arXiv Machine Learning
Sparse Incident-Cluster Learning for 12-hour Port Flood Pre-warning in Digital-Twin Analytics
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arXiv AI
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An Incremental Knowledge-Graph Learner Digital Twin for Evidence-Grounded Early Warning
The paper presents an incremental Learner Digital Twin that uses a lightweight knowledge graph to provide evidence‑grounded early warning in learning analytics. The twin is updated at temporal checkpoints by replaying learning traces and assessment records, enabling both feature extraction and explanation grounding. Evaluated on a public benchmark, the graph‑mediated approach preserves predictive performance while generating alerts that link risk decisions to specific evidence items and raw records.
arXiv AI
Jun 26
Digital Twin-Driven Communication-Efficient Federated Anomaly Detection for Industrial IoT
arXiv:2601. 01701v2 Announce Type: replace-cross Abstract: Anomaly detection is increasingly becoming crucial for maintaining the safety, reliability, and efficiency of industrial systems.
arXiv Machine Learning
Sep 23
Event-Based Early Warning of Vineyard Disease Risk from Environmental Time Series
The paper proposes an event-based approach to predict transitions into vineyard disease‑risk periods within a 3–7 day window, rather than daily disease status. It defines events only after a minimum disease‑free gap to reduce label fragmentation and uses multi‑year agro‑meteorological data to capture humidity, rainfall, temperature, and seasonal patterns. Experiments with XGBoost, LSTM, and TCN models show that this formulation improves short‑horizon warning, highlighting trade‑offs among recall, lead time, and false alerts.
arXiv AI
Jun 30
Redefining Maritime Anomaly Detection via Equation-Grounded Synthetic Anomalies
arXiv:2606. 29721v1 Announce Type: cross Abstract: Maritime anomaly detection is essential for ensuring maritime safety, security, and efficient traffic management at sea, with Automatic Identification System (AIS) data serving as a primary data source.
arXiv AI
Aug 13
TailBooster: A Dual-Layer Generative Framework for Extreme Value Augmentation with Operational Validity Enforcement
arXiv:2608. 11951v1 Announce Type: cross Abstract: Extreme events in air transport, such as severe arrival delays and abnormal air times, cause cascading network disruptions with substantial operational, economic, and safety costs.
arXiv AI
Jul 23
A Unified Survival Benchmark for Temporal Dropout Risk Prediction in Learning Analytics
arXiv:2604. 08870v3 Announce Type: replace-cross Abstract: Student dropout is a persistent concern in Learning Analytics, yet comparative studies frequently evaluate predictive models under heterogeneous protocols, prioritizing discrimination over temporal interpretability and calibration.