Buch, Englisch, 592 Seiten, Format (B × H): 155 mm x 235 mm, Gewicht: 914 g
17th International Conference, ICAART 2025, Porto, Portugal, February 23-25, 2025, Revised Selected Papers, Part III
Buch, Englisch, 592 Seiten, Format (B × H): 155 mm x 235 mm, Gewicht: 914 g
Reihe: Lecture Notes in Artificial Intelligence
ISBN: 978-3-032-25034-6
Verlag: Springer
This book constitutes the post publication of extended paper versions from the 17th International Conference on Agents and Artificial Intelligence, ICAART 2025, which took place in Porto, Portugal, during February 23–25, 2025.
The 52 full papers and 39 short papers presented in these two volumes were carefully reviewed and selected from 472 submissions.
The papers are organized in the following topical sections of relevant trends of current research on Agents and Artificial Intelligence, including: Machine Learning, Deep Learning, Multi-Agent Systems, Natural Language Processing, AI and Creativity, Intelligence and Cybersecurity,
Explainable AI, Industrial Applications of AI, Simulation and Agent Models and Architectures.
Zielgruppe
Research
Autoren/Hrsg.
Fachgebiete
- Mathematik | Informatik EDV | Informatik Technische Informatik Netzwerk-Hardware
- Mathematik | Informatik EDV | Informatik EDV & Informatik Allgemein
- Mathematik | Informatik EDV | Informatik Informatik Mathematik für Informatiker
- Mathematik | Informatik EDV | Informatik Informatik Bildsignalverarbeitung
- Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Wissensbasierte Systeme, Expertensysteme
Weitere Infos & Material
.- Artificial Intelligence.
.- Evolving AI for Preclinical Alzheimer’s Disease: A Systematic Literature
Review from Static Detection to Multimodal and Explainable Models.
.- LLM-Augmented Reinforcement Learning for Scalable Web GUI Testing.
.- Autonomous Manoeuvre Planning for Low-Thrust Satellites via Reinforcement Learning.
.- Resource-Efficient Techniques for Hyperparameter Optimization in Machine Learning.
.- Machine Learning for Prostate Cancer Risk Stratification: A Web-Based Tool with Explainability and Fairness.
.- GQL-QL: A Reinforcement Learning Framework for High-Coverage Automated Testing of GraphQL APIs.
.- Species-Aware Fish Catch Forecasting Using CatBoost and Multi-Source Temporal Features.
.- From Tiny to 72 B: Evaluating Vision–Language Models for E-commerce Image Validation.
.- PUR: A Standalone Metric for Measuring Neural Network Computational Efficiency.
.- Soccer Action Spotting with Multimodal Fusion and Markov-Guided Transformers.
.- CapTrAD: A Hybrid Model for Alzheimer’s Disease Classification.
.- Investigating User-Side Answer Errors and Large Language Model Awareness in Goal-Oriented Conversations.
.- Analyzing and Comparing Machine Learning Models via Inductive Orientation.
.- Enhancing Disease Classification in Emergency Medical Scenarios Through Synthetic Data: A Comparative Study.
.- Analogy-Based Classifier for Text Morphological Disambiguation: A Comparative Study.
.- From Realistic to Story-Worth Simulations: Making Agents Dramatic.
.- Towards Interpretable Deep Learning for Early Alzheimer’s Diagnosis: An Improved YOLOv8 Framework with Grad-CAM Validation.
.- Transformer Architectures for Option Pricing: Valuation, Prediction, and Trading.
.- Improving Brain Tumor Diagnosis and Multiclassification with Capsule Network-Based Deep Learning.
.- Improving Generative Cross-Lingual Aspect-Based Sentiment Analysis with Constrained Decoding.
.- Cross-Domain Analysis of the Effectiveness of LLMs in Essay Assessment: From the Technical Rigor of IME to the Large-Scale Context of ENEM.
.- Artificial Intelligence in Model-Driven Architecture: A Comprehensive Survey and Context-Aware Framework.
.- Large Language Models for the Summarization of Czech Documents: From History to the Present.
.- Behavior-Driven Test Generation from Natural Language Using Agents and Large Language Models.
.- A Federated Calibration Framework for ML-Based Intrusion Detection Systems.
.- Genetic Programming in Federated Aggregation: A comparison of FedGP with State of the Art Methods.
.- A Neural Approach to Building a Lexicon for a Symbolic Minimalist Parser.
