Zobrazeno 1 - 10
of 53
pro vyhledávání: '"Iluju Kiringa"'
Publikováno v:
Remote Sensing, Vol 16, Iss 4, p 683 (2024)
Food demand is expected to rise significantly by 2050 due to the increase in population; additionally, receding water levels, climate change, and a decrease in the amount of available arable land will threaten food production. To address these challe
Externí odkaz:
https://doaj.org/article/be04c5611f4746268052b7e7c7470033
Publikováno v:
ACM Transactions on Internet of Things. 3:1-36
In recent years, big data produced by the Internet of Things has enabled new kinds of useful applications. One such application is monitoring a fleet of vehicles in real time to predict their remaining useful life. The consensus self-organized models
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::50d4921e2210e91889a85b012e6fffbc
https://doi.org/10.1145/3530991
https://doi.org/10.1145/3530991
Publikováno v:
2023 IEEE 2nd International Conference on AI in Cybersecurity (ICAIC).
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::47e84a15b7cef30de4102bb83c7658bc
https://doi.org/10.1109/icaic57335.2023.10044123
https://doi.org/10.1109/icaic57335.2023.10044123
Publikováno v:
2022 IEEE International Conference on Data Mining Workshops (ICDMW).
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::87ab1a4799213565bdbb753c02fcfa65
https://doi.org/10.1109/icdmw58026.2022.00137
https://doi.org/10.1109/icdmw58026.2022.00137
Publikováno v:
2022 IEEE International Conference on Cyber Security and Resilience (CSR).
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::0105c6fcd3a251bf7a01a72c86486faf
https://doi.org/10.1109/csr54599.2022.9850310
https://doi.org/10.1109/csr54599.2022.9850310
Publikováno v:
Proceedings of the Canadian Conference on Artificial Intelligence.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::6c4c2d0a3608e0dfe038583daf7e3b5f
https://doi.org/10.21428/594757db.daad1be1
https://doi.org/10.21428/594757db.daad1be1
Publikováno v:
Information Sciences. 496:572-591
The Growing Hierarchical Self-Organizing Map (GHSOM) algorithm has shown its potential for performing several tasks such as exploratory analysis, anomaly detection and forecasting on a variety of domains including the financial and cyber-security dom
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::b5e6ba97706b63265bd5c1128806f6de
https://doi.org/10.1016/j.ins.2018.12.007
https://doi.org/10.1016/j.ins.2018.12.007
Publikováno v:
ANT/EDI40
In recent years, the Internet of Things (IoT) and big data have been hot topics. With all this data being produced, new applications such as predictive maintenance are possible. Consensus self-organized models approach (COSMO) is an example of a pred
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::74de911a49a8897ce76dd36ac55f1c89
https://doi.org/10.1016/j.procs.2019.04.184
https://doi.org/10.1016/j.procs.2019.04.184
Publikováno v:
IJCNN
Identifying anomalous samples from highly complex and unstructured data is a crucial but challenging task in a variety of intelligent systems. In this paper, we present a novel deep anomaly detection framework named AnoDM (standing for Anomaly detect
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::47d23c0bb57ba067bcacd0ac8eb475c7
https://doi.org/10.1109/ijcnn48605.2020.9207046
https://doi.org/10.1109/ijcnn48605.2020.9207046
Publikováno v:
Advances in Artificial Intelligence ISBN: 9783030473570
Canadian Conference on AI
Canadian Conference on AI
In this paper, we develop and explore deep anomaly detection techniques based on capsule network (named AnoCapsNet) for image data. Being able to encode intrinsic spatial relationship between parts and a whole, CapsNet has been applied as both a clas
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::9dca533412371fccda475fb9466cba27
https://doi.org/10.1007/978-3-030-47358-7_39
https://doi.org/10.1007/978-3-030-47358-7_39
