Zobrazeno 1 - 10
of 791
pro vyhledávání: '"Smith, Michael R"'
Autor:
Field Jr., Richard V.1 (AUTHOR) msmith4@sandia.gov, Smith, Michael R.1 (AUTHOR) jbingra@sandia.gov, Wuest, Ellery J.2 (AUTHOR) ellerywu@nmsu.edu, Ingram, Joe B.1 (AUTHOR)
Publikováno v:
Mathematics (2227-7390). Nov2024, Vol. 12 Issue 21, p3392. 25p.
Publikováno v:
Proc. SPIE 11413, Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications II, 114131W (18 May 2020)
Many environments currently employ machine learning models for data processing and analytics that were built using a limited number of training data points. Once deployed, the models are exposed to significant amounts of previously-unseen data, not a
Externí odkaz:
http://arxiv.org/abs/2005.09787
Autor:
Smith, Michael R., Johnson, Nicholas T., Ingram, Joe B., Carbajal, Armida J., Ramyaa, Ramyaa, Domschot, Evelyn, Lamb, Christopher C., Verzi, Stephen J., Kegelmeyer, W. Philip
Despite the potential of Machine learning (ML) to learn the behavior of malware, detect novel malware samples, and significantly improve information security (InfoSec) we see few, if any, high-impact ML techniques in deployed systems, notwithstanding
Externí odkaz:
http://arxiv.org/abs/2005.01800
Akademický článek
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Publikováno v:
In Journal of Criminal Justice January-February 2024 90
Publikováno v:
In Ultrasound in Medicine & Biology May 2023 49(5):1118-1128
Autor:
Smith, Michael R., Ingram, Joe B., Lamb, Christopher C., Draelos, Timothy J., Doak, Justin E., Aimone, James B., James, Conrad D.
It is needed to ensure the integrity of systems that process sensitive information and control many aspects of everyday life. We examine the use of machine learning algorithms to detect malware using the system calls generated by executables-alleviat
Externí odkaz:
http://arxiv.org/abs/1711.03947
Autor:
Smith, Michael R., Hill, Aaron J., Carlson, Kristofor D., Vineyard, Craig M., Donaldson, Jonathon, Follett, David R., Follett, Pamela L., Naegle, John H., James, Conrad D., Aimone, James B.
Information in neural networks is represented as weighted connections, or synapses, between neurons. This poses a problem as the primary computational bottleneck for neural networks is the vector-matrix multiply when inputs are multiplied by the neur
Externí odkaz:
http://arxiv.org/abs/1704.08306
Approved for public release; distribution is unlimited
Prior MBA projects have analyzed the capabilities of various in-theater non-government organizations (NGOs) and their interactive efforts with the U.S. military toward rapid and effective hu
Prior MBA projects have analyzed the capabilities of various in-theater non-government organizations (NGOs) and their interactive efforts with the U.S. military toward rapid and effective hu
Externí odkaz:
http://hdl.handle.net/10945/44552