Literatura científica selecionada sobre o tema "Contextual anomalies"
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Artigos de revistas sobre o assunto "Contextual anomalies"
Yu, Xiang, Hui Lu, Xianfei Yang, Ying Chen, Haifeng Song, Jianhua Li e Wei Shi. "An adaptive method based on contextual anomaly detection in Internet of Things through wireless sensor networks". International Journal of Distributed Sensor Networks 16, n.º 5 (maio de 2020): 155014772092047. http://dx.doi.org/10.1177/1550147720920478.
Texto completo da fonteFilik, Ruth. "Contextual override of pragmatic anomalies: Evidence from eye movements". Cognition 106, n.º 2 (fevereiro de 2008): 1038–46. http://dx.doi.org/10.1016/j.cognition.2007.04.006.
Texto completo da fonteNgueilbaye, Alladoumbaye, Hongzhi Wang, Daouda Ahmat Mahamat, Ibrahim A. Elgendy e Sahalu B. Junaidu. "Methods for detecting and correcting contextual data quality problems". Intelligent Data Analysis 25, n.º 4 (9 de julho de 2021): 763–87. http://dx.doi.org/10.3233/ida-205282.
Texto completo da fonteClausen, Henry, Gudmund Grov e David Aspinall. "CBAM: A Contextual Model for Network Anomaly Detection". Computers 10, n.º 6 (11 de junho de 2021): 79. http://dx.doi.org/10.3390/computers10060079.
Texto completo da fonteSeymour, Deni J. "Contextual Incongruities, Statistical Outliers, and Anomalies: Targeting Inconspicuous Occupational Events". American Antiquity 75, n.º 1 (janeiro de 2010): 158–76. http://dx.doi.org/10.7183/0002-7316.75.1.158.
Texto completo da fonteDou, Shaoyu, Kai Yang e H. Vincent Poor. "PC2A: Predicting Collective Contextual Anomalies via LSTM With Deep Generative Model". IEEE Internet of Things Journal 6, n.º 6 (dezembro de 2019): 9645–55. http://dx.doi.org/10.1109/jiot.2019.2930202.
Texto completo da fonteWei, Ji Dong, e Ge Guo. "Multi-Sensor Stockline Tracking within a Blast Furnace". Applied Mechanics and Materials 701-702 (dezembro de 2014): 522–27. http://dx.doi.org/10.4028/www.scientific.net/amm.701-702.522.
Texto completo da fonteAl-Gabalawy, Mostafa. "Detecting anomalies within Unmanned Aerial Vehicle (UAV) video based on contextual saliency". Applied Soft Computing 96 (novembro de 2020): 106715. http://dx.doi.org/10.1016/j.asoc.2020.106715.
Texto completo da fonteFrolova, E. A. "Typology of speech anomalies in the present-day advertising language". Russian language at school 84, n.º 3 (22 de maio de 2023): 68–76. http://dx.doi.org/10.30515/0131-6141-2023-84-3-68-76.
Texto completo da fonteZhao, Bo, Xiang Li, Jiayue Li, Jianwen Zou e Yifan Liu. "An Area-Context-Based Credibility Detection for Big Data in IoT". Mobile Information Systems 2020 (25 de janeiro de 2020): 1–12. http://dx.doi.org/10.1155/2020/5068731.
Texto completo da fonteTeses / dissertações sobre o assunto "Contextual anomalies"
Görnitz, Nico Verfasser], Klaus-Robert [Akademischer Betreuer] [Gutachter] [Müller, Manfred [Gutachter] Opper e Marius [Gutachter] Kloft. "One-class classification in the presence of point, collective, and contextual anomalies / Nico Görnitz ; Gutachter: Klaus-Robert Müller, Manfred Opper, Marius Kloft ; Betreuer: Klaus-Robert Müller". Berlin : Technische Universität Berlin, 2019. http://d-nb.info/1178524663/34.
Texto completo da fonteGörnitz, Nico [Verfasser], Klaus-Robert [Akademischer Betreuer] [Gutachter] Müller, Manfred [Gutachter] Opper e Marius [Gutachter] Kloft. "One-class classification in the presence of point, collective, and contextual anomalies / Nico Görnitz ; Gutachter: Klaus-Robert Müller, Manfred Opper, Marius Kloft ; Betreuer: Klaus-Robert Müller". Berlin : Technische Universität Berlin, 2019. http://d-nb.info/1178524663/34.
Texto completo da fonteWilmet, Audrey. "Détection d'anomalies dans les flots de liens : combiner les caractéristiques structurelles et temporelles". Electronic Thesis or Diss., Sorbonne université, 2019. http://www.theses.fr/2019SORUS402.
Texto completo da fonteA link stream is a set of links {(t, u, v)} in which a triplet (t, u, v) models the interaction between two entities u and v at time t. In many situations, data result from the measurement of interactions between several million of entities over time and can thus be studied through the link stream's formalism. This is the case, for instance, of phone calls, email exchanges, money transfers, contacts between individuals, IP traffic, online shopping, and many more. The goal of this thesis is the detection of sets of abnormal links in a link stream. In a first part, we design a method that constructs different contexts, a context being a set of characteristics describing the circumstances of an anomaly. These contexts allow us to find unexpected behaviors that are relevant, according to several dimensions and perspectives. In a second part, we design a method to detect anomalies in heterogeneous distributions whose behavior is constant over time, by comparing a sequence of similar heterogeneous distributions. We apply our methodological tools to temporal interactions coming from retweets of Twitter and IP traffic of MAWI group
Vasco, Daniela Oliveira Baía Soares. "Identificação de Anomalias Contextuais". Master's thesis, 2013. https://repositorio-aberto.up.pt/handle/10216/70366.
Texto completo da fonteVasco, Daniela Oliveira Baía Soares. "Identificação de Anomalias Contextuais". Dissertação, 2013. https://repositorio-aberto.up.pt/handle/10216/70366.
Texto completo da fonteCapítulos de livros sobre o assunto "Contextual anomalies"
Vaudaine, Rémi, Baptiste Jeudy e Christine Largeron. "Detection of Contextual Anomalies in Attributed Graphs". In Advances in Intelligent Data Analysis XIX, 338–49. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-74251-5_27.
Texto completo da fonteCorizzo, Roberto, Michelangelo Ceci, Gianvito Pio, Paolo Mignone e Nathalie Japkowicz. "Spatially-Aware Autoencoders for Detecting Contextual Anomalies in Geo-Distributed Data". In Discovery Science, 461–71. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-88942-5_36.
Texto completo da fonteBaldoni, Sara, Giuseppe Celozzi, Alessandro Neri, Marco Carli e Federica Battisti. "Inferring Anomaly Situation from Multiple Data Sources in Cyber Physical Systems". In Cyber-Physical Security for Critical Infrastructures Protection, 67–76. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-69781-5_5.
Texto completo da fonteKhrennikov, Andrei. "Contextual Approach to Quantum Theory". In Information Dynamics in Cognitive, Psychological, Social and Anomalous Phenomena, 153–85. Dordrecht: Springer Netherlands, 2004. http://dx.doi.org/10.1007/978-94-017-0479-3_9.
Texto completo da fonteSanthi, M., e Leya Elizabeth Sunny. "Contextual Multi-scale Region Convolutional 3D Network for Anomalous Activity Detection in Videos". In Computational Vision and Bio-Inspired Computing, 98–108. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-37218-7_12.
Texto completo da fonteZaverucha, Gerson. "A prioritized Contextual Default Logic: Curing anomalous extensions with a simple abnormality default theory". In KI-94: Advances in Artificial Intelligence, 260–71. Berlin, Heidelberg: Springer Berlin Heidelberg, 1994. http://dx.doi.org/10.1007/3-540-58467-6_23.
Texto completo da fonteWolff, Christian. "Composing Aseneth". In When Aseneth Met Joseph, 19–49. Oxford University PressNew York, NY, 1998. http://dx.doi.org/10.1093/oso/9780195114751.003.0003.
Texto completo da fonteZadeh, Esmaeil, Stephen Amstutz, James Collins, Craig Ingham, Marian Gheorghe e Savas Konur. "Automated Contextual Anomaly Detection for Network Interface Bandwidth Utilisation: A Case Study in Network Capacity Management". In Proceedings of CECNet 2021. IOS Press, 2021. http://dx.doi.org/10.3233/faia210459.
Texto completo da fonteDhibar, Kunal, e Prasenjit Maji. "Future Outlier Detection Algorithm for Smarter Industry Application Using ML and AI". In Advances in Systems Analysis, Software Engineering, and High Performance Computing, 152–66. IGI Global, 2023. http://dx.doi.org/10.4018/978-1-6684-8785-3.ch008.
Texto completo da fontePalakurti, Naga Ramesh. "Challenges and Future Directions in Anomaly Detection". In Advances in Systems Analysis, Software Engineering, and High Performance Computing, 269–84. IGI Global, 2024. http://dx.doi.org/10.4018/979-8-3693-2909-2.ch020.
Texto completo da fonteTrabalhos de conferências sobre o assunto "Contextual anomalies"
Vasco, Daniela, Pedro Pereira Rodrigues e Joao Gama. "Contextual anomalies in medical data". In 2013 IEEE 26th International Symposium on Computer-Based Medical Systems (CBMS). IEEE, 2013. http://dx.doi.org/10.1109/cbms.2013.6627869.
Texto completo da fontePrado-Romero, Mario Alfonso, e Andres Gago-Alonso. "Detecting contextual collective anomalies at a Glance". In 2016 23rd International Conference on Pattern Recognition (ICPR). IEEE, 2016. http://dx.doi.org/10.1109/icpr.2016.7900017.
Texto completo da fonteDimopoulos, Giorgos, Pere Barlet-Ros, Constantine Dovrolis e Ilias Leontiadis. "Detecting network performance anomalies with contextual anomaly detection". In 2017 IEEE International Workshop on Measurements & Networking (M&N). IEEE, 2017. http://dx.doi.org/10.1109/iwmn.2017.8078404.
Texto completo da fonteLatif, Hamid, José Suárez-Varela, Albert Cabellos-Aparicio e Pere Barlet-Ros. "Detecting Contextual Network Anomalies with Graph Neural Networks". In CoNEXT 2023: The 19th International Conference on emerging Networking EXperiments and Technologies. New York, NY, USA: ACM, 2023. http://dx.doi.org/10.1145/3630049.3630171.
Texto completo da fonteCarmona, Chris U., François-Xavier Aubet, Valentin Flunkert e Jan Gasthaus. "Neural Contextual Anomaly Detection for Time Series". In Thirty-First International Joint Conference on Artificial Intelligence {IJCAI-22}. California: International Joint Conferences on Artificial Intelligence Organization, 2022. http://dx.doi.org/10.24963/ijcai.2022/394.
Texto completo da fonteFan Jiang, Ying Wu e Aggelos K. Katsaggelos. "Detecting contextual anomalies of crowd motion in surveillance video". In 2009 16th IEEE International Conference on Image Processing ICIP 2009. IEEE, 2009. http://dx.doi.org/10.1109/icip.2009.5414535.
Texto completo da fonteSharma, Shivam, Mirdul Swarup, Tanush Mahajan e Zeel Dilipkumar Patel. "Detecting anomalies, contradictions, and contextual analysis through NLP in text". In 2022 3rd International Conference on Issues and Challenges in Intelligent Computing Techniques (ICICT). IEEE, 2022. http://dx.doi.org/10.1109/icict55121.2022.10064560.
Texto completo da fonteJinadasa, Minura, Suranga Nisiwasala, Suthan Senthinathan, Shiromi Arunatileka e Damitha Sandaruwan. "Framework for detection of anomalies in mass moving objects by non-technical users utilizing contextual & spatio-temporal data". In 2017 Seventeenth International Conference on Advances in ICT for Emerging Regions (ICTer). IEEE, 2017. http://dx.doi.org/10.1109/icter.2017.8257798.
Texto completo da fonteSchaefer, S., e O. Revheim. "Building Trust in AI/ML Solutions: Key Factors for Successful Adoption in Drilling Optimization and Hazard Prevention". In SPE Norway Subsurface Conference. SPE, 2024. http://dx.doi.org/10.2118/218455-ms.
Texto completo da fonteSun, Kewen, Chao Mu, Tao Yu e Graeme Paterson. "An Innovative Workflow for Real-Time Torque and Drag Monitoring". In SPE/IADC International Drilling Conference and Exhibition. SPE, 2023. http://dx.doi.org/10.2118/212535-ms.
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