Search

Your search keyword '"Meira, Jorge"' showing total 4 results
Start Over Author "Meira, Jorge" Topic anomaly detection
4 results on '"Meira, Jorge"'

1. Comparative Results with Unsupervised Techniques in Cyber Attack Novelty Detection

Author

Meira, Jorge, Andrade, Rui, Praça, Isabel, Carneiro, João, Marreiros, Goreti, Kacprzyk, Janusz, Series Editor, Pal, Nikhil R., Advisory Editor, Bello Perez, Rafael, Advisory Editor, Corchado, Emilio S., Advisory Editor, Hagras, Hani, Advisory Editor, Kóczy, László T., Advisory Editor, Kreinovich, Vladik, Advisory Editor, Lin, Chin-Teng, Advisory Editor, Lu, Jie, Advisory Editor, Melin, Patricia, Advisory Editor, Nedjah, Nadia, Advisory Editor, Nguyen, Ngoc Thanh, Advisory Editor, Wang, Jun, Advisory Editor, Novais, Paulo, editor, Jung, Jason J., editor, Villarrubia González, Gabriel, editor, Fernández-Caballero, Antonio, editor, Navarro, Elena, editor, González, Pascual, editor, Carneiro, Davide, editor, Pinto, António, editor, Campbell, Andrew T., editor, and Durães, Dalila, editor

Published
2019
Full Text
View/download PDF

3. Data-driven predictive maintenance framework for railway systems.

Author

Meira, Jorge, Veloso, Bruno, Bolón-Canedo, Verónica, Marreiros, Goreti, Alonso-Betanzos, Amparo, and Gama, João

Subjects
*ANOMALY detection (Computer security), *BIG data, *INDUSTRY 4.0, *K-nearest neighbor classification, *ELECTRONIC data processing, *MACHINE learning, *RAILROADS
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 sensorial and business process data. The growing and proliferation of big data and machine learning technologies enable strategic decisions based on the analyzed data. This study suggests a data-driven predictive maintenance framework for the air production unit (APU) system of a train of Metro do Porto. The proposed method assists in detecting failures and errors in machinery before they reach critical stages. We present an anomaly detection model following an unsupervised approach, combining the Half-Space-trees method with One Class K Nearest Neighbor, adapted to deal with data streams. We evaluate and compare our approach with the Half-Space-Trees method applied without the One Class K Nearest Neighbor combination. Our model produced few type-I errors, significantly increasing the value of precision when compared to the Half-Space-Trees model. Our proposal achieved high anomaly detection performance, predicting most of the catastrophic failures of the APU train system. [ABSTRACT FROM AUTHOR]

Published
2023
Full Text
View/download PDF

4. Fast anomaly detection with locality-sensitive hashing and hyperparameter autotuning.

Author

Meira, Jorge, Eiras-Franco, Carlos, Bolón-Canedo, Verónica, Marreiros, Goreti, and Alonso-Betanzos, Amparo

Subjects
*ANOMALY detection (Computer security), *SCALABILITY
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 implementation in Apache Spark further increases its ability to handle very large datasets. Moreover, the algorithm incorporates an automatic hyperparameter tuning mechanism so that users do not have to implement costly manual tuning. Our LSHAD method is novel as both hyperparameter automation and distributed properties are not usual in AD techniques. Our results for experiments with LSHAD across a variety of datasets point to state-of-the-art AD performance while handling much larger datasets than state-of-the-art alternatives. In addition, evaluation results for the tradeoff between AD performance and scalability show that our method offers significant advantages over competing methods. [ABSTRACT FROM AUTHOR]

Published
2022
Full Text
View/download PDF

Catalog

Books, media, physical & digital resources
See catalog results