Imagen de Portada

A Maritime Industry Experience for Vessel Operational Anomaly Detection: Utilizing Deep Learning Augmented with Lightweight Interpretable Models

Fuente: arXiv

Guardado en:

Detalles Bibliográficos
Autores principales:	Moghadam, Mahshid Helali, Rzymowski, Mateusz, Kulas, Lukasz
Formato:	Preprint
Publicado:	2023
Materias:	Machine Learning
Acceso en línea:	Acceder al recurso
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

Ejemplares similares

Alleviating Attack Data Scarcity: SCANIA's Experience Towards Enhancing In-Vehicle Cyber Security Measures
por: Sundfeldt, Frida, et al.
Publicado: (2025)

Aerial Maritime Vessel Detection and Identification
por: Kulas, Antonella Barisic, et al.
Publicado: (2025)

Federated Learning for Anomaly Detection in Maritime Movement Data
por: Graser, Anita, et al.
Publicado: (2025)

ConceptCaps: a Distilled Concept Dataset for Interpretability in Music Models
por: Sienkiewicz, Bruno, et al.
Publicado: (2026)

Knowledge-Augmented Explainable and Interpretable Learning for Anomaly Detection and Diagnosis
por: Atzmueller, Martin, et al.
Publicado: (2024)

A Transfer Learning Framework for Anomaly Detection in Multivariate IoT Traffic Data
por: Rezakhani, Mahshid, et al.
Publicado: (2025)

Interpretable Maximum Margin Deep Anomaly Detection
por: Yang, Zhiji, et al.
Publicado: (2026)

A Novel Evaluation Metric for Unsupervised Learning in AIS-Based Maritime Anomaly Detection: MADQI
por: Gocer, Ismet, et al.
Publicado: (2026)

Vessel Re-identification and Activity Detection in Thermal Domain for Maritime Surveillance
por: Ginige, Yasod, et al.
Publicado: (2024)

Retrieval Augmented Deep Anomaly Detection for Tabular Data
por: Thimonier, Hugo, et al.
Publicado: (2024)

Acoustic Classification of Maritime Vessels using Learnable Filterbanks
por: Elsborg, Jonas, et al.
Publicado: (2025)

Impact of Recurrent Neural Networks and Deep Learning Frameworks on Real-time Lightweight Time Series Anomaly Detection
por: Lee, Ming-Chang, et al.
Publicado: (2024)

Spatio-Temporal Graphs Beyond Grids: Benchmark for Maritime Anomaly Detection
por: Kim, Jeehong, et al.
Publicado: (2025)

Adaptive Sparsified Graph Learning Framework for Vessel Behavior Anomalies
por: Kim, Jeehong, et al.
Publicado: (2025)

A Comparison of Deep Learning Architectures for Spacecraft Anomaly Detection
por: Lakey, Daniel, et al.
Publicado: (2024)

Deep Learning Advancements in Anomaly Detection: A Comprehensive Survey
por: Huang, Haoqi, et al.
Publicado: (2025)

Multivariate Time Series Anomaly Detection in Industry 5.0
por: Colombi, Lorenzo, et al.
Publicado: (2025)

Open Challenges in Time Series Anomaly Detection: An Industry Perspective
por: Mueller, Andreas
Publicado: (2025)

AnomalyAID: Reliable Interpretation for Semi-supervised Network Anomaly Detection
por: Yuan, Yachao, et al.
Publicado: (2024)

Benchmarking Anomaly Detection Algorithms: Deep Learning and Beyond
por: Mehta, Shanay, et al.
Publicado: (2024)

Enhancing ECG Classification Robustness with Lightweight Unsupervised Anomaly Detection Filters
por: Ibrahim, Mustafa Fuad Rifet, et al.
Publicado: (2025)

tsGT: Stochastic Time Series Modeling With Transformer
por: Kuciński, Łukasz, et al.
Publicado: (2024)

HOTSPOT-YOLO: A Lightweight Deep Learning Attention-Driven Model for Detecting Thermal Anomalies in Drone-Based Solar Photovoltaic Inspections
por: Dhimish, Mahmoud
Publicado: (2025)

Advancing Welding Defect Detection in Maritime Operations via Adapt-WeldNet and Defect Detection Interpretability Analysis
por: S, Kamal Basha, et al.
Publicado: (2025)

LiteInception: A Lightweight and Interpretable Deep Learning Framework for General Aviation Fault Diagnosis
por: Wei, Zhihuan, et al.
Publicado: (2026)

TADA! Tuning Audio Diffusion Models through Activation Steering
por: Staniszewski, Łukasz, et al.
Publicado: (2026)

A Deep Learning Approach to Anomaly Detection in High-Frequency Trading Data
por: Bao, Qiuliuyang, et al.
Publicado: (2025)

Deep Learning for Time Series Anomaly Detection: A Survey
por: Darban, Zahra Zamanzadeh, et al.
Publicado: (2022)

Deep Learning Approach to Anomaly Detection in Enterprise ETL Processes with Autoencoders
por: Chen, Xin, et al.
Publicado: (2025)

Cross-Modal Deep Metric Learning for Time Series Anomaly Detection
por: Li, Wei, et al.
Publicado: (2025)

AIS-LLM: A Unified Framework for Maritime Trajectory Prediction, Anomaly Detection, and Collision Risk Assessment with Explainable Forecasting
por: Park, Hyobin, et al.
Publicado: (2025)

PatchAD: A Lightweight Patch-based MLP-Mixer for Time Series Anomaly Detection
por: Zhong, Zhijie, et al.
Publicado: (2024)

MSAD: A Deep Dive into Model Selection for Time series Anomaly Detection
por: Sylligardos, Emmanouil, et al.
Publicado: (2025)

Trajectory Guard -- A Lightweight, Sequence-Aware Model for Real-Time Anomaly Detection in Agentic AI
por: Advani, Laksh
Publicado: (2026)

Multi-Agent Reinforcement Learning for Maritime Operational Technology Cyber Security
por: Wilson, Alec, et al.
Publicado: (2024)

Time-EAPCR-T: A Universal Deep Learning Approach for Anomaly Detection in Industrial Equipment
por: Liang, Huajie, et al.
Publicado: (2025)

Application of Deep Generative Models for Anomaly Detection in Complex Financial Transactions
por: Tang, Tengda, et al.
Publicado: (2025)

A Distance-based Anomaly Detection Framework for Deep Reinforcement Learning
por: Zhang, Hongming, et al.
Publicado: (2021)

Weakly Augmented Variational Autoencoder in Time Series Anomaly Detection
por: Wu, Zhangkai, et al.
Publicado: (2024)

A Deep Learning Framework for Evaluating Dynamic Network Generative Models and Anomaly Detection
por: Rashnu, Alireza, et al.
Publicado: (2024)