Results 141 to 150 of about 16,329 (217)

Unmasking insider threats using a robust hybrid optimized generative pretrained neural network approach. [PDF]

Sci Rep
Lavanya P, Glory HA, Aggarwal M, Sriram VSS.   +3 more
europepmc   +1 more source

Integrating geometries of ReLU feedforward neural networks. [PDF]

Front Big Data, 2023
Liu Y, Caglar T, Peterson C, Kirby M.
europepmc   +1 more source

Multi-scale dynamic graph neural network for PM2.5 concentration prediction in regional station cluster. [PDF]

PLoS One
Lu X, Liao J, Huang H, Li J, Huang H, Zhu Y.   +5 more
europepmc   +1 more source

New equivalent resistance formula of [Formula: see text] rectangular resistor network represented by Chebyshev polynomials. [PDF]

Sci Rep
Wang R, Jiang X, Zheng Y, Jiang Z, Xiang D.   +4 more
europepmc   +1 more source

In-Plane Vibration Analysis of Rectangular Plates with Elastically Restrained Boundaries Using Differential Quadrature Method of Variational Weak Form. [PDF]

Materials (Basel)
Wang X, Zhou W, Yi S, Li S.
europepmc   +1 more source

A multi-domain graph convolutional network-based prediction model for personalized motor imagery action. [PDF]

Front Neurosci
Ge J, Wang J, Zheng X, Li M, Wang F, Xu G.   +5 more
europepmc   +1 more source

Efficient hyperparameter estimation in Bayesian inverse problems using sample average approximation. [PDF]

Philos Trans A Math Phys Eng Sci
Chung J, Miller SM, Sabate Landman M, Saibaba AK.   +3 more
europepmc   +1 more source

CGSDA: inferring snoRNA-disease associations via ChebNetII and GatedGCN. [PDF]

Front Genet
Zou Y, Lu Y, Lu S, Wei Z, Li L, Liao S, Zeng T, Zhang Y, Miao R.   +8 more
europepmc   +1 more source

Evaluating topological and graph-theoretical approaches to extract complex multimodal brain connectivity patterns in multiple sclerosis. [PDF]

Health Inf Sci Syst
Lozano-Bagén T, Martinez-Heras E, Pontillo G, Solana E, Vivó F, Petracca M, Calvi A, Garrido-Romero S, Solé-Ribalta A, Llufriu S, Prados F, Casas-Roma J.   +11 more
europepmc   +1 more source

Multi-grained contrastive-learning driven MLPs for node classification. [PDF]

Sci Rep
Bao Q, Huang X, Zhuang W, Pan P.
europepmc   +1 more source