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HybridBranchNetV2: Towards reliable artificial intelligence in image classification using reinforcement learning.

Ebrahim Parcham1, Mansoor Fateh1, Vahid Abolghasemi2

  • 1Faculty of Computer Engineering, Shahrood University of Technology, Shahrood, Iran.

Plos One
|February 10, 2025
PubMed
Summary
This summary is machine-generated.

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HybridBranchNetV2 enhances artificial intelligence (AI) adaptability in dynamic environments. This novel hybrid architecture integrates reinforcement learning and graph-based methods for improved object recognition and classification accuracy.

Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Machine Learning

Background:

  • Current artificial intelligence (AI) algorithms exhibit limitations in adapting to dynamic real-world scenarios.
  • Challenges include complex classification tasks and object relationship extraction due to non-adaptive behaviors.

Purpose of the Study:

  • Introduce HybridBranchNetV2, an optimized hybrid architecture to enhance AI adaptability.
  • Address limitations in current AI models for dynamic environments.

Main Methods:

  • Integrate reinforcement learning for adaptive feature extraction.
  • Employ graph-based techniques for analyzing object relationships in complex environments.
  • Dynamically adjust feature extraction based on environmental feedback.

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Main Results:

  • Achieved an average accuracy of 91.75% across four challenging datasets.
  • Demonstrated significant improvements: 14% on Visual Genome and ImageNet 1K, 6% on CIFAR and ImageNet, 1% on Flowers.
  • Enhanced classification accuracy and computational efficiency.

Conclusions:

  • HybridBranchNetV2 offers superior adaptability and performance in complex AI tasks.
  • The model is suitable for real-time applications with reduced risk of overfitting.
  • The framework significantly improves adaptability, performance, and computational efficiency.