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[null,"1.","Record Nr.",null,"UNINA9910983079003321"]
[null,null,"Autore",null,"Meo Rosa"]
[null,null,"Titolo",null,"Machine Learning and Principles and Practice of Knowledge Discovery in Databases : International Workshops of ECML PKDD 2023, Turin, Italy, September 18–22, 2023, Revised Selected Papers, Part III / / edited by Rosa Meo, Fabrizio Silvestri"]
[null,null,"Pubbl/distr/stampa",null,"Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2025"]
[null,null,"ISBN",null,"9783031746338"]
[{"$nulls":4},"3031746333"]
[null,null,"Edizione",null,"[1st ed. 2025.]"]
[null,null,"Descrizione fisica",null,"1 online resource (594 pages)"]
[null,null,"Collana",null,"Communications in Computer and Information Science, , 1865-0937 ; ; 2135"]
[null,null,"Disciplina",null,"006.31"]
[null,null,"Soggetti",null,"Artificial intelligence"]
[{"$nulls":4},"Image processing - Digital techniques"]
[{"$nulls":4},"Computer vision"]
[{"$nulls":4},"Computer engineering"]
[{"$nulls":4},"Computer networks"]
[{"$nulls":4},"Application software"]
[{"$nulls":4},"Computers"]
[{"$nulls":4},"Data structures (Computer science)"]
[{"$nulls":4},"Information theory"]
[{"$nulls":4},"Artificial Intelligence"]
[{"$nulls":4},"Computer Imaging, Vision, Pattern Recognition and Graphics"]
[{"$nulls":4},"Computer Engineering and Networks"]
[{"$nulls":4},"Computer and Information Systems Applications"]
[{"$nulls":4},"Computing Milieux"]
[{"$nulls":4},"Data Structures and Information Theory"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,"Nota di contenuto",null,"-- XAI-TS: Explainable AI for Time Series: Advances and Applications.  -- Introducing the Attribution Stability Indicator: a Measure for Time Series XAI Attributions.  -- LMFD: Latent Monotonic Feature Discovery.  -- LinC: Explaining Time Series Clusterings with User-Provided Constraints.  -- Explainable Long- and Short-term Pattern Detection in Projected Sequential Data.  -- XKDD 2023: 5th International Workshop "]
[{"$nulls":4},"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."]
[null,null,null,"Sommario/riassunto","The five-volume set CCIS 2133-2137 constitutes the refereed proceedings of the workshops held in conjunction with the Joint European Conference on Machine Learning and Knowledge Discovery in "]
[{"$nulls":4},"Databases, ECML PKDD 2023, which took place in Turin, Italy, during September 18-22, 2023. The 200 full papers presented in these proceedings were carefully reviewed and selected from 515 submissions. The papers have been organized in the following tracks: Part I: Advances in Interpretable Machine Learning and Artificial Intelligence -- Joint Workshop and Tutorial; BIAS 2023 - 3rd Workshop on Bias and Fairness in AI; Biased Data in Conversational Agents; Explainable Artificial Intelligence: From Static to Dynamic; ML, Law and Society; Part II: RKDE 2023: 1st International Tutorial and Workshop on Responsible Knowledge Discovery in Education; SoGood 2023 – 8th Workshop on Data Science for Social Good; Towards Hybrid Human-Machine Learning and Decision Making (HLDM); Uncertainty meets explainability in machine learning; Workshop: Deep Learning and Multimedia Forensics. Combating fake media and misinformation; Part III: XAI-TS: Explainable AI for Time Series: Advances and Applications; XKDD 2023: 5th International Workshop on eXplainable Knowledge Discovery in Data Mining; Deep Learning for Sustainable Precision Agriculture; Knowledge Guided Machine Learning; MACLEAN: MAChine Learning for EArth ObservatioN; MLG: Mining and Learning with Graphs; Neuro Explicit AI and Expert Informed ML for Engineering and Physical Sciences; New Frontiers in Mining Complex Patterns; Part IV: PharML, Machine Learning for Pharma and Healthcare Applications; Simplification, Compression, Efficiency and Frugality for Artificial intelligence; Workshop on Uplift Modeling and Causal Machine Learning for Operational Decision Making; 6th Workshop on AI in Aging, Rehabilitation and Intelligent Assisted Living (ARIAL); Adapting to Change: Reliable Multimodal Learning Across Domains; AI4M: AI for Manufacturing; Part V: Challenges and Opportunities of Large Language Models in Real-World Machine Learning Applications; Deep learning meets Neuromorphic Hardware; Discovery challenge; ITEM: IoT, Edge, and Mobile for Embedded Machine Learning; LIMBO - LearnIng and Mining for BlOckchains; Machine Learning for Cybersecurity (MLCS 2023); MIDAS - The 8th Workshop on MIning DAta for financial applicationS; Workshop on Advancements in Federated Learning."]