The Short Video Popularity Prediction Using Internet of Things and Deep
DOI:
https://doi.org/10.46647/rdems0205002Keywords:
Deep Learning, Firearm Detection, Convolutional Neural Networks, Real-time Surveillance, Object Detection, Computer Vision, Public Safety, Smart Surveillance System, Threat Detection, Video AnalyticsAbstract
The rapid growth of short video platforms has led to an increasing demand for intelligent systems that can predict content popularity. This project, “Short Video Popularity Prediction Using Internet of Things and Deep Learning,” presents an integrated framework that leverages IoT-generated contextual data and advanced deep learning techniques to forecast the popularity of short videos.The proposed system collects heterogeneous data from multiple sources, including video content, user interactions, and IoT devices such as smartphones and wearable sensors. This data is processed through a feature extraction module to derive meaningful video features (e.g., visual, audio, and metadata) and IoT-based contextual features (e.g., location, time, and user activity). A deep learning model, such as a Recurrent Neural Network (RNN) or Convolutional Neural Network (CNN), is then employed to learn complex patterns and relationships within the data.