Local Media Image Propagation Algorithm and Its Governance in the Age of Artificial Intelligence

Area of Science:

• Artificial Intelligence
• Media Studies
• Communication Technology

Background:

• The internet and reduced technical barriers have led to a surge in image and video propagation.
• Images are now a primary medium for personal expression and public discourse in cyberspace.
• Analyzing AI's role in media image dissemination is crucial for understanding its impact.

Purpose of the Study:

• To analyze and propose governance for local media image propagation algorithms in the AI era.
• To identify ethical issues in media dissemination activities, specifically video works.
• To find fundamental solutions for ethical challenges in digital image and video sharing.

Main Methods:

• Analysis of intelligent algorithms for video recommendation, focusing on the BP neural network.
• Comparison of traditional BP neural networks with swarm optimization-based algorithms (FEBP).
• Integration of improved intelligent algorithms into image transmission systems.

Main Results:

• The BP neural network algorithm based on swarm optimization (FEBP) demonstrated a 15.8% higher video recommendation accuracy compared to traditional BP neural networks.
• The study identified specific advantages and disadvantages of BP neural network-based algorithms.
• The proposed integration aims to enhance image transmission and recommendation effectiveness.

Conclusions:

• AI-driven algorithms significantly impact media image propagation and recommendation.
• Swarm optimization enhances BP neural networks for more accurate video recommendations.
• Implementing advanced AI algorithms can improve the efficiency and effectiveness of digital media content delivery.