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,…mehr
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.
Produktdetails
Produktdetails
Communications in Computer and Information Science 2615
Artikelnr. des Verlages: 89576376, 978-3-032-02002-4
Seitenzahl: 112
Erscheinungstermin: 10. September 2025
Englisch
Abmessung: 235mm x 155mm
ISBN-13: 9783032020024
ISBN-10: 3032020026
Artikelnr.: 74854055
Herstellerkennzeichnung
Springer-Verlag GmbH
Tiergartenstr. 17
69121 Heidelberg
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Inhaltsangabe
.- 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.
.- 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.
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