Fischer / Göhner / Gül-Ficici Database and Expert Systems Applications - DEXA 2025 Workshops

AISys and AI4IP, Bangkok, Thailand, August 25–27, 2025, Proceedings
Erscheinungsjahr 2025
ISBN: 978-3-032-02003-1
Verlag: Springer International Publishing
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, 116 Seiten

Reihe: Communications in Computer and Information Science

This volume constitutes the refereed proceedings of the 7th International Workshop on AI System Engineering: Math, Modelling and Software, AISys 2025 and the First International Workshop on Optimisation of Industrial Production with AI Algorithms, AI4IP, co-located with the 36th International Conference on Database and Expert Systems Applications, DEXA 2025, which took place in Bangkok, Thailand, during August 25-27, 2025.
The 11 full papers were thoroughly reviewed and selected from a total of 23 submissions. They are organized in topical sections as follows: AI System Engineering: Math, Modelling and Software; and Optimization of Industrial Production with AI Algorithms.

Zielgruppe

Research

Autoren/Hrsg.

Fischer, Lukas

Göhner, Ulrich

Gül-Ficici, Sebnem

Jacob, Dirk

Kotsis, Gabriele

Tjoa, A Min

Khalil, Ismail

Inhaltsverzeichnis

.- AI System Engineering: Math, Modelling and Software.

.- Exploring the benefits of iterative retrieval-augmented generation for risk mitiga tion in LLM response.

.- TrustAI: Designing and Implementing a Trustworthy and User-Centered AI Plat form.

.- Collaborative Trustworthy Foundation Model Framework: An Environmental  Sustainability Use-Case to Detect Contamination Objects in Organic Waste  Streams.

.- Optimisation of Industrial Production with AI Algorithms.

.- Efficient Federated Learning Integration into Existing MLOps Pipelines via Centralized Model Management.

.- Deep Photometric Stereo for Tool Wear Inspection.

.- Multi-Objective Reinforcement Learning for Energy-Efficient Industrial Control.

.- Deep learning-based defect detection in laser powder bed fusion.

.- Prediction of CNC Manufacturing Time Under Real-World Conditions Using  Graph Convolutional Networks.

.- A Vision-Guided Approach to Pick-and-Place Robotics: From Assembly Drawings to Industrial Assembly Automation.

.- Towards Real-time Tool Wear Detection on Edge Devices: A Lightweight Di mensionality Reduction Approach for Spindle Integrated Cutting Force Sensor 
Data.

.- Energy Optimized Piecewise Polynomial Approximation Utilizing Modern Ma chine Learning Optimizers.