Computational Intelligence

.- International Conference on Explainable AI for Neural and Symbolic Methods.
.- AutoCausalAIME: A CMA-ES-Driven Framework for Parametric
Penalty Tuning in Causal Inverse Explanations.
.- Leveraging Large Language Models for Generating and Evaluating Natural Language Explanations in XAI: A Comparative Study.
.- Uncertainty in Deep Model Performance for Radiology: A Case Study of Classifying Maxillary Sinus Appearance.
.- Explain to Gain: Optimising Performance Through Explainable Reinforcement Learning Parameter Investigation.
.- Quantifying Prototype Stability in ProtoPNet Without Manual Part Annotations.
.- Interpretable Railway Object Classification Using Part-Prototype Networks.
.- Efficient Construction of Interpretable Oblique Decision Trees.
.- Extracting Deterministic Finite Automata from RNNs via Hyperplane Partitioning and Learning.
.- Profiling German Text Simplification with Model-Fingerprints.
.- Attention Maps in 3D Shape Classification for Dental Stage Estimation with Class Node Graph Attention Networks.
.- Extensibility, Model Interpretability and Explainability, and Automation in ML.NET: A Comprehensive Analysis.
.- SemantriX: An Explainable Hybrid Model for Aligning Vector Similarity and Semantic Relevance.
.- Explainable Knowledge Access: Recursive and Rerank-Based RAG for Interpretable QA.
.- How Prompting Shapes Decisions: Analyzing LLM Behavior in XAI-Augmented Decision Support Systems.
.- Mechanistic Interpretability for Transformer-based Time Series Classification.
.- XAI-Driven Solutions to Enhance Safety for Limited-Mobility Road Users.
.- User Fairness in Recommender Systems using Beyond-Accuracy Basket Quality Metrics.
.- Analyzing Accuracy and Consistency of GPT 4o Mini in Trivial Pursuit, and the Implications for its Use in Professional Contexts.
.- Interpretable Explainable AI: Comparing Bayesian Structural Equation Modelling with Other Algorithms.
.- Unsupervised Hierarchical Growing Neural Architecture for Sensorimotor Map Learning.
.- Rule Extraction from Fake News Classifiers.
.- Contrasting Human and Emergent Concepts in Image Classifiers.
.- An Explainable Multi-Domain Document Summarization Framework using Domain-Aware Fine-Tuned Large Language Models.
.- SPAX: A Shapley-Based Point Attribution eXplanation for Interpreting 3D Point Cloud Classification.
.- A Privacy-Preserving and Explainable Approach for Anomaly Detection in Substation Networks.
.- Exposing Shortcuts in Image Classification by Aggregating Counterfactuals.
.- On Explainable Disease Progression Forecasting with Transformer Models.
.- International Conference on Neural Computation Theory and Applications.
.- Determining Optimal Pixel Resolution for Object Detection in Satellite Imagery: A Class-Specific Approach.
.- Re-Ranked Transformer: New Strategy Based on Misspellings and Typos Pattern Analysis for Keystroke Biometrics Improvement.
.- Towards Generalizing Deep Reinforcement Learning Algorithms for Real World Applications.
.- Degradation-Aware Energy Management in Residential Microgrids: A Reinforcement Learning Framework.
.- Innovative Techniques for Efficient Hyperdimensional Computing on Hardware: Enhance Accuracy and On-Fly Hypervector Generation.
.- A Universal Urban Electricity-Demand Simulator for Developing and Evaluating Load-Scheduling and Forecasting Systems.
.- Drowsiness Detection with Time-Series Classification Using HRV Features.
.- A Structured Survey of Anomaly Types and Classification-Based Detection Models in IoT.
.- Assessing Driving Style from Telematics Data with a Two-Stage Clustering Approach.
.- Fine-Tuning Prototypes for Cross-Domain Few-Shot Image Classification Using Contrastive Objective.
.- OS-QLR: One-Shot Quantized Latent Refinement for Fast and Efficient Image Generation.
.- Dataset-Independent Approach for Generating Synthetic Data in Optical Defect Detection.
.- Combining Large-Scale and Domain-Specific Datasets for Hate Speech Severity Modeling: A Regression-Based Approach.
.- MLP Model for Prediction of Pellet Combustion: How to Deal with Small Datasets.
.- Multi-Subspace SVD Generators for Continual Learning.
.- From High-Frequency Sensors to Noon Reports: Using Transfer Learning for Shaft Power Prediction in Maritime.
.- Towards Robust Urban Parking Violation Prediction Using Graph Kolmogorov–Arnold Networks and Liquid Neural Networks.
.- Data Augmentation for Neuroaesthetics Analysis.

• Erscheinungsdatum: 10.02.2026
• Autor/Autorin: Francesco Marcelloni
• Reihe: Springer Nature Proceedings excluding Computer Science
• Format: E-Book
• Dateiformat: PDF
• Kopierschutz: Wasserzeichen
• Dateigröße: 111.3 MB
• Verlag: SPRINGER
• Sprache: Englisch
• Umfang: 781 Seiten
• ISBN: 9783032156389
• Lieferung: Sofort per Download
• Hinweis: Sofort per Download lieferbar. Kein physischer Versand.
• Kompatibilität: Lesbar auf Geräten und Apps mit PDF-Unterstützung.