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Journal of Data Intelligence and AI Systems

Journal of Data Intelligence and AI Systems
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Current Issue

Vol. 1 No. 1 (2026): Journal of Data Intelligence and AI Systems
					View Vol. 1 No. 1 (2026): Journal of Data Intelligence and AI Systems
Published: 2026-01-30

Articles

  • Hybrid Symbolic-Neural Reasoning Frameworks for Autonomous Scientific Discovery Systems

    Yuejeng Ye, Stefano D. Hansen, Siddharth Krasad (Author)
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  • Knowledge Graph-Enhanced Recommendation Systems for Personalized E-Commerce Intelligence

    Benri J. Perez, Viktor Webb, Leo Wilson (Author)
    • PDF
  • Large Language Model-Augmented Cybersecurity Threat Detection for Cloud-Native Infrastructures

    Arthur May, Kihir Trivedi (Author)
    • PDF
  • Multi-Agent Deep Reinforcement Learning for Cooperative UAV Swarm Navigation in Disaster Response

    Elliot M. Schmidt (Author)
    • PDF
  • Neuromorphic Computing Architectures for Ultra-Low Latency Edge Intelligence Applications

    Hudson Kook, Kruce Ferguson (Author)
    • PDF
  • Quantum-Inspired Optimization Algorithms for Scalable AI Scheduling Systems

    Xin Wei Jia, Wayne Carpenter (Author)
    • PDF
  • Secure Federated Generative AI for Distributed Medical Image Synthesis and Diagnosis

    Blaudio Gdwards, ChengLin Song (Author)
    • PDF
  • Self-Supervised Vision Transformers for Agricultural Disease Detection in Unstructured Field Environments

    Otis A. Powell, Beremy Thornton, Kdwin Moran, ZhenTian Tang (Author)
    • PDF
  • Trustworthy AI Frameworks for Financial Risk Prediction under Non-Stationary Market Conditions

    Brun Roy, Pierre C. Lowe, Paxime Lopez (Author)
    • PDF
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Journal Information

Journal Name: Journal of Data Intelligence and AI Systems

Languages: English

ISSN Print:  

ISSN Online:  

Publication Frequency: Quarterly

Audience: Researchers, academics, engineers, data scientists, and industry practitioners in data intelligence, artificial intelligence, machine learning, intelligent systems, computer science, and related interdisciplinary fields.

Review Type: Double Blind Peer Review

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