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Related Experiment Video

Updated: May 29, 2025

Construction of an Improved Multi-Tetrode Hyperdrive for Large-Scale Neural Recording in Behaving Rats
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AI-driven video summarization for optimizing content retrieval and management through deep learning techniques.

Deepali Vora1, Payal Kadam2,3, Dadaso D Mohite4

  • 1Symbiosis Institute of Technology, Pune Campus, Symbiosis International (Deemed University), Pune, India.

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|February 3, 2025
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Summary
This summary is machine-generated.

This study introduces an AI-driven system for organizing video content in large archives, improving retrieval efficiency and promoting sustainable digital practices for enhanced environmental, social, and governance outcomes.

Keywords:
Content retrievalConvolutional neural networksLSTMResNet50Video summarization

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Area of Science:

  • Artificial Intelligence
  • Computer Science
  • Information Science

Background:

  • Growing volume and complexity of digital video content challenge traditional archive management.
  • Existing methods for video organization and retrieval often require significant human input or use simplistic algorithms.
  • Need for scalable and efficient solutions to manage large, heterogeneous media archives.

Purpose of the Study:

  • To develop a novel artificial intelligence (AI) framework for enhanced organization and retrieval of video content.
  • To address the limitations of current methods in handling large-scale video archives.
  • To explore how AI can support sustainable digital practices in media management.

Main Methods:

  • Utilized convolutional neural networks (CNNs) and long short-term memory (LSTM) networks for extracting frame-level and temporal video features.
  • Integrated Residual Networks 50 (ResNet50) for improved content representation.
  • Employed two-frame video flow to enhance system performance.

Main Results:

  • Achieved high performance metrics: 79.2% precision, 86.5% recall, and 83% F-score.
  • Validated the framework on diverse datasets including YouTube, EPFL, and TVSum.
  • Demonstrated the system's capability to minimize data duplication and optimize resource usage.

Conclusions:

  • The proposed AI framework offers a scalable and effective solution for managing large media collections.
  • The system contributes to promoting sustainable digital practices through optimized resource utilization.
  • AI advancements can significantly enhance organizational environmental, social, and governance (ESG) outcomes via improved content management.