Applied Machine Learning in Industry 4.0
A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".
Deadline for manuscript submissions: 30 January 2026 | Viewed by 1262
Special Issue Editors
Interests: swarm intelligence; distributed collaboration; intelligent decision-making
Special Issues, Collections and Topics in MDPI journals
Interests: artificial intelligence and robotics; swarm intelligence; computational intelligence; design automation; computer vision
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
The Fourth Industrial Revolution has transformed decision-making in information-intensive environments through advanced machine learning (ML) and foundation models. This Special Issue emphasizes domain-constrained intelligence—systems integrating domain-specific knowledge architectures (e.g., ontologies and rule repositories) with adaptive learning to address dynamic challenges. Innovations include vertical decision engines, domain-adapted transformers, multimodal RAG systems unifying structured/unstructured data, and self-optimizing frameworks using real-time feedback. These enable context-aware decision augmentation, expert-guided validation, and cross-modal fusion, ensuring compliance with domain logic.
Critical advances require human-AI co-reasoning systems that merge large language models (LLMs) with domain rule constraints. Hybrid frameworks must achieve interpretable decision provenance (e.g., tracing reasoning via knowledge graphs) and robust performance under adversarial scenarios (e.g., data noise and information attacks). Submissions must validate domain knowledge integration, quantify expert intervention impacts, and address challenges such as balancing generalization/specificity, aligning multimodal data semantics, and optimizing human-AI cognitive workflows.
We prioritize reproducible case studies in information-centric verticals: LLM-enhanced analysis with dynamic threat simulation, compliance reasoning via knowledge bases, and multimodal decision support in complex environments. Contributions should advance context-aware architectures, human-AI co-evolution mechanisms, and decision lifecycle metrics. Target outcomes include auditable decision protocols, causal reasoning models, and privacy-preserving distributed learning solutions. This collection aims to establish methodologies for trustworthy systems that unify data-driven learning with domain expertise, shaping the future of intelligent decision ecosystems.
Dr. Xiaomin Zhu
Prof. Dr. Zhun Fan
Guest Editors
Manuscript Submission Information
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Keywords
- domain-constrained intelligence
- decision-centric systems
- human-AI co-reasoning
- knowledge graph integration
- multimodal data fusion
- real-time feedback optimization
- compliance-driven AI
- semantic alignment
- distributed learning privacy
- cognitive efficiency optimization
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