Results 21 to 30 of about 86,091 (310)
, 2022 The CNNs block consists of three 3D convolutional layers, with kernels of sizes 3×3×3, each of which is followed by a max-pooling layer with a kernel of size 2×2×2.
Rogers F. Silva (11986044) +10 more
core +1 more source
Convolutional Neural Networks: A Survey
Computers, 2023 Artificial intelligence (AI) has become a cornerstone of modern technology, revolutionizing industries from healthcare to finance. Convolutional neural networks (CNNs) are a subset of AI that have emerged as a powerful tool for various tasks including ...
Moez Krichen
doaj +1 more source
CNN+CNN: Convolutional Decoders for Image Captioning
CoRR, 2018 Image captioning is a challenging task that combines the field of computer vision and natural language processing. A variety of approaches have been proposed to achieve the goal of automatically describing an image, and recurrent neural network (RNN) or long-short term memory (LSTM) based models dominate this field.
Qingzhong Wang, Antoni B. Chan
openaire +2 more sources
Open Chemistry, 2023 Ciprofloxacin (CIP) belongs to the fluoroquinolone antibiotic family. It is mostly used for the treatment of bacterial infections and highly recalcitrant to naturally decompose.
Tamyiz Muchammad, Doong Ruey-an
doaj +1 more source
CNNs Avoid the Curse of Dimensionality by Learning on Patches
IEEE Open Journal of Signal Processing, 2023 Despite the success of convolutional neural networks (CNNs) in numerous computer vision tasks and their extraordinary generalization performances, several attempts to predict the generalization errors of CNNs have only been limited to a posteriori ...
Vamshi C. Madala +2 more
doaj +1 more source
A Comprehensive Review on the Application of 3D Convolutional Neural Networks in Medical Imaging
Engineering Proceedings, 2023 Convolutional Neural Networks (CNNs) are kinds of deep learning models that were created primarily for processing and evaluating visual input, which makes them extremely applicable in the field of medical imaging.
Satyam Tiwari +5 more
doaj +1 more source
Convolutional Neural Networks using FPGA-based Pipelining
Iraqi Journal for Computer Science and Mathematics, 2023 In order to speed up convolutional neural networks (CNNs), this study gives a complete overview of the use of FPGA-based pipelining for hardware acceleration of CNNs.
Gheni A. Ali, ahmed hussein ali
doaj +1 more source
Structured Receptive Fields in CNNs [PDF]
, 2016 Learning powerful feature representations with CNNs is hard when training data are limited. Pre-training is one way to overcome this, but it requires large datasets sufficiently similar to the target domain.
van Gemert, J. +7 more
core +1 more source
Diagnostics, 2022 (1) Background: The present study aims to evaluate and compare the model performances of different convolutional neural networks (CNNs) used for classifying sagittal skeletal patterns.
Haizhen Li +4 more
doaj +1 more source
CNNs-based hybrid learning framework.
, 2018 CNNs-based hybrid learning framework.
Chun Yang (251691) +4 more
core +1 more source