Auswahl der wissenschaftlichen Literatur zum Thema „Contextual anomalies“
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Zeitschriftenartikel zum Thema "Contextual anomalies"
Yu, Xiang, Hui Lu, Xianfei Yang, Ying Chen, Haifeng Song, Jianhua Li und 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, Nr. 5 (Mai 2020): 155014772092047. http://dx.doi.org/10.1177/1550147720920478.
Der volle Inhalt der QuelleFilik, Ruth. „Contextual override of pragmatic anomalies: Evidence from eye movements“. Cognition 106, Nr. 2 (Februar 2008): 1038–46. http://dx.doi.org/10.1016/j.cognition.2007.04.006.
Der volle Inhalt der QuelleNgueilbaye, Alladoumbaye, Hongzhi Wang, Daouda Ahmat Mahamat, Ibrahim A. Elgendy und Sahalu B. Junaidu. „Methods for detecting and correcting contextual data quality problems“. Intelligent Data Analysis 25, Nr. 4 (09.07.2021): 763–87. http://dx.doi.org/10.3233/ida-205282.
Der volle Inhalt der QuelleClausen, Henry, Gudmund Grov und David Aspinall. „CBAM: A Contextual Model for Network Anomaly Detection“. Computers 10, Nr. 6 (11.06.2021): 79. http://dx.doi.org/10.3390/computers10060079.
Der volle Inhalt der QuelleSeymour, Deni J. „Contextual Incongruities, Statistical Outliers, and Anomalies: Targeting Inconspicuous Occupational Events“. American Antiquity 75, Nr. 1 (Januar 2010): 158–76. http://dx.doi.org/10.7183/0002-7316.75.1.158.
Der volle Inhalt der QuelleDou, Shaoyu, Kai Yang und H. Vincent Poor. „PC2A: Predicting Collective Contextual Anomalies via LSTM With Deep Generative Model“. IEEE Internet of Things Journal 6, Nr. 6 (Dezember 2019): 9645–55. http://dx.doi.org/10.1109/jiot.2019.2930202.
Der volle Inhalt der QuelleWei, Ji Dong, und Ge Guo. „Multi-Sensor Stockline Tracking within a Blast Furnace“. Applied Mechanics and Materials 701-702 (Dezember 2014): 522–27. http://dx.doi.org/10.4028/www.scientific.net/amm.701-702.522.
Der volle Inhalt der QuelleAl-Gabalawy, Mostafa. „Detecting anomalies within Unmanned Aerial Vehicle (UAV) video based on contextual saliency“. Applied Soft Computing 96 (November 2020): 106715. http://dx.doi.org/10.1016/j.asoc.2020.106715.
Der volle Inhalt der QuelleFrolova, E. A. „Typology of speech anomalies in the present-day advertising language“. Russian language at school 84, Nr. 3 (22.05.2023): 68–76. http://dx.doi.org/10.30515/0131-6141-2023-84-3-68-76.
Der volle Inhalt der QuelleZhao, Bo, Xiang Li, Jiayue Li, Jianwen Zou und Yifan Liu. „An Area-Context-Based Credibility Detection for Big Data in IoT“. Mobile Information Systems 2020 (25.01.2020): 1–12. http://dx.doi.org/10.1155/2020/5068731.
Der volle Inhalt der QuelleDissertationen zum Thema "Contextual anomalies"
Görnitz, Nico Verfasser], Klaus-Robert [Akademischer Betreuer] [Gutachter] [Müller, Manfred [Gutachter] Opper und 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.
Der volle Inhalt der QuelleGörnitz, Nico [Verfasser], Klaus-Robert [Akademischer Betreuer] [Gutachter] Müller, Manfred [Gutachter] Opper und 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.
Der volle Inhalt der QuelleWilmet, 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.
Der volle Inhalt der QuelleA 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.
Der volle Inhalt der QuelleVasco, Daniela Oliveira Baía Soares. „Identificação de Anomalias Contextuais“. Dissertação, 2013. https://repositorio-aberto.up.pt/handle/10216/70366.
Der volle Inhalt der QuelleBuchteile zum Thema "Contextual anomalies"
Vaudaine, Rémi, Baptiste Jeudy und 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.
Der volle Inhalt der QuelleCorizzo, Roberto, Michelangelo Ceci, Gianvito Pio, Paolo Mignone und 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.
Der volle Inhalt der QuelleBaldoni, Sara, Giuseppe Celozzi, Alessandro Neri, Marco Carli und 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.
Der volle Inhalt der QuelleKhrennikov, 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.
Der volle Inhalt der QuelleSanthi, M., und 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.
Der volle Inhalt der QuelleZaverucha, 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.
Der volle Inhalt der QuelleWolff, 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.
Der volle Inhalt der QuelleZadeh, Esmaeil, Stephen Amstutz, James Collins, Craig Ingham, Marian Gheorghe und 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.
Der volle Inhalt der QuelleDhibar, Kunal, und 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.
Der volle Inhalt der QuellePalakurti, 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.
Der volle Inhalt der QuelleKonferenzberichte zum Thema "Contextual anomalies"
Vasco, Daniela, Pedro Pereira Rodrigues und 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.
Der volle Inhalt der QuellePrado-Romero, Mario Alfonso, und 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.
Der volle Inhalt der QuelleDimopoulos, Giorgos, Pere Barlet-Ros, Constantine Dovrolis und 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.
Der volle Inhalt der QuelleLatif, Hamid, José Suárez-Varela, Albert Cabellos-Aparicio und 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.
Der volle Inhalt der QuelleCarmona, Chris U., François-Xavier Aubet, Valentin Flunkert und 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.
Der volle Inhalt der QuelleFan Jiang, Ying Wu und 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.
Der volle Inhalt der QuelleSharma, Shivam, Mirdul Swarup, Tanush Mahajan und 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.
Der volle Inhalt der QuelleJinadasa, Minura, Suranga Nisiwasala, Suthan Senthinathan, Shiromi Arunatileka und 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.
Der volle Inhalt der QuelleSchaefer, S., und 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.
Der volle Inhalt der QuelleSun, Kewen, Chao Mu, Tao Yu und 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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