Results 121 to 130 of about 3,605,315 (303)

An Extensive Study of Convolutional Neural Networks: Applications in Computer Vision for Improved Robotics Perceptions

Sensors
Convolutional neural networks (CNNs), a type of artificial neural network (ANN) in the deep learning (DL) domain, have gained popularity in several computer vision applications and are attracting research in other fields, including robotic perception ...
Ravi Raj, Andrzej Kos
doaj   +1 more source

Bankruptcy Analysis Using Images and Convolutional Neural Networks (CNN)

The marketing departments of financial institutions strive to craft products and services that cater to the diverse needs of businesses of all sizes. However, it is evident upon analysis that larger corporations often receive a more substantial portion of available funds. This disparity arises from the relative ease of assessing the risk of default and
Luiz Wanderley Tavares, José Afonso Mazzon, Francisco Carlos Paletta, Fábio Meletti de Oliveira Barros   +3 more
openaire   +3 more sources

Augmenting convolutional neural networks with kernels inspired by the early visual system [PDF]

, 2022
openEarly neural networks were inspired by biology: the McCulloch-Pitts neuron, the Perceptron and the Neocognitron were all attempting to imitate the functioning of the brain.
ROVOLETTO, MATTEO BRUNO
core  

Physics‐Informed Machine Learning for Sustainable Alloy Design: Toward a Recyclable Unified Q&P Steel

Advanced Science, EarlyView.
A physics‐informed property‐bridging framework links high‐throughput hardness screening to tensile performance in quenching and partitioning steels. By transferring metallurgically guided representations across properties, a single alloy composition is designed to achieve multiple strength grades through heat‐treatment tuning alone, offering a ...
Xiaolu Wei, Yong Li, Chenchong Wang, Lingyu Wang, Xiang Song, Keming Mao, Yu Zhang, Wei Xu   +7 more
wiley   +1 more source

Performance analysis of seven Convolutional Neural Networks (CNNs) with transfer learning for Invasive Ductal Carcinoma (IDC) grading in breast histopathological images. [PDF]

Sci Rep, 2022
Voon W, Hum YC, Tee YK, Yap WS, Salim MIM, Tan TS, Mokayed H, Lai KW.   +7 more
europepmc   +1 more source

Directly Connected Convolutional Neural Networks

, 2018
Convolutional neural networks (CNNs) have better performance in feature extraction and classification. Most of the applications are based on a traditional structure of CNNs.
Zhanji Gui, Jun Ou, Shuqing Li, Qingxiu Wu   +3 more
core   +1 more source

Retinomorphic Visual Processing Enabled by Contact‐Engineered IGZO Optoelectronic Synaptic Memtransistors

Advanced Science, EarlyView.
Here, we present an optoelectronic synaptic memtransistor (OSMT) integrating photoresponsive IGZO with contact‐engineered HfO2, enabling electrically and optically tunable synaptic weights. The device demonstrates broad range of tunable conductance states and array‐level image processing, highlighting its potential for intelligent machine vision ...
Donghyun Kang, San Nam, Dayul Nam, In‐Soo Kim, Myung‐Gil Kim, Sung Kyu Park, Yong‐Hoon Kim   +6 more
wiley   +1 more source

The Application of Convolutional Neural Networks (CNNs) to Recognize Defects in 3D-Printed Parts. [PDF]

Materials (Basel), 2021
Wen H, Huang C, Guo S.
europepmc   +1 more source

A Versatile‐Designable Framework for Active and Programmable Shape‐Morphing Soft Matter Systems: From Inverse Design to Closed‐Loop Control

Advanced Science, EarlyView.
A versatile framework integrates addressable electrothermal actuation and strain‐constraint mechanisms to construct programmable shape‐morphing soft matter systems. By combining an analytical inverse design strategy for high‐fidelity 3D surface reconstruction with deep learning‐based closed‐loop control, this approach enables zero‐energy shape locking,
Kai Liu, Peiling Xie, Ruitong Song, Banghan Liu, Rui Guo, Jiu‐an Lv   +5 more
wiley   +1 more source

Convolutional neural network transformer (CNNT) for fluorescence microscopy image denoising with improved generalization and fast adaptation

Scientific Reports
Deep neural networks can improve the quality of fluorescence microscopy images. Previous methods, based on Convolutional Neural Networks (CNNs), require time-consuming training of individual models for each experiment, impairing their applicability and ...
Azaan Rehman, Alexander Zhovmer, Ryo Sato, Yoh-suke Mukouyama, Jiji Chen, Alberto Rissone, Rosa Puertollano, Jiamin Liu, Harshad D. Vishwasrao, Hari Shroff, Christian A. Combs, Hui Xue   +11 more
doaj   +1 more source