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Narjes Davari
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2020 – today
- 2024
- [j2]João Gama, Rita P. Ribeiro, Saulo Martiello Mastelini, Narjes Davari, Bruno Veloso:
From fault detection to anomaly explanation: A case study on predictive maintenance. J. Web Semant. 81: 100821 (2024) - [c5]Narjes Davari, Bruno Veloso, Rita Paula Ribeiro, João Manuel Portela da Gama:
Detecting and Explaining Anomalies in the Air Production Unit of a Train. SAC 2024: 358-364 - [i2]João Gama, Rita P. Ribeiro, Saulo Martiello Mastelini, Narjes Davari, Bruno Veloso:
A Neuro-Symbolic Explainer for Rare Events: A Case Study on Predictive Maintenance. CoRR abs/2404.14455 (2024) - 2023
- [d1]Narjes Davari, Bruno Veloso, Rita P. Ribeiro, João Gama:
MetroPT-3 Dataset. UCI Machine Learning Repository, 2023 - [i1]Sepideh Pashami, Slawomir Nowaczyk, Yuantao Fan, Jakub Jakubowski, Nuno Paiva, Narjes Davari, Szymon Bobek, Samaneh Jamshidi, Hamid Sarmadi, Abdallah Alabdallah, Rita P. Ribeiro, Bruno Veloso, Moamar Sayed Mouchaweh, Lala H. Rajaoarisoa, Grzegorz J. Nalepa, João Gama:
Explainable Predictive Maintenance. CoRR abs/2306.05120 (2023) - 2022
- [c4]Narjes Davari, Sepideh Pashami, Bruno Veloso, Slawomir Nowaczyk, Yuantao Fan, Pedro Mota Pereira, Rita P. Ribeiro, João Gama:
A Fault Detection Framework Based on LSTM Autoencoder: A Case Study for Volvo Bus Data Set. IDA 2022: 39-52 - [c3]Rita P. Ribeiro, Saulo Martiello Mastelini, Narjes Davari, Ehsan Aminian, Bruno Veloso, João Gama:
Online Anomaly Explanation: A Case Study on Predictive Maintenance. PKDD/ECML Workshops (2) 2022: 383-399 - [c2]Narjes Davari, Bruno Veloso, Rita P. Ribeiro, João Gama:
Fault Forecasting Using Data-Driven Modeling: A Case Study for Metro do Porto Data Set. PKDD/ECML Workshops (2) 2022: 400-409 - 2021
- [j1]Narjes Davari, Bruno Veloso, Gustavo de Assis Costa, Pedro Mota Pereira, Rita P. Ribeiro, João Gama:
A Survey on Data-Driven Predictive Maintenance for the Railway Industry. Sensors 21(17): 5739 (2021) - [c1]Narjes Davari, Bruno Veloso, Rita P. Ribeiro, Pedro Mota Pereira, João Gama:
Predictive maintenance based on anomaly detection using deep learning for air production unit in the railway industry. DSAA 2021: 1-10
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last updated on 2024-12-03 21:18 CET by the dblp team
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