Advancing Smart Systems Through Deep Learning, Generative AI, and Big Data Analytics

Dear Colleagues,

The rapid advancements in deep learning, generative AI, and big data analytics have significantly transformed smart systems across various domains, including industrial applications, higher education, manufacturing, and agriculture. These technologies have revolutionized the way industries operate, enabling automation, intelligent decision-making, and enhanced productivity. In industrial applications, AI-driven predictive maintenance, quality control, and supply chain optimization have resulted in increased efficiency and reduced downtime. In higher education, generative AI is reshaping teaching methodologies by personalizing learning experiences, automating content generation, and providing AI-assisted tutoring. In manufacturing, the integration of deep learning and big data analytics has enabled smart factories, where real-time monitoring, predictive analytics, and adaptive control systems enhance production processes. In agriculture, the application of deep learning and big data analytics has enhanced our knowledge of agriculture in various aspects, such as protein-directed evolution, smart breeding, smart livestock farming, and so on. The convergence of these technologies is paving the way for more intelligent, data-driven, and autonomous systems that will continue to evolve across various sectors.

This Special Issue, titled ‘Advancing Smart Systems Through Deep Learning, Generative AI, and Big Data Analytics’, aims to bring together cutting-edge research that explores the integration of deep learning, generative AI, and reinforcement learning techniques to enhance smart manufacturing, Digital Intelligent Education, and smart agriculture. This Special Issue will focus on, but is not limited to, the following key areas:

1. Deep learning for smart systems:
   • Advanced deep learning models for intelligent decision-making;
   • Deep learning-driven automation and optimization in smart environments;
   • Edge and fog computing for real-time deep learning applications.
2. Generative AI and big data analytics in industrial applications:
   • AI-driven predictive maintenance and fault diagnosis;
   • Generative AI for industrial process optimization;
   • AI-powered digital twins for manufacturing systems;
   • Data-driven decision-making in Industry 4.0;
   • IoT and big data integration in smart factories;
   • Real-time data processing for predictive analytics in manufacturing.
3. Generative AI in higher education:
   • AI-powered personalized learning and assessment tools;
   • GenAI for automated content generation in educational platforms;
   • AI-driven student engagement and feedback mechanisms.
4. Human–robot collaboration with deep reinforcement learning:
   • Adaptive learning strategies for human–robot teaming;
   • Deep reinforcement learning for autonomous robotic systems;
   • Safety and trust in human–robot collaborative environments.
5. Deep learning for smart agriculture:
   • Deep learning for smart breeding;
   • Deep learning for smart livestock farming;
   • Deep learning for protein directed evolution.
6. Artificial intelligence for next-generation networks and digital ecosystems:
   • Artificial intelligence for 5G verticals;
   • Artificial intelligence in digital health;
   • Intelligent IoT and digital twin applications in beyond 5G systems.

Dr. Xin Lu
Dr. Jianhua Yang
Dr. Xiaoxia Li
Dr. Dehao Wu
Dr. Wei Wang
Guest Editors

Manuscript Submission Information

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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

• Artificial Intelligence (AI)
• deep learning
• Generative AI (GenAI)
• big data analytics
• smart systems
• smart manufacturing
• human–robot collaboration
• deep reinforcement learning
• digital twins
• predictive maintenance
• IoT in manufacturing
• AI in higher education
• smart agriculture