Results 161 to 170 of about 336,781 (326)
Domain‐Aware Implicit Network for Arbitrary‐Scale Remote Sensing Image Super‐Resolution
Advanced Intelligent Discovery, EarlyView.Although existing arbitrary‐scale image super‐resolution methods are flexible to reconstruct images with arbitrary scales, the characteristic of training distribution is neglected that there exists domain shift between samples of various scales. In this work, a Domain‐Aware Implicit Network (DAIN) is proposed to handle it from the perspective of domain
Xiaoxuan Ren +6 more
wiley +1 more source
Advanced Intelligent Discovery, EarlyView.This study applies QSAR‐based new approach methodologies to 90 synthetic tattoo and permanent makeup pigments, revealing systemic links between their physicochemical properties and absorption, distribution, metabolism, and elimination profiles. The correlation‐driven analysis using SwissADME, ChemBCPP, and principal component analysis uncovers insights
Girija Bansod +10 more
wiley +1 more source
Economic Contribution of MSE to Women in Dire Dawa City A comparative Study Between MSE And Non-MSE
The aim of this study is to examine the contribution of Micro & small Enterprises on women by focusing on MSE participants in Dire Dawa city in comparative study of their counter parts. It also addressed factors that affect women’s engagement in the sector. The study has used both qualitative and quantitative data collected from 87 MSE participants
openaire +2 more sources
AN AUTOMATIC BLIND MODULATION RECOGNITION ALGORITHM FOR M-PSK SIGNALS BASED ON MSE CRITERION
, 2005 M. Vastram Naik +3 more
openalex +1 more source
Entrepreneur Success in Micro and Small Enterprises (MSEs): Evidence from Indonesia [PDF]
, 2019 Elsye Tandelilin +3 more
openalex +1 more source
Indirect NRDF for Partially Observable Gauss-Markov Processes with MSE Distortion: Complete Characterizations and Optimal Solutions [PDF]
, 2020 Fotios Stavrou, Mikael Skoglund
openalex +1 more source
Advanced Intelligent Discovery, EarlyView.Combining machine learning and probabilistic statistical learning is a powerful way to discover and design new materials. A variety of machine learning approaches can be used to identify promising candidates for target applications, and causal inference can help identify potential ways to make them a reality.
Jonathan Y. C. Ting, Amanda S. Barnard
wiley +1 more source
Advanced Intelligent Discovery, EarlyView.A sequential deep learning framework is developed to model surface roughness progression in multi‐stage microneedle fabrication. Using real‐world experimental data from 3D printing, molding, and casting stages, an long short‐term memory‐based recurrent neural network captures the cumulative influence of geometric parameters and intermediate outputs ...
Abdollah Ahmadpour +5 more
wiley +1 more source
Advanced Intelligent Discovery, EarlyView.A novel convolutional neural network architecture enables rapid, unsupervised analysis of IR spectroscopic data from DRIFTS and IRRAS. By combining synthetic data generation with parallel convolutional layers and advanced regularization, the model accurately resolves spectral features of adsorbed CO, offering real‐time insights into ceria surface ...
Mehrdad Jalali +5 more
wiley +1 more source
Causal MSE-Optimal Filters for Personal Audio Subject to Constrained Contrast [PDF]
, 2019 Simon Widmark
openalex +1 more source