Artigos de revistas sobre o tema "Unsupervised anomaly detection"
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倪, 一鸣, e 松灿 陈. "Continual unsupervised anomaly detection". SCIENTIA SINICA Informationis 52, n.º 1 (1 de janeiro de 2022): 75. http://dx.doi.org/10.1360/ssi-2021-0192.
Texto completo da fonteShi, Chengming, Bo Luo, Hongqi Li, Bin Li, Xinyong Mao e Fangyu Peng. "Anomaly Detection via Unsupervised Learning for Tool Breakage Monitoring". International Journal of Machine Learning and Computing 6, n.º 5 (outubro de 2016): 256–59. http://dx.doi.org/10.18178/ijmlc.2016.6.5.607.
Texto completo da fonteFarzad, Amir, e T. Aaron Gulliver. "Unsupervised log message anomaly detection". ICT Express 6, n.º 3 (setembro de 2020): 229–37. http://dx.doi.org/10.1016/j.icte.2020.06.003.
Texto completo da fonteGoernitz, N., M. Kloft, K. Rieck e U. Brefeld. "Toward Supervised Anomaly Detection". Journal of Artificial Intelligence Research 46 (20 de fevereiro de 2013): 235–62. http://dx.doi.org/10.1613/jair.3623.
Texto completo da fonteAlmalawi, Abdulmohsen, Adil Fahad, Zahir Tari, Asif Irshad Khan, Nouf Alzahrani, Sheikh Tahir Bakhsh, Madini O. Alassafi, Abdulrahman Alshdadi e Sana Qaiyum. "Add-On Anomaly Threshold Technique for Improving Unsupervised Intrusion Detection on SCADA Data". Electronics 9, n.º 6 (18 de junho de 2020): 1017. http://dx.doi.org/10.3390/electronics9061017.
Texto completo da fonteTian, Yu, Haihua Liao, Jing Xu, Ya Wang, Shuai Yuan e Naijin Liu. "Unsupervised Spectrum Anomaly Detection Method for Unauthorized Bands". Space: Science & Technology 2022 (21 de fevereiro de 2022): 1–10. http://dx.doi.org/10.34133/2022/9865016.
Texto completo da fonteLok, Lai Kai, Vazeerudeen Abdul Hameed e Muhammad Ehsan Rana. "Hybrid machine learning approach for anomaly detection". Indonesian Journal of Electrical Engineering and Computer Science 27, n.º 2 (1 de agosto de 2022): 1016. http://dx.doi.org/10.11591/ijeecs.v27.i2.pp1016-1024.
Texto completo da fonteGoldstein, Markus. "Special Issue on Unsupervised Anomaly Detection". Applied Sciences 13, n.º 10 (11 de maio de 2023): 5916. http://dx.doi.org/10.3390/app13105916.
Texto completo da fonteZhou, Wei, Yuan Gao, Jianhang Ji, Shicheng Li e Yugen Yi. "Unsupervised Anomaly Detection for Glaucoma Diagnosis". Wireless Communications and Mobile Computing 2021 (1 de outubro de 2021): 1–14. http://dx.doi.org/10.1155/2021/5978495.
Texto completo da fonteChung, Hwehee, Jongho Park, Jongsoo Keum, Hongdo Ki e Seokho Kang. "Unsupervised Anomaly Detection Using Style Distillation". IEEE Access 8 (2020): 221494–502. http://dx.doi.org/10.1109/access.2020.3043473.
Texto completo da fonteVincent, Vercruyssen, Meert Wannes e Davis Jesse. "Transfer Learning for Anomaly Detection through Localized and Unsupervised Instance Selection". Proceedings of the AAAI Conference on Artificial Intelligence 34, n.º 04 (3 de abril de 2020): 6054–61. http://dx.doi.org/10.1609/aaai.v34i04.6068.
Texto completo da fonteAmarbayasgalan, Tsatsral, Van Huy Pham, Nipon Theera-Umpon e Keun Ho Ryu. "Unsupervised Anomaly Detection Approach for Time-Series in Multi-Domains Using Deep Reconstruction Error". Symmetry 12, n.º 8 (29 de julho de 2020): 1251. http://dx.doi.org/10.3390/sym12081251.
Texto completo da fonteGuo, Jiahao, Xiaohuo Yu e Lu Wang. "Unsupervised Anomaly Detection and Segmentation on Dirty Datasets". Future Internet 14, n.º 3 (13 de março de 2022): 86. http://dx.doi.org/10.3390/fi14030086.
Texto completo da fonteWen-Jen Ho, Wen-Jen Ho, Hsin-Yuan Hsieh Wen-Jen Ho e Chia-Wei Tsai Hsin-Yuan Hsieh. "Anomaly Detection Model of Time Segment Power Usage Behavior Using Unsupervised Learning". 網際網路技術學刊 25, n.º 3 (maio de 2024): 455–63. http://dx.doi.org/10.53106/160792642024052503011.
Texto completo da fonteSong, Yide. "Weakly-Supervised and Unsupervised Video Anomaly Detection". Highlights in Science, Engineering and Technology 12 (26 de agosto de 2022): 160–70. http://dx.doi.org/10.54097/hset.v12i.1444.
Texto completo da fonteZHOU, JUNLIN, ALEKSANDAR LAZAREVIC, KUO-WEI HSU, JAIDEEP SRIVASTAVA, YAN FU e YUE WU. "UNSUPERVISED LEARNING BASED DISTRIBUTED DETECTION OF GLOBAL ANOMALIES". International Journal of Information Technology & Decision Making 09, n.º 06 (novembro de 2010): 935–57. http://dx.doi.org/10.1142/s0219622010004172.
Texto completo da fonteZhang, Shen Qi, Wei Yuan, Ran Yi e Li Chen. "DC operating circuit anomaly detection based on node voltage unsupervised time series". Journal of Physics: Conference Series 2474, n.º 1 (1 de abril de 2023): 012030. http://dx.doi.org/10.1088/1742-6596/2474/1/012030.
Texto completo da fonteYang, Zhengqiang, Junwei Tian e Ning Li. "Flow Graph Anomaly Detection Based on Unsupervised Learning". Mobile Information Systems 2022 (31 de março de 2022): 1–9. http://dx.doi.org/10.1155/2022/4194714.
Texto completo da fonteLiu, Jiaqi, Kai Wu, Qiang Nie, Ying Chen, Bin-Bin Gao, Yong Liu, Jinbao Wang, Chengjie Wang e Feng Zheng. "Unsupervised Continual Anomaly Detection with Contrastively-Learned Prompt". Proceedings of the AAAI Conference on Artificial Intelligence 38, n.º 4 (24 de março de 2024): 3639–47. http://dx.doi.org/10.1609/aaai.v38i4.28153.
Texto completo da fonteNakao, Takahiro, Shouhei Hanaoka, Yukihiro Nomura, Masaki Murata, Tomomi Takenaga, Soichiro Miki, Takeyuki Watadani, Takeharu Yoshikawa, Naoto Hayashi e Osamu Abe. "Unsupervised Deep Anomaly Detection in Chest Radiographs". Journal of Digital Imaging 34, n.º 2 (8 de fevereiro de 2021): 418–27. http://dx.doi.org/10.1007/s10278-020-00413-2.
Texto completo da fonteLiu, Boyang, Pang-Ning Tan e Jiayu Zhou. "Unsupervised Anomaly Detection by Robust Density Estimation". Proceedings of the AAAI Conference on Artificial Intelligence 36, n.º 4 (28 de junho de 2022): 4101–8. http://dx.doi.org/10.1609/aaai.v36i4.20328.
Texto completo da fonteLópez-Vizcaíno, Manuel, Carlos Dafonte, Francisco Nóvoa, Daniel Garabato e M. Álvarez. "Network Data Unsupervised Clustering to Anomaly Detection". Proceedings 2, n.º 18 (17 de setembro de 2018): 1173. http://dx.doi.org/10.3390/proceedings2181173.
Texto completo da fonteLi, H., A. Achim e D. Bull. "Unsupervised video anomaly detection using feature clustering". IET Signal Processing 6, n.º 5 (2012): 521. http://dx.doi.org/10.1049/iet-spr.2011.0074.
Texto completo da fonteKhan, Samir, Chun Fui Liew, Takehisa Yairi e Richard McWilliam. "Unsupervised anomaly detection in unmanned aerial vehicles". Applied Soft Computing 83 (outubro de 2019): 105650. http://dx.doi.org/10.1016/j.asoc.2019.105650.
Texto completo da fonteOlson, C. C., K. P. Judd e J. M. Nichols. "Manifold learning techniques for unsupervised anomaly detection". Expert Systems with Applications 91 (janeiro de 2018): 374–85. http://dx.doi.org/10.1016/j.eswa.2017.08.005.
Texto completo da fonteErgen, Tolga, e Suleyman Serdar Kozat. "Unsupervised Anomaly Detection With LSTM Neural Networks". IEEE Transactions on Neural Networks and Learning Systems 31, n.º 8 (agosto de 2020): 3127–41. http://dx.doi.org/10.1109/tnnls.2019.2935975.
Texto completo da fonteWang, Pei, Wei Zhai e Yang Cao. "Robustness Benchmark for Unsupervised Anomaly Detection Models". JUSTC 53 (2023): 1. http://dx.doi.org/10.52396/justc-2022-0165.
Texto completo da fonteXu, Haohao, Shuchang Xu e Wenzhen Yang. "Unsupervised industrial anomaly detection with diffusion models". Journal of Visual Communication and Image Representation 97 (dezembro de 2023): 103983. http://dx.doi.org/10.1016/j.jvcir.2023.103983.
Texto completo da fonteBulut, Okan, Guher Gorgun e Surina He. "Unsupervised Anomaly Detection in Sequential Process Data". Zeitschrift für Psychologie 232, n.º 2 (abril de 2024): 74–94. http://dx.doi.org/10.1027/2151-2604/a000558.
Texto completo da fonteDai, Songmin, Yifan Wu, Xiaoqiang Li e Xiangyang Xue. "Generating and Reweighting Dense Contrastive Patterns for Unsupervised Anomaly Detection". Proceedings of the AAAI Conference on Artificial Intelligence 38, n.º 2 (24 de março de 2024): 1454–62. http://dx.doi.org/10.1609/aaai.v38i2.27910.
Texto completo da fonteHu, Jingtao, En Zhu, Siqi Wang, Xinwang Liu, Xifeng Guo e Jianping Yin. "An Efficient and Robust Unsupervised Anomaly Detection Method Using Ensemble Random Projection in Surveillance Videos". Sensors 19, n.º 19 (24 de setembro de 2019): 4145. http://dx.doi.org/10.3390/s19194145.
Texto completo da fonteHang, Feilu, Wei Guo, Hexiong Chen, Linjiang Xie, Xiaoyu Bai e Yao Liu. "Network Intrusion Anomaly Detection Model Based on Multiclassifier Fusion Technology". Mobile Information Systems 2023 (8 de abril de 2023): 1–11. http://dx.doi.org/10.1155/2023/1594622.
Texto completo da fonteShao, Yingzhao, Yunsong Li, Li Li, Yuanle Wang, Yuchen Yang, Yueli Ding, Mingming Zhang, Yang Liu e Xiangqiang Gao. "RANet: Relationship Attention for Hyperspectral Anomaly Detection". Remote Sensing 15, n.º 23 (30 de novembro de 2023): 5570. http://dx.doi.org/10.3390/rs15235570.
Texto completo da fonteRashid, A. N. M. Bazlur, Mohiuddin Ahmed, Leslie F. Sikos e Paul Haskell-Dowland. "Anomaly Detection in Cybersecurity Datasets via Cooperative Co-evolution-based Feature Selection". ACM Transactions on Management Information Systems 13, n.º 3 (30 de setembro de 2022): 1–39. http://dx.doi.org/10.1145/3495165.
Texto completo da fonteKlarák, Jaromír, Robert Andok, Peter Malík, Ivan Kuric, Mário Ritomský, Ivana Klačková e Hung-Yin Tsai. "From Anomaly Detection to Defect Classification". Sensors 24, n.º 2 (10 de janeiro de 2024): 429. http://dx.doi.org/10.3390/s24020429.
Texto completo da fonteQu, YanZe, HaiLong Ma e YiMing Jiang. "CRND: An Unsupervised Learning Method to Detect Network Anomaly". Security and Communication Networks 2022 (28 de outubro de 2022): 1–9. http://dx.doi.org/10.1155/2022/9509417.
Texto completo da fonteLiu, Wenqiang, Li Yan, Ningning Ma, Gaozhou Wang, Xiaolong Ma, Peishun Liu e Ruichun Tang. "Unsupervised Deep Anomaly Detection for Industrial Multivariate Time Series Data". Applied Sciences 14, n.º 2 (16 de janeiro de 2024): 774. http://dx.doi.org/10.3390/app14020774.
Texto completo da fonteKatser, Iurii, Viacheslav Kozitsin, Victor Lobachev e Ivan Maksimov. "Unsupervised Offline Changepoint Detection Ensembles". Applied Sciences 11, n.º 9 (9 de maio de 2021): 4280. http://dx.doi.org/10.3390/app11094280.
Texto completo da fonteZoppi, Tommaso, Andrea Ceccarelli, Tommaso Capecchi e Andrea Bondavalli. "Unsupervised Anomaly Detectors to Detect Intrusions in the Current Threat Landscape". ACM/IMS Transactions on Data Science 2, n.º 2 (2 de abril de 2021): 1–26. http://dx.doi.org/10.1145/3441140.
Texto completo da fonteDu, Yan, Yuanyuan Huang, Guogen Wan e Peilin He. "Deep Learning-Based Cyber–Physical Feature Fusion for Anomaly Detection in Industrial Control Systems". Mathematics 10, n.º 22 (20 de novembro de 2022): 4373. http://dx.doi.org/10.3390/math10224373.
Texto completo da fonteApostol, Ioana, Marius Preda, Constantin Nila e Ion Bica. "IoT Botnet Anomaly Detection Using Unsupervised Deep Learning". Electronics 10, n.º 16 (4 de agosto de 2021): 1876. http://dx.doi.org/10.3390/electronics10161876.
Texto completo da fonteArjunan, Tamilselvan. "A Comparative Study of Deep Neural Networks and Support Vector Machines for Unsupervised Anomaly Detection in Cloud Computing Environments". International Journal for Research in Applied Science and Engineering Technology 12, n.º 2 (29 de fevereiro de 2024): 983–90. http://dx.doi.org/10.22214/ijraset.2024.58496.
Texto completo da fonteLe, Duc C., e Nur Zincir-Heywood. "Anomaly Detection for Insider Threats Using Unsupervised Ensembles". IEEE Transactions on Network and Service Management 18, n.º 2 (junho de 2021): 1152–64. http://dx.doi.org/10.1109/tnsm.2021.3071928.
Texto completo da fonteSpiekermann, Daniel, e Jörg Keller. "Unsupervised packet-based anomaly detection in virtual networks". Computer Networks 192 (junho de 2021): 108017. http://dx.doi.org/10.1016/j.comnet.2021.108017.
Texto completo da fonteAnitha Kumari, K., Avinash Sharma, R. Barani Priyanga e A. Kevin Paul. "ENERGY DATA ANOMALY DETECTION USING UNSUPERVISED LEARNING TECHNIQUES". Advances in Mathematics: Scientific Journal 9, n.º 9 (25 de agosto de 2020): 6687–98. http://dx.doi.org/10.37418/amsj.9.9.26.
Texto completo da fonteJeong, Woodon, Mohammed S. Almubarak e Constantinos Tsingas. "Seismic erratic noise attenuation using unsupervised anomaly detection". Geophysical Prospecting 69, n.º 7 (11 de junho de 2021): 1473–86. http://dx.doi.org/10.1111/1365-2478.13123.
Texto completo da fonteWang, Jin, Changqing Zhao, Shiming He, Yu Gu, Osama Alfarraj e Ahed Abugabah. "LogUAD: Log Unsupervised Anomaly Detection Based on Word2Vec". Computer Systems Science and Engineering 41, n.º 3 (2022): 1207–22. http://dx.doi.org/10.32604/csse.2022.022365.
Texto completo da fonteKandanaarachchi, Sevvandi. "Unsupervised anomaly detection ensembles using item response theory". Information Sciences 587 (março de 2022): 142–63. http://dx.doi.org/10.1016/j.ins.2021.12.042.
Texto completo da fonteWang, Zhipeng, Chunping Hou, Bangbang Ge, Yang Liu, Zhicheng Dong e Zhiqiang Wu. "Unsupervised anomaly detection via dual transformation‐aware embeddings". IET Image Processing 16, n.º 6 (8 de fevereiro de 2022): 1657–68. http://dx.doi.org/10.1049/ipr2.12438.
Texto completo da fonteSon, Jonghwan, Chayoung Kim e Minjoong Jeong. "Unsupervised Learning for Anomaly Detection of Electric Motors". International Journal of Precision Engineering and Manufacturing 23, n.º 4 (10 de março de 2022): 421–27. http://dx.doi.org/10.1007/s12541-022-00635-0.
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