paper explores various challenges inherent in video matching. Through extensive experimentation and analysis, we assess the performance of different machine learning models, focusing on their matching accuracy, resilience to content variations, and real-time processing capabilities. Additionally, the paper underscores the effectiveness of machine learning in meeting the complex demands of video matching on online platforms.

LEVERAGING MACHINE LEARNING FOR VIDEO MATCHING ON ONLINE PLATFORMS: A COMPREHENSIVE REVIEW AND ANALYSIS

"LEVERAGING MACHINE LEARNING FOR VIDEO MATCHING ON ONLINE PLATFORMS: A COMPREHENSIVE REVIEW AND ANALYSIS", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.10, Issue 11, page no.f665-f667, November-2023, Available :http://www.jetir.org/papers/JETIR2311579.pdf

"LEVERAGING MACHINE LEARNING FOR VIDEO MATCHING ON ONLINE PLATFORMS: A COMPREHENSIVE REVIEW AND ANALYSIS", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.10, Issue 11, page no. ppf665-f667, November-2023, Available at : http://www.jetir.org/papers/JETIR2311579.pdf