Results 71 to 80 of about 204,781 (309)
Clustering-oriented Multiple Convolutional Neural Networks for Single Image Super-resolution [PDF]
, 2017 This is the author accepted manuscript. The final version is available from Springer Verlag via the DOI in this record.In contrast to the human visual system (HVS) that applies different processing schemes to visual information of different textural ...
Ren, Peng +11 more
core +1 more source
Predicting the Demand in Bitcoin Using Data Charts: A Convolutional Neural Networks Prediction Model [PDF]
, 2020 Traditional time series modeling techniques emphasize on predicting cryptocurrencies using classically structured data representation as numerical features to present the time-series datasets.
Kashef, R., Corrigan, L., Ibrahim, A.
core +1 more source
Understanding convolutional neural networks [PDF]
, 2021 In the past decade, deep learning has fueled a number of exciting developments in artificial intelligence (AI). However, as deep learning is increasingly being applied to high-impact domains, like medical diagnosis or autonomous driving, the impact of ...
Fong, Ruth
core +2 more sources
Advanced Engineering Materials, EarlyView.Low‐voltage FIB‐SEM tomography combined with a image preprocessing pipeline improves phase contrast and enables reliable machine‐learning segmentation of conductive networks in lithium‐ion battery electrodes. Structural descriptors are extracted from segmented images, done semimanually and automated, and compared.
Lisa Beran +6 more
wiley +1 more source
Quantum Dilated Convolutional Neural Networks
IEEE Access, 2022 In recent years, with rapid progress in the development of quantum technologies, quantum machine learning has attracted a lot of interest. In particular, a family of hybrid quantum-classical neural networks, consisting of classical and quantum elements ...
Yixiong Chen
doaj +1 more source
Advanced Engineering Materials, EarlyView.This study applies machine learning regression to predict chromium layer thickness in decorative trivalent chromium electroplating, using 441 experiments from laboratory‐scale (1L) and pilot‐scale (14L) setups. Tree‐based models, particularly CatBoost, outperformed linear regression by capturing nonlinear parameter interactions (R2$R^2$ up to 0.77 ...
Christoph Baumer +4 more
wiley +1 more source
Petrographic analysis with deep convolutional neural networks [PDF]
, 2019 Petrographic analysis is based on the microscopic description and classification of rocks and is a crucial technique for sedimentary and diagenetic studies.
Pires de Lima, Rafael
core
Top-Down Person Re-Identification With Siamese Convolutional Neural Networks [PDF]
, 2018 Automated person re-identification is a challenging research problem that has many real-world applications, especially in video surveillance. While many recent studies have been focusing on solving the person re-identification problem using full-scale ...
Liu, Z +4 more
core +1 more source
A Practical Noise2Noise Denoising Pipeline for High‐Throughput Raman Spectroscopy
Advanced Engineering Materials, EarlyView.A lightweight and reproducible denoising pipeline for high‐throughput Raman spectroscopy is introduced, based on a 1D convolutional autoencoder trained with a Noise2Noise strategy. Using only repeated short‐exposure acquisitions, the method suppresses stochastic noise without reference spectra, enabling reliable spectral reconstruction while preserving
David Martin‐Calle +5 more
wiley +1 more source
A new approach to seasonal energy consumption forecasting using temporal convolutional networks
Results in Engineering, 2023 There has been a significant increase in the attention paid to resource management in smart grids, and several energy forecasting models have been published in the literature.
Abdul Khalique Shaikh +4 more
doaj +1 more source