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1980 3
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1982 5
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1984 7
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1988 5
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1991 10
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1993 12
1994 12
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2004 47
2005 33
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2007 49
2008 51
2009 69
2010 47
2011 63
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1,983 results

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Page 1
Showing results for obtain nn
Search for Obáin NN instead (2 results)
Infant intraosseous infusion.
DeBoer S, Russell T, Seaver M, Vardi A. DeBoer S, et al. Neonatal Netw. 2008 Jan-Feb;27(1):25-32. doi: 10.1891/0730-0832.27.1.25. Neonatal Netw. 2008. PMID: 18232588 Review.
Outside of the immediate newborn period, however, it may be impossible to obtain peripheral or umbilical IV access in critically ill newborns. Intraosseous (IO) infusion is not widely used in the neonatal population, but is a viable option when IV access cannot be establis …
Outside of the immediate newborn period, however, it may be impossible to obtain peripheral or umbilical IV access in critically ill …
Symmetry-aware reservoir computing.
Barbosa WAS, Griffith A, Rowlands GE, Govia LCG, Ribeill GJ, Nguyen MH, Ohki TA, Gauthier DJ. Barbosa WAS, et al. Phys Rev E. 2021 Oct;104(4-2):045307. doi: 10.1103/PhysRevE.104.045307. Phys Rev E. 2021. PMID: 34781436
For the parity task, our symmetry-aware RC obtains zero error using an exponentially reduced neural network and training data, greatly speeding up the time to result and outperforming artificial neural networks. When both symmetries are respected, we find that the network …
For the parity task, our symmetry-aware RC obtains zero error using an exponentially reduced neural network and training data, greatl …
nn-PINNs: Non-Newtonian physics-informed neural networks for complex fluid modeling.
Mahmoudabadbozchelou M, Karniadakis GE, Jamali S. Mahmoudabadbozchelou M, et al. Soft Matter. 2021 Dec 22;18(1):172-185. doi: 10.1039/d1sm01298c. Soft Matter. 2021. PMID: 34859251
In this work, we present non-Newtonian physics-informed neural networks (nn-PINNs) for solving systems of coupled PDEs adopted for complex fluid flow modeling. ...These include a range of Generalized Newtonian Fluid (GNF) empirical constitutive models, as well as some phen …
In this work, we present non-Newtonian physics-informed neural networks (nn-PINNs) for solving systems of coupled PDEs adopted for co …
Neural networks for geospatial data.
Zhan W, Datta A. Zhan W, et al. J Am Stat Assoc. 2025;120(549):535-547. doi: 10.1080/01621459.2024.2356293. Epub 2024 Jun 24. J Am Stat Assoc. 2025. PMID: 40255678
We provide methodology to obtain uncertainty bounds for estimation and predictions from NN-GLS. Theoretically, we show that NN-GLS will be consistent for irregularly observed spatially correlated data processes. ...We demonstrate the methodology through numer …
We provide methodology to obtain uncertainty bounds for estimation and predictions from NN-GLS. Theoretically, we show that …
Comprehensive Review on the Synthesis of [1,2,3]Triazolo[1,5-a]Quinolines.
Da Costa GP, Sacramento M, Barcellos AM, Alves D. Da Costa GP, et al. Chem Rec. 2024 Nov;24(11):e202400107. doi: 10.1002/tcr.202400107. Epub 2024 Oct 16. Chem Rec. 2024. PMID: 39413121 Review.
This review encompasses a broad range of approaches, describing several reactions for obtaining this since, such as dehydrogenative cyclization, oxidative N-N coupling, Dieckmann condensation, intramolecular Heck, (3+2)-cycloaddition, Ullman-type coupling and direct intram …
This review encompasses a broad range of approaches, describing several reactions for obtaining this since, such as dehydrogenative c …
Multivariate Density Estimation by Neural Networks.
Peerlings DEW, van den Brakel JA, Basturk N, Puts MJH. Peerlings DEW, et al. IEEE Trans Neural Netw Learn Syst. 2024 Feb;35(2):2436-2447. doi: 10.1109/TNNLS.2022.3190220. Epub 2024 Feb 5. IEEE Trans Neural Netw Learn Syst. 2024. PMID: 35849671
The proposed NN has advantages compared to well-known parametric and nonparametric density estimators. ...The proposed solution applies the PDF estimation by NN to continuous distributions as well as discrete distributions. We also show that the proposed solution to …
The proposed NN has advantages compared to well-known parametric and nonparametric density estimators. ...The proposed solution appli …
Reticular Chemistry-Enabled Sonodynamic Activity of Covalent Organic Frameworks for Nanodynamic Cancer Therapy.
Duan Y, Yu Y, Liu P, Gao Y, Dai X, Zhang L, Chen L, Chen Y. Duan Y, et al. Angew Chem Int Ed Engl. 2023 May 8;62(20):e202302146. doi: 10.1002/anie.202302146. Epub 2023 Apr 3. Angew Chem Int Ed Engl. 2023. PMID: 36894504
Herein, we report that the reticular chemistry-based synthesis of COFs from two inert monomers yields a COF-based sonosensitizer (TPE-NN) with inherent sonodynamic activity. Subsequently, a nanoscale COF TPE-NN is fabricated and embedded with copper (Cu)-coordinated …
Herein, we report that the reticular chemistry-based synthesis of COFs from two inert monomers yields a COF-based sonosensitizer (TPE-NN …
A computational approach to biological pathogenicity.
Garzon M, Mainali S, Chacon MF, Azizzadeh-Roodpish S. Garzon M, et al. Mol Genet Genomics. 2022 Nov;297(6):1741-1754. doi: 10.1007/s00438-022-01951-w. Epub 2022 Sep 20. Mol Genet Genomics. 2022. PMID: 36125534 Free PMC article.
We propose two operational tests based on the nearest neighbor (NN) model of the Gibbs Energy and an approximating metric (the h-distance.) Quality assessments demonstrate that these tests predict pathogenicity with an accuracy of over 80%, and sensitivity and specificity …
We propose two operational tests based on the nearest neighbor (NN) model of the Gibbs Energy and an approximating metric (the h-dist …
MetaCell: analysis of single-cell RNA-seq data using K-nn graph partitions.
Baran Y, Bercovich A, Sebe-Pedros A, Lubling Y, Giladi A, Chomsky E, Meir Z, Hoichman M, Lifshitz A, Tanay A. Baran Y, et al. Genome Biol. 2019 Oct 11;20(1):206. doi: 10.1186/s13059-019-1812-2. Genome Biol. 2019. PMID: 31604482 Free PMC article.
Unlike clustering analysis, our algorithm specializes at obtaining granular as opposed to maximal groups. We show how to use metacells as building blocks for complex quantitative transcriptional maps while avoiding data smoothing. ...
Unlike clustering analysis, our algorithm specializes at obtaining granular as opposed to maximal groups. We show how to use metacell …
Applying machine learning technologies to explore students' learning features and performance prediction.
Su YS, Lin YD, Liu TQ. Su YS, et al. Front Neurosci. 2022 Dec 22;16:1018005. doi: 10.3389/fnins.2022.1018005. eCollection 2022. Front Neurosci. 2022. PMID: 36620438 Free PMC article. Review.
In this study, we used random forest (RF), support vector machine (SVM), logistic regression (LR), and neural network (NN) algorithms to predict whether students would submit on time for the course. Among them, the NN algorithm showed the best prediction results. Ed …
In this study, we used random forest (RF), support vector machine (SVM), logistic regression (LR), and neural network (NN) algorithms …
1,983 results