Results 161 to 170 of about 16,403 (208)

A Novel Multistep Wavelet Convolutional Transfer Diagnostic Framework for Cross-Machine Bearing Fault Diagnosis. [PDF]

Sensors (Basel)
Zhao L, He Y, Zheng H, Dai D.
europepmc   +1 more source

CausalCervixNet: convolutional neural networks with causal insight (CICNN) in cervical cancer cell classification-leveraging deep learning models for enhanced diagnostic accuracy. [PDF]

BMC Cancer
Taghados Z, Azimifar Z, Monsefi M, Jahromi MA.   +3 more
europepmc   +1 more source

A motor bearing fault diagnosis model based on multi-adversarial domain adaptation. [PDF]

Sci Rep
Liu XM, Zhang RM, Li JP, Xu YF, Li K.
europepmc   +1 more source

Adversarial Training Based Domain Adaptation of Skin Cancer Images. [PDF]

Life (Basel)
Gilani SQ, Umair M, Naqvi M, Marques O, Kim HC.   +4 more
europepmc   +1 more source

Enhancing genomic-based forward prediction accuracy in wheat by integrating UAV-derived hyperspectral and environmental data with machine learning under heat-stressed environments. [PDF]

Plant Genome
McBreen J, Babar MA, Jarquin D, Ampatzidis Y, Khan N, Kunwar S, Acharya JP, Adewale S, Brown-Guedira G.   +8 more
europepmc   +1 more source

Multi-Feature Unsupervised Domain Adaptation (M-FUDA) Applied to Cross Unaligned Domain-Specific Distributions in Device-Free Human Activity Classification. [PDF]

Sensors (Basel)
Hassan M, Kelsey T.
europepmc   +1 more source

Lightweight Knowledge Distillation-Based Transfer Learning Framework for Rolling Bearing Fault Diagnosis. [PDF]

Sensors (Basel)
Lu R, Liu S, Gong Z, Xu C, Ma Z, Zhong Y, Li B.   +6 more
europepmc   +1 more source

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A new application of reproducing kernel Hilbert space method

AIP Conference Proceedings, 2018
We demonstrate a new application of the reproducing kernel Hilbert space method in this paper. We showed our results by tables and figures.
Akgul, A., Sakar, M. Giyas
openaire   +4 more sources

Multiscale Approximation and Reproducing Kernel Hilbert Space Methods

SIAM Journal on Numerical Analysis, 2015
We consider reproducing kernels $K:\Omega\times \Omega \to \mathbb{R}$ in multiscale series expansion form, i.e., kernels of the form $K\left(\boldsymbol{x},\boldsymbol{y}\right)=\sum_{\ell\in\mathbb{N}}\lambda_\ell\sum_{j\in I_\ell}\phi_{\ell,j}\left(\boldsymbol{x}\right)\phi_{\ell,j}\left(\boldsymbol{y}\right)$ with weights $\lambda_\ell$ and ...
Christian Rieger, Barbara Zwicknagl, Michael Griebel   +2 more
openaire   +2 more sources