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A deep learning approach for anomaly detection and prediction in power consumption data

Autor: Hichem Snoussi, C. Chahla, Leila Merghem, Moez Esseghir

Publikováno v: Energy Efficiency. 13:1633-1651

Anomaly detection in power consumption data can be very useful to building managers. It allows them to detect unexpected power consumption values, identify unusual behaviors, and foresee uncommon events. This paper proposes a novel unsupervised appro

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::2a99aab0e07bc78ac5cb3519a8bf74ca
https://doi.org/10.1007/s12053-020-09884-2

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Multiple Cues Association for Multiple Object Tracking Based on Convolutional Neural Network

Autor: C. Chahla, Abel Cherouat, Ronghua Hu, Hichem Snoussi, Samir Bouindour

Publikováno v: IEEE AIKE 2019-Second International Conference on Artificial Intelligence and Knowledge Engineering
IEEE AIKE 2019-Second International Conference on Artificial Intelligence and Knowledge Engineering, Jun 2019, Cagliari, Italy. pp.117-122, ⟨10.1109/AIKE.2019.00030⟩
AIKE

International audience; Tracking-by-detection is a popular framework for Multiple Object Tracking (MOT) where detectors produce a set of labeled detection to indicate the categories, the size, and the position of the objects. Most of the MOT approach

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::f5560e6c08442a88a298065aa9a960b3
https://hal-utt.archives-ouvertes.fr/hal-02353268

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A Novel Approach for Anomaly Detection in Power Consumption Data

Autor: Hichem Snoussi, Moez Esseghir, C. Chahla, Leila Merghem

Publikováno v: ICPRAM
8th International Conference on Pattern Recognition Applications and Methods
8th International Conference on Pattern Recognition Applications and Methods, May 2019, Prague, Czech Republic. ⟨10.5220/0007361704830490⟩

International audience; Anomalies are patterns in data that do not follow the expected behaviour and they are rarely encountered. Anomaly detection has been widely used within diverse research areas such as credit card fraud detection, image processi

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::5a46920e014c4f2fa79fa905562290aa
https://doi.org/10.5220/0007361704830490

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Sparse feature extraction for model-less robust face pose estimation

Autor: Fahed Abdallah, Fadi Dornaika, C. Chahla, Hichem Snoussi

Publikováno v: 2017 Sensors Networks Smart and Emerging Technologies (SENSET).

Head pose estimation has been a focus of research in computer vision both implicitly in tasks that use full body pose estimation, and explicitly in tasks that perform face tracking and face recognition. One of the main problems in the field of face r

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::5d34739a32a53c88e67aabda9957adf5
https://doi.org/10.1109/senset.2017.8125063

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Discriminant quaternion local binary pattern embedding for person re-identification through prototype formation and color categorization

Autor: Fadi Dornaika, C. Chahla, Hichem Snoussi, Fahed Abdallah

Publikováno v: Engineering Applications of Artificial Intelligence
Engineering Applications of Artificial Intelligence, 2017, 58, pp.27-33. ⟨10.1016/j.engappai.2016.11.004⟩
Engineering Applications of Artificial Intelligence, Elsevier, 2017, 58, pp.27-33. ⟨10.1016/j.engappai.2016.11.004⟩

International audience; Re-identifying objects is one of the fundamental elements for visual surveillance, in the sense that images of the same object at different time or places should be assigned with the same label. In this work, we propose a new

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::2483e7249c1d2fb6d4e9a745f726853f
https://utt.hal.science/hal-02307929

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