Machine Learning and Principles and Practice of Knowledge Discovery in Databases

.- 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.