Sandi Baressi Šegota

Sandi Baressi Šegota

Researching the application of AI in complex engineering systems

Editorial Work

Special issues and topical collections currently open for submissions. Contributions within scope are welcome — if you are unsure whether your work fits, write to sandi.baressi.segota@unipu.hr before preparing a full manuscript.

AI in Complex Engineering Systems (external link)

Computers (ISSN 2073-431X, section "AI-Driven Innovations") · MDPI · Guest Editor

Open for submissions — deadline 30 August 2027

The accurate prediction of performance and maintenance metrics in complex engineering systems has driven a rapid expansion in the application of artificial intelligence. AI methods are now routine across regression modelling of critical values, the detection of events and anomalies in numerical and image-based data, and the optimization of operational processes, in many cases outperforming traditional analytical or rule-based approaches.

This Special Issue explores and advances the use of AI in engineering contexts, with an emphasis on interdisciplinary collaboration and on making emerging AI paradigms more familiar to engineering-oriented researchers. Research articles, reviews and short communications are all welcome.

Topics of interest include:

  • Sensor fusion strategies for robust data interpretation
  • Generative models for data augmentation and simulation
  • Multi-agent optimization frameworks for complex control environments
  • Digital twins and AI-enhanced simulation
  • Edge AI for real-time industrial monitoring
  • Explainable AI in engineering decision-making
  • Resilient AI systems for fault-tolerant engineering
  • Large language models for documentation automation and semantic parsing

View the call at MDPI

Evolutionary Algorithms for Advanced Robotics (external link)

Discover Robotics (open access) · Springer Nature · Guest Editor, with Nikola Anđelić

Closed — deadline was 30 September 2026

This Collection focuses on the integration of evolutionary algorithms into robotic systems, and on their potential to solve complex problems in control, navigation, optimization and learning. Genetic algorithms, genetic programming and differential evolution offer adaptive, bio-inspired approaches that improve robotic performance, autonomy and decision-making in dynamic environments.

Original research, case studies and review articles are invited. Contributions addressing the challenges of evolving controllers, real-time adaptation, explainability and the scalability of these methods in both physical and simulated environments are particularly encouraged.

Topics of interest include:

  • Autonomous navigation
  • Swarm robotics and multi-agent systems
  • Robotic manipulation
  • Human-robot interaction
  • Evolving controllers and real-time adaptation

View the call at Springer Nature