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141
Basic types of LabVIEW scripting functions used for the purpose of automated code generation.
Published 2024Subjects: -
142
Data from: Structural and Functional Analysis of Multi-interface Domains
Published 2012“…This work applies graph theory and algorithms to discover fingerprints for the multiple interfaces of a domain and to establish associations between the interfaces and functions, based on a huge set of multi-interface proteins from PDB. …”
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143
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Experiment 2D and 5D: Progressive Sample Scaling Algorithm To Solve Many-Affine BBOB Functions.
Published 2024“…</p><p dir="ltr"><b>Objectives</b></p><ul><li>Solve Many-Affine BBOB Functions using a Deterministic Algorithm.</li></ul><p dir="ltr"><b>Limitations</b></p><ul><li>This algorithm is designed for Many-Affine BBOB problems of 2 and 5 dimensions.…”
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145
Table 1_Comprehensive analysis of anoikis-related gene signature in ulcerative colitis using machine learning algorithms.xlsx
Published 2025“…Key anoikis-DEGs in UC were identified using three machine learning algorithms, including least absolute shrinkage and selection operator (LASSO) Cox regression, random forest (RF), and support vector machine (SVM). …”
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146
Image 1_Comprehensive analysis of anoikis-related gene signature in ulcerative colitis using machine learning algorithms.tiff
Published 2025“…Key anoikis-DEGs in UC were identified using three machine learning algorithms, including least absolute shrinkage and selection operator (LASSO) Cox regression, random forest (RF), and support vector machine (SVM). …”
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147
Image 4_Comprehensive analysis of anoikis-related gene signature in ulcerative colitis using machine learning algorithms.tiff
Published 2025“…Key anoikis-DEGs in UC were identified using three machine learning algorithms, including least absolute shrinkage and selection operator (LASSO) Cox regression, random forest (RF), and support vector machine (SVM). …”
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148
Image 5_Comprehensive analysis of anoikis-related gene signature in ulcerative colitis using machine learning algorithms.tiff
Published 2025“…Key anoikis-DEGs in UC were identified using three machine learning algorithms, including least absolute shrinkage and selection operator (LASSO) Cox regression, random forest (RF), and support vector machine (SVM). …”
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149
Image 3_Comprehensive analysis of anoikis-related gene signature in ulcerative colitis using machine learning algorithms.tiff
Published 2025“…Key anoikis-DEGs in UC were identified using three machine learning algorithms, including least absolute shrinkage and selection operator (LASSO) Cox regression, random forest (RF), and support vector machine (SVM). …”
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150
Image 2_Comprehensive analysis of anoikis-related gene signature in ulcerative colitis using machine learning algorithms.tiff
Published 2025“…Key anoikis-DEGs in UC were identified using three machine learning algorithms, including least absolute shrinkage and selection operator (LASSO) Cox regression, random forest (RF), and support vector machine (SVM). …”
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151
Flowchart depicting the optimal control framework.
Published 2019“…<p>We developed two approaches (AD-ADOLC and AD-Recorder) to make an OpenSim function <i>F</i> and its forward (<i>F fwd</i>) and reverse (<i>F rev</i>) directional derivatives available within the CasADi environment for use by the NLP solver during the optimization. …”
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152
Statistical results of various algorithms.
Published 2025“…Results from experiments confirm that the enhanced WOA algorithm outperforms the standard WOA algorithm in terms of both fitness value and convergence speed. …”
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153
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154
Images of partial benchmark functions.
Published 2025“…Results from experiments confirm that the enhanced WOA algorithm outperforms the standard WOA algorithm in terms of both fitness value and convergence speed. …”
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155
Analytic Gradients for Density Fitting MP2 Using Natural Auxiliary Functions
Published 2024“…The natural auxiliary function (NAF) approach is an approximation to decrease the size of the auxiliary basis set required for quantum chemical calculations utilizing the density fitting technique. …”
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Relearning under noisy feedback signal using recursive-least-squares algorithm and local learning algorithm [47].
Published 2021“…<p>(A-B) Relearning performance, measured as mean squared error (MSE), as a function of the amplitude of the noise in the feedback signal using recursive-least-squares (RLS) algorithm (A) and an alternative implementation with a local learning algorithm (Eprop) (B). …”
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160