ListarGI-LIDIA - Artigos por tema "Anomaly detection"

Mostrando ítems 1-5 de 5

    • Data-driven predictive maintenance framework for railway systems 

      Meira, Jorge; Veloso, Bruno; Bolón-Canedo, Verónica; Marreiros, Goreti; Alonso-Betanzos, Amparo; Gama, João (IOS Press, 2023)
      [Abstract]: The emergence of the Industry 4.0 trend brings automation and data exchange to industrial manufacturing. Using computational systems and IoT devices allows businesses to collect and deal with vast volumes of ...

    • Explained anomaly detection in text reviews: Can subjective scenarios be correctly evaluated? 

      Novoa-Paradela, David; Fontenla-Romero, Óscar; Guijarro-Berdiñas, Bertha (2024-07)
      In the current landscape, user opinions exert an unprecedented influence on the trajectory of companies. In the field of online review platforms, these opinions, transmitted through text reviews and numerical ratings, ...

    • Fast anomaly detection with locality-sensitive hashing and hyperparameter autotuning 

      Meira, Jorge; Eiras-Franco, Carlos; Bolón-Canedo, Verónica; Marreiros, Goreti; Alonso-Betanzos, Amparo (Elsevier, 2022-08)
      [Abstract]: This paper presents LSHAD, an anomaly detection (AD) method based on Locality Sensitive Hashing (LSH), capable of dealing with large-scale datasets. The resulting algorithm is highly parallelizable and its ...

    • Fast deep autoencoder for federated learning 

      Novoa-Paradela, David; Fontenla-Romero, Óscar; Guijarro-Berdiñas, Bertha (Elsevier Ltd, 2023-11)
      [Abstract]: This paper presents a novel, fast and privacy preserving implementation of deep autoencoders. DAEF (Deep AutoEncoder for Federated learning), unlike traditional neural networks, trains a deep autoencoder network ...

    • Large scale anomaly detection in mixed numerical and categorical input spaces 

      Eiras-Franco, Carlos; Martínez Rego, David; Guijarro-Berdiñas, Bertha; Alonso-Betanzos, Amparo; Bahamonde, Antonio (Elsevier, 2019)
      [Abstract]: This work presents the ADMNC method, designed to tackle anomaly detection for large-scale problems with a mixture of categorical and numerical input variables. A flexible parametric probability measure is ...