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A Novel Approach for Evaluating Spatial-Temporal Synergy in Hybrid CNN-RNN and Vision Transformer Architectures

Author

Listed:
  • Viren Passi

    (Chandigarh College of Engineering and Technology, Chandigarh, India)

  • Sudhakar Kumar

    (Chandigarh College of Engineering and Technology, Chandigarh, India)

  • Sunil K. Singh

    (Chandigarh College of Engineering and Technology, Chandigarh, India)

  • Shreya Verma

    (Chandigarh College of Engineering and Technology, Chandigarh, India)

  • Varsha Arya

    (Electronic Engineering and Computer Science, Hong Kong Metropolitan University, Hong Kong, China & Center for Interdisciplinary Research, University of Petroleum and Energy Studies, Dehradun, India & UCRD, Chandigarh University, Chandigarh, India)

  • Valerie Tang

    (Department of Supply Chain and Information Management, The Hang Seng University of Hong Kong, Shatin, Hong Kong)

  • Brij B. Gupta

    (Department of Computer Science and Information Engineering, Asia University, Taichung, Taiwan & VIZJA University, Warsaw, Poland & Symbiosis Centre for Information Technology, Symbiosis International University, Pune, India & School of Cybersecurity, Korea University, Seoul, South Korea)

  • Kwok Tai Chui

    (Electronic Engineering and Computer Science, Hong Kong Metropolitan University, Hong Kong, China)

Abstract

Pattern recognition is key to enhancing automated decision systems, particularly for image data analysis. As visual data grows in volume and complexity, robust models for accurate pattern interpretation are essential. This study examines multi-class image recognition via advanced architectures including CNNs, hybrid CNN-RNNs, and Vision Transformers (ViT), with comparative experiments. Performance metrics include accuracy, precision, recall, and F-score. The hybrid CNN-RNN model excelled, achieving 92.24% test accuracy by fusing spatial and temporal features. Precision and recall exceeded 90%, proving effectiveness across patterns. This work demonstrates how advanced methods boost classification accuracy, aiding smarter AI for real-world tasks.

Suggested Citation

  • Viren Passi & Sudhakar Kumar & Sunil K. Singh & Shreya Verma & Varsha Arya & Valerie Tang & Brij B. Gupta & Kwok Tai Chui, 2026. "A Novel Approach for Evaluating Spatial-Temporal Synergy in Hybrid CNN-RNN and Vision Transformer Architectures," International Journal of Intelligent Information Technologies (IJIIT), IGI Global Scientific Publishing, vol. 22(1), pages 1-22, January.
    • Handle: RePEc:igg:jiit00:v:22:y:2026:i:1:p:1-22
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