المؤلفون: Oussama Mounnan, Otman Manad, Larbi Boubchir, Abdelkrim El Mouatasim, Boubaker Daachi

المصدر: Blockchain: Research and Applications, Vol 5, Iss 4, Pp 100227- (2024)

مصطلحات موضوعية: Blockchain, Cyber security, Deep anomaly detection, Deep learning, Cyber-attacks, Fraud detection, Information technology, T58.5-58.64

وصف الملف: electronic resource

Relation: http://www.sciencedirect.com/science/article/pii/S209672092400040X; https://doaj.org/toc/2666-9536

URL الوصول: https://doaj.org/article/a2b053ae3a214c768e54dcb2ba008190

المؤلفون: Leo Corman, Jason Bono, Catherine Acton, Danielle Gewurz, Evan Schulz

مصطلحات موضوعية: sparse data, semi-supervised, deep learning, autoencoder, deep anomaly detection, ensemble

Relation: https://zenodo.org/communities/2024jsmproceedings; https://doi.org/10.5281/zenodo.14009895; https://doi.org/10.5281/zenodo.14009896; oai:zenodo.org:14009896

الاتاحة: https://doi.org/10.5281/zenodo.14009896

المؤلفون: Li, Aodong

مصطلحات موضوعية: Computer science, Bayesian online learning, deep anomaly detection, distribution shifts

وصف الملف: application/pdf

URL الوصول: https://escholarship.org/uc/item/8479n8jg
https://escholarship.org/content/qt8479n8jg/qt8479n8jg.pdf

المؤلفون: Acton, Catherine, Corman, Leo, Bono, Jason, Gewurz, Danielle, Walsh, Christopher, Schulz, Evan

مصطلحات موضوعية: sparse data, unsupervised, deep learning, autoencoder, deep anomaly detection

Relation: https://zenodo.org/communities/proceedings2023; https://doi.org/10.5281/zenodo.10001050; https://doi.org/10.5281/zenodo.10001051; oai:zenodo.org:10001051

الاتاحة: https://doi.org/10.5281/zenodo.10001051

المؤلفون: Yuxiang Kang, Guo Chen, Hao Wang, Wenping Pan, Xunkai Wei

المصدر: Sensors, Vol 23, Iss 8013, p 8013 (2023)

مصطلحات موضوعية: rolling bearing, dual-input deep anomaly detection, unsupervised learning, zero fault samples, CNN, Chemical technology, TP1-1185

Relation: https://www.mdpi.com/1424-8220/23/18/8013; https://doaj.org/toc/1424-8220; https://doaj.org/article/d9111d7784674048b44ea1f02e797f86

الاتاحة: https://doi.org/10.3390/s23188013
https://doaj.org/article/d9111d7784674048b44ea1f02e797f86

المؤلفون: Eric Stefan Miele, Fabrizio Bonacina, Alessandro Corsini

المصدر: Energy and AI, Vol 8, Iss , Pp 100145- (2022)

مصطلحات موضوعية: Wind turbine, Condition monitoring, Deep anomaly detection, SCADA data, Graph Convolutional Autoencoder, Multivariate Time series, Electrical engineering. Electronics. Nuclear engineering, TK1-9971, Computer software, QA76.75-76.765

وصف الملف: electronic resource

Relation: http://www.sciencedirect.com/science/article/pii/S2666546822000076; https://doaj.org/toc/2666-5468

URL الوصول: https://doaj.org/article/24c38fb3bba94c47b4048e1cf1c518b2

المؤلفون: ZHANG, Jianpeng, XIE, Yutong, PANG, Guansong, LIAO, Zhibin, VERJANS, Johan, LI, Wenxing, SUN, Zongji, HE, Jian, LI, Yi, SHEN, Chunhua, XIA, Yong

المصدر: Research Collection School Of Computing and Information Systems

مصطلحات موضوعية: Viral pneumonia screening, deep anomaly detection, confidence prediction, chest X-ray, Artificial Intelligence and Robotics, Health Information Technology

وصف الملف: application/pdf

Relation: https://ink.library.smu.edu.sg/sis_research/7019; https://ink.library.smu.edu.sg/context/sis_research/article/8022/viewcontent/2003.12338.pdf

الاتاحة: https://ink.library.smu.edu.sg/sis_research/7019
https://doi.org/10.1109/TMI.2020.3040950
https://ink.library.smu.edu.sg/context/sis_research/article/8022/viewcontent/2003.12338.pdf

المؤلفون: Bindini, Luca, Pagani, Stefano, Bernardini, Andrea, Grossi, Benedetta, Giomi, Andrea, Frontera, Antonio, Frasconi, Paolo

المساهمون: Bindini, Luca, Pagani, Stefano, Bernardini, Andrea, Grossi, Benedetta, Giomi, Andrea, Frontera, Antonio, Frasconi, Paolo

مصطلحات موضوعية: Atrial fibrillation, Deep anomaly detection, Unsupervised learning, Artificial intelligence, Deep learning

Relation: info:eu-repo/semantics/altIdentifier/wos/WOS:001296925300001; volume:98; firstpage:1; lastpage:12; numberofpages:12; journal:BIOMEDICAL SIGNAL PROCESSING AND CONTROL; https://hdl.handle.net/11311/1272982; info:eu-repo/semantics/altIdentifier/scopus/2-s2.0-85201322586

الاتاحة: https://hdl.handle.net/11311/1272982
https://doi.org/10.1016/j.bspc.2024.106737

المؤلفون: Anuroop Gaddam, Vinodha Govender

مصطلحات موضوعية: outlier detection, Industrial Internet of Things (IIoT), Internet of Things (IoT), federated learning, equipment monitoring, resources industry, sensor anomaly, deep anomaly detection, temperature monitoring, wireless sensor network

Relation: http://hdl.handle.net/10779/DRO/DU:23431988.v2; https://figshare.com/articles/chapter/Detecting_Sensor_Faults_and_Outliers_in_Industrial_Internet_of_Things/23431988

الاتاحة: http://hdl.handle.net/10779/DRO/DU:23431988.v2
https://figshare.com/articles/chapter/Detecting_Sensor_Faults_and_Outliers_in_Industrial_Internet_of_Things/23431988

المؤلفون: Jezequel, Loic, Vu, Ngoc-Son, Beaudet, Jean, Histace, Aymeric

المساهمون: Equipes Traitement de l'Information et Systèmes (ETIS - UMR 8051), Ecole Nationale Supérieure de l'Electronique et de ses Applications (ENSEA)-Centre National de la Recherche Scientifique (CNRS)-CY Cergy Paris Université (CY), Idemia, IAPR, IEEE

المصدر: International Conference on Pattern Recognition ; https://hal.science/hal-03737352 ; International Conference on Pattern Recognition, IAPR ; IEEE, Aug 2022, Montreal, Canada

مصطلحات موضوعية: Deep Anomaly detection, Pretext tasks, Learnable tasks, [INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV], [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing, [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG]

جغرافية الموضوع: Montreal

Time: Montreal, Canada

الاتاحة: https://hal.science/hal-03737352

المؤلفون: Liu, Yi, Garg, Sahil, Nie, Jiangtian, Zhang, Yang, Xiong, Zehui, Kang, Jiawen, Hossain, M. Shamim

المساهمون: School of Computer Science and Engineering, Interdisciplinary Graduate School (IGS), Energy Research Institute @ NTU (ERI@N), Alibaba-NTU Joint Research Institute

مصطلحات موضوعية: Engineering::Computer science and engineering, Deep Anomaly Detection, Federated Learning

Relation: IEEE Internet of Things Journal; Liu, Y., Garg, S., Nie, J., Zhang, Y., Xiong, Z., Kang, J. & Hossain, M. S. (2020). Deep anomaly detection for time-series data in industrial IoT: a communication-efficient on-device federated learning approach. IEEE Internet of Things Journal, 8(8), 6348-6358. https://dx.doi.org/10.1109/JIOT.2020.3011726; https://hdl.handle.net/10356/159853; 2-s2.0-85104070584; 6348; 6358

الاتاحة: https://hdl.handle.net/10356/159853
https://doi.org/10.1109/JIOT.2020.3011726

المؤلفون: Yang Zhang, Jiawen Kang, Jiangtian Nie, Zehui Xiong, Yi Liu, M. Shamim Hossain, Sahil Garg

المساهمون: School of Computer Science and Engineering, Interdisciplinary Graduate School (IGS), Energy Research Institute @ NTU (ERI@N), Alibaba-NTU Joint Research Institute

مصطلحات موضوعية: Scheme (programming language), FOS: Computer and information sciences, Computer Science - Machine Learning, Edge device, Computer Networks and Communications, Computer science, Distributed computing, Machine Learning (stat.ML), 02 engineering and technology, Convolutional neural network, Data modeling, Machine Learning (cs.LG), Statistics - Machine Learning, 0202 electrical engineering, electronic engineering, information engineering, Overhead (computing), Time series, computer.programming_language, 020206 networking & telecommunications, Computer Science Applications, Deep Anomaly Detection, Computer Science - Distributed, Parallel, and Cluster Computing, Hardware and Architecture, Signal Processing, Computer science and engineering [Engineering], 020201 artificial intelligence & image processing, Anomaly detection, Distributed, Parallel, and Cluster Computing (cs.DC), computer, Federated Learning, Information Systems

URL الوصول: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::93f4e4087296d0917872459e946f04ab
https://hdl.handle.net/10356/159853

المؤلفون: Robinson, William

المساهمون: McNeill, Dean (Electrical and Computer Engineering) McLeod, Robert (Electrical and Computer Engineering), Shafai, Cyrus (Electrical and Computer Engineering)

مصطلحات موضوعية: Machine learning, Deep anomaly detection, Aquaculture monitoring system, aMSCRED

وصف الملف: application/pdf

Relation: http://hdl.handle.net/1993/34579

الاتاحة: http://hdl.handle.net/1993/34579

المؤلفون: Herrmann, Lars (author)

المساهمون: Santos, Bruno F. (mentor), Bieber, M.T. (graduation committee), Delft University of Technology (degree granting institution)

مصطلحات موضوعية: Real-life Satellite Telemetry Data, Anomaly Detection Metrics, Time-series Anomaly Detection, Deep anomaly detection

Relation: http://resolver.tudelft.nl/uuid:8de2b304-c70d-4171-b93c-8eda51af839d

الاتاحة: http://resolver.tudelft.nl/uuid:8de2b304-c70d-4171-b93c-8eda51af839d

المؤلفون: Munyua, John Gatara, Wambugu, Geoffrey Mariga, Njenga, Stephen Thiiru

المصدر: International Journal of Computer and Information Technology(2279-0764); Vol. 10 No. 5 (2021): September 2021 ; 2279-0764

مصطلحات موضوعية: Deep Learning, Anomaly Detection, Anomaly Detection in Videos, Intelligence Video Surveillance, Deep Anomaly Detection, Anomaly Detection in Surveillance Videos, Review of Deep Anomaly Detection, Rise of Intelligence Surveillance

وصف الملف: application/pdf

Relation: https://www.ijcit.com/index.php/ijcit/article/view/166/45; https://www.ijcit.com/index.php/ijcit/article/view/166

الاتاحة: https://www.ijcit.com/index.php/ijcit/article/view/166
https://doi.org/10.24203/ijcit.v10i5.166

المؤلفون: Copiaco, Abigail, Himeur, Yassine, Amira, Abbes, Mansoor, Wathiq, Fadli, Fodil, Atalla, Shadi

مصطلحات موضوعية: AlexNet, Building Energy Con-sumption, Deep Anomaly Detection, Time-series imaging, transfer learning

وصف الملف: application/pdf

Relation: http://dx.doi.org/10.1109/CSDE56538.2022.10089265; Copiaco, A., Himeur, Y., Amira, A., Mansoor, W., Fadli, F., & Atalla, S. (2022, December). Exploring deep time-series imaging for anomaly detection of building energy consumption. In 2022 IEEE Asia-Pacific Conference on Computer Science and Data Engineering (CSDE) (pp. 1-5). IEEE.; https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85153685644&origin=inward; http://hdl.handle.net/10576/53148; 1-5

الاتاحة: http://hdl.handle.net/10576/53148
https://doi.org/10.1109/CSDE56538.2022.10089265
https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85153685644&origin=inward

المؤلفون: Abigail, Copiaco, Himeur, Yassine, Amira, Abbes, Mansoor, Wathiq, Fadli, Fodil, Atalla, Shadi, Sohail, Shahab Saquib

مصطلحات موضوعية: Deep anomaly detection, Building energy consumption, Hyper-parameter variation, Neural network activation, Transfer learning

وصف الملف: application/pdf

Relation: http://dx.doi.org/10.1016/j.engappai.2022.105775; Copiaco, A., Himeur, Y., Amira, A., Mansoor, W., Fadli, F., Atalla, S., & Sohail, S. S. (2023). An innovative deep anomaly detection of building energy consumption using energy time-series images. Engineering Applications of Artificial Intelligence, 119, 105775.; https://www.sciencedirect.com/science/article/pii/S0952197622007655; http://hdl.handle.net/10576/53093; 119

الاتاحة: http://hdl.handle.net/10576/53093
https://doi.org/10.1016/j.engappai.2022.105775
https://www.sciencedirect.com/science/article/pii/S0952197622007655

المؤلفون: McNeill, Dean (Electrical and Computer Engineering) McLeod, Robert (Electrical and Computer Engineering), Shafai, Cyrus (Electrical and Computer Engineering), Robinson, William

مصطلحات الفهرس: Machine learning, Deep anomaly detection, Aquaculture monitoring system, aMSCRED, master thesis

URL: http://hdl.handle.net/1993/34579