.- XAI-TS: Explainable AI for Time Series: Advances and Applications.
.- Introducing the Attribution Stability Indicator: a Measure for Time Series XAI Attributions.
.- LMFD: Latent Monotonic Feature Discovery.
.- LinC: Explaining Time Series Clusterings with User-Provided Constraints.
.- Explainable Long- and Short-term Pattern Detection in Projected Sequential Data.
.- XKDD 2023: 5th International Workshop on eXplainable Knowledge Discovery in Data Mining.
.- Matching the expert's knowledge via a counterfactual-based feature importance measure.
.- Explaining Fatigue in Runners Using Time Series Analysis on Wearable Sensor Data.
.- Wave Top-k Random-d Family Search: How to Guide an Expert in a Structured Pattern Space.
.- Diffusion-based Visual Counterfactual Explanations - Towards Systematic Quantitative Evaluation.
.- Exploring gender bias in misclassification with clustering and local explanations.
.- Are Generative-based Graph Counterfactual Explainers Worth It?.
.- FIPER: a Visual-based Explanation Combining Rules and Feature Importance.
.- Manipulation Risks in Explainable AI: The Implications of the Disagreement Problem.
.- Using Graph Neural Networks for the Detection and Explanation of Network Intrusions.
.- Game Theoretic Explanations for Graph Neural Networks.
.- From Black Box to Glass Box: Evaluating the Faithfulness of Process Predictions with GCNNs.
.- A New Class of Intelligible Models for Tabular Learning.
.- Deep Learning for Sustainable Precision Agriculture.
.- Plant Disease Detection using Deep Learning: A.
.- Proof of Concept on Pear Leaf Disease Detection.
.- Modelling Solar PV Adoption in Irish Dairy Farms using Agent-Based Modelling.
.- Deep Networks based Approach for Automatic Counting Panicles on UAV captured Paddy RGB Imagery.
.- The ACRE Crop-Weed Dataset for Benchmarking Weed Detection Models on Maize and Beans Fields.
.- Integrating Renewable Energy in Agriculture: A Deep Reinforcement Learning-based Approach.
.- Knowledge Guided Machine Learning.
.- Unsupervised Ontology- and Taxonomy Construction through Hyperbolic Relational Domains and Ranges.
.- A Filter-based Neural ODE Approach for Modelling Natural Systems with Prior Knowledge Constraints.
.- Towards Automatically Refining Low-Quality Domain Knowledge: A Case Study in Healthcare.
.- Lorentz-invariant augmentation for high-energy physics deep learning models.
.- Discovering SpatioTemporal Warning Contexts from Non-Emergency Call Reports.
.- SEEDOT: Tool for Enhancing Sentiment Lexicon with Machine Learning.
.- MACLEAN: MAChine Learning for EArth ObservatioN.
.- Detection and semantic description of changes in Earth Observation Time Series data.
.- Low-rank hierarchical clustering of PRISMA hyperspectral images to identify burned areas.
.- Next day fire prediction via semantic segmentation.
.- Robust Burned Area Delineation through Multitask Learning.
.- Burnt area extraction from high-resolution satellite images based on anomaly detection.
.- Seasonal average temperature forecast with the AutoGluonTS modern autoML tool.
.- MLG: Mining and Learning with Graphs.
.- Curvature-based Pooling within Graph Neural Networks.
.- Finding coherent node groups in directed graphs.
.- Neuro Explicit AI and Expert Informed ML for Engineering and Physical Sciences.
.- Constructing Neural Forms for Hard-Constraint PINNs with Complex Dirichlet Boundaries.
.- Enhancing generability: AutoML for robust denoising of strong gravitational lens systems.
.- Data-Efficient Interactive Multi-Objective Optimization Using ParEGO.
.- New Frontiers in Mining Complex Patterns.
.- Striving for Simplicity in Deep Neural Models Trained for Malware Detection.
.- On the Effectiveness of Non-negative Matrix Factorization for Text Open-set Recognition.
.- Real-time Anomaly Prediction from Cryptocurrency Time Series.
.- A Joint Analysis of Trajectory Mining and Process Mining for Smartphone User Behaviour.
.- Towards Automation of Pollen Monitoring - Dealing with the Background in Pollen Monitoring Images.
