Zobrazeno 1 - 10
of 30
pro vyhledávání: '"Nurali Virani"'
Publikováno v:
Annual Conference of the PHM Society. 14
Free-form text-based maintenance and service records related to industrial assets capture the observations and actions of service engineers and are a crucial resource for assessing system-level asset health. To facilitate tracking of historical asset
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::aa8862f75ac5226ba47ececd297000ef
https://doi.org/10.36001/phmconf.2022.v14i1.3159
https://doi.org/10.36001/phmconf.2022.v14i1.3159
Publikováno v:
AAAI
With the advent of Deep Learning, the field of machine learning (ML) has surpassed human-level performance on diverse classification tasks. At the same time, there is a stark need to characterize and quantify reliability of a model's prediction on in
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::c0b051a8a7ae346c3ea03df7e5428ea2
https://doi.org/10.1609/aaai.v34i04.6071
https://doi.org/10.1609/aaai.v34i04.6071
Publikováno v:
Handbook of Dynamic Data Driven Applications Systems ISBN: 9783030745677
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::d8211eeb1314e798e347970e801e526d
https://doi.org/10.1007/978-3-030-74568-4_6
https://doi.org/10.1007/978-3-030-74568-4_6
Publikováno v:
Handbook of Dynamic Data Driven Applications Systems ISBN: 9783030745677
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::2884c2a155afdc9a18ef59f238153337
https://doi.org/10.1007/978-3-030-74568-4_25
https://doi.org/10.1007/978-3-030-74568-4_25
Publikováno v:
Engineering Applications of Artificial Intelligence. 81:234-246
This paper addresses sequential hypothesis testing for Markov models of time-series data by using the concepts of symbolic dynamics. These models are inferred by discretizing the measurement space of a dynamical system, where the system dynamics are
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::4f5e1e89d7a29f5799f7f676a11a0ad8
https://doi.org/10.1016/j.engappai.2019.02.015
https://doi.org/10.1016/j.engappai.2019.02.015
Autor:
Steven Gray, Shiraj Sen, Mohammed Yousefhussien, Katelyn Angeliu, Nurali Virani, Nicholas Abate, Zhaoyuan Yang, Brandon Stephen Good, Yewteck Tan
Publikováno v:
Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications III.
To function at the same operational tempo as human teammates on the battlefield in a robust and resilient manner, autonomous systems must assess and manage risk as it pertains to vehicle navigation. Risk comes in multiple forms, associated with both
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::c9bde5be6fd7413bc216853e192334e2
https://doi.org/10.1117/12.2586120
https://doi.org/10.1117/12.2586120
Publikováno v:
ICIP
Machine learning models provide statistically impressive results which might be individually unreliable. To provide reliability, we propose an Epistemic Classifier (EC) that can provide justification of its belief using support from the training data
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::0f984e08d7240c2e59c1190c042047b2
https://doi.org/10.1109/icip40778.2020.9190836
https://doi.org/10.1109/icip40778.2020.9190836
Publikováno v:
Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications II.
In machine learning, backdoor or trojan attacks during model training can cause the targeted model to deceptively learn to misclassify in the presence of specific triggers. This mechanism of deception enables the attacker to exercise full control on
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::41333677365396a8ad2949aa20caa17d
https://doi.org/10.1117/12.2558255
https://doi.org/10.1117/12.2558255
Autor:
Sandeep Gogineni, Chitra Sivanandam, Chung-Sheng Li, Cory Kays, José E. Moreira, Nurali Virani, Daniel Y. Abramovitch
Publikováno v:
Lecture Notes in Computer Science ISBN: 9783030617240
DDDAS
DDDAS
This panel convenes representatives from several industries in the information technology, aerospace, power, manufacturing and finance sectors, who will address how advances in modeling and prediction methods, and decision support for complex systems
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::2d2d8f9ae8e4659e16b6cf2bd67d6afe
https://doi.org/10.1007/978-3-030-61725-7_41
https://doi.org/10.1007/978-3-030-61725-7_41
Publikováno v:
Advances in Artificial Intelligence ISBN: 9783030473570
Canadian Conference on AI
Canadian Conference on AI
We present a new methodology for assessing when data-based predictive models can be trusted. Particularly, we propose to learn a model from experimentation that determines, for a given labeled data set and a learning technique, when the model generat
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::a2126f77627cd8a0a48924fd180a8c96
https://doi.org/10.1007/978-3-030-47358-7_10
https://doi.org/10.1007/978-3-030-47358-7_10