Data Science Empowers Intelligent Systems: Theories and Applications

Dear Colleagues,

This Special Issue focuses on cutting-edge interdisciplinary research in data science and intelligent systems, with particular emphasis on core technologies such as machine learning, big data analytics, and knowledge graphs. Intelligent decision-making, autonomous systems, and human–machine collaboration are at the forefront of this evolving field. While advances in data science have significantly expanded the potential of intelligent systems, critical gaps remain between theoretical innovations and real-world deployment—especially in robustness, interpretability, and ethical governance.

This Special Issue aims to bridge these gaps by curating interdisciplinary studies that:

1. Develop novel DS theories to enhance IS reliability (e.g., causal inference, uncertainty quantification);
2. Design scalable architectures for complex scenarios (e.g., distributed/edge intelligence, cross-modal learning);
3. Embed ethical AI principles (fairness, privacy, explainability) into IS design;
4. Validate methodologies through domain-specific case studies.

By situating contributions within these objectives, this Special Issue addresses unmet needs in the literature—advancing beyond incremental algorithmic improvements to tackle core challenges in intelligent system trustworthiness, adaptability, and human-centricity.

The scope of submissions includes, but is not limited to:

1. Causal discovery and inference for intelligent decision systems;
2. Explainable AI (XAI) with formal robustness guarantees;
3. Privacy-preserving federated/edge learning architectures;
4. Neuro-symbolic integration for knowledge-enhanced reasoning;
5. Uncertainty-aware learning under distribution shifts;
6. Cross-modal fusion for heterogeneous data processing;
7. Generative AI for autonomous system simulation;
8. Reinforcement learning for adaptive control in robotics;
9. Real-time optimization in distributed intelligent systems;
10. Healthcare applications (e.g., clinical diagnostics, AI-driven drug discovery);
11. Smart city solutions (e.g., traffic optimization, infrastructure monitoring);
12. Industrial 4.0 technologies (e.g., predictive maintenance, digital twins);
13. Human–AI collaboration frameworks (e.g., cognitive assistants, ethical governance).

Dr. Junwei Yan
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Big Data and Cognitive Computing is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 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.

• big data
• intelligent system
• data theory
• interdisciplinary application
• artificial intelligence
• knowledge graph