Results 41 to 50 of about 3,605,315 (303)

Fine Tuning Hyperparameters of Deep Learning Models Using Metaheuristic Accelerated Particle Swarm Optimization Algorithm

IEEE Access
In recent years, Convolutional Neural Networks (CNNs) have emerged as powerful tools for solving complex real-world problems, particularly in the domain of image processing.
Abdel-Hamid M. Emara, Ghada Atteia, Jawad Hasan Alkhateeb   +2 more
doaj   +1 more source

YOLO Network with a Circular Bounding Box to Classify the Flowering Degree of Chrysanthemum

AgriEngineering, 2023
Detecting objects in digital images is challenging in computer vision, traditionally requiring manual threshold selection. However, object detection has improved significantly with convolutional neural networks (CNNs), and other advanced algorithms, like
Hee-Mun Park, Jin-Hyun Park
doaj   +1 more source

Integrating convolutional neural networks into a sparse distributed representation model based on mammalian cortical learning

, 2016
Biological brains exhibit a remarkable capacity to recognise real-world patterns effectively. Despite major advances in neuroscience over the last few decades, an understanding of the brain's underlying mechanisms for pattern recognition remains ...
Daniel E. Padilla, McDonnell, M.D., Mark D. McDonnell, Padilla, D.E.   +3 more
core   +1 more source

Synchronization in an array of linearly stochastically coupled networks with time delays [PDF]

, 2007
This is the post print version of the article. The official published version can be obtained from the link - Copyright 2007 Elsevier LtdIn this paper, the complete synchronization problem is investigated in an array of linearly stochastically coupled ...
Wang, Z, Cao, J, Sun, Y
core   +1 more source

Convolutional Neural Networks in the Inspection of Serrasalmids (Characiformes) Fingerlings

Animals
Aquaculture produces more than 122 million tons of fish globally. Among the several economically important species are the Serrasalmidae, which are valued for their nutritional and sensory characteristics.
Marília Parreira Fernandes, Adriano Carvalho Costa, Heyde Francielle do Carmo França, Alene Santos Souza, Pedro Henrique de Oliveira Viadanna, Lessandro do Carmo Lima, Liege Dauny Horn, Matheus Barp Pierozan, Isabel Rodrigues de Rezende, Rafaella Machado dos S. de Medeiros, Bruno Moraes Braganholo, Lucas Oliveira Pereira da Silva, Jean Marc Nacife, Kátia Aparecida de Pinho Costa, Marco Antônio Pereira da Silva, Rodrigo Fortunato de Oliveira   +15 more
doaj   +1 more source

Humans can decipher adversarial images

Nature Communications, 2019
Convolutional Neural Networks (CNNs) have reached human-level benchmarks in classifying images, but they can be “fooled” by adversarial examples that elicit bizarre misclassifications from machines.
Zhenglong Zhou, Chaz Firestone
doaj   +1 more source

Application of Convolutional Neural Network (CNN) to Recognize Ship Structures

Sensors, 2022
The purpose of this paper is to study the recognition of ships and their structures to improve the safety of drone operations engaged in shore-to-ship drone delivery service. This study has developed a system that can distinguish between ships and their structures by using a convolutional neural network (CNN).
Jae-Jun Lim, Dae-Won Kim, Woon-Hee Hong, Min Kim, Dong-Hoon Lee, Sun-Young Kim, Jae-Hoon Jeong   +6 more
openaire   +5 more sources

Two-Branch Convolutional Neural Network with Polarized Full Attention for Hyperspectral Image Classification

Remote Sensing, 2023
In recent years, convolutional neural networks (CNNs) have been introduced for pixel-wise hyperspectral image (HSI) classification tasks. However, some problems of the CNNs are still insufficiently addressed, such as the receptive field problem, small ...
Haimiao Ge, Liguo Wang, Moqi Liu, Yuexia Zhu, Xiaoyu Zhao, Haizhu Pan, Yanzhong Liu   +6 more
doaj   +1 more source

Convolutional neural networks with dynamic regularization

, 2020
Regularization is commonly used for alleviating overfitting in machine learning. For convolutional neural networks (CNNs), regularization methods, such as DropBlock and Shake-Shake, have illustrated the improvement in the generalization performance ...
Wang, Yi, Chau, Lap-Pui, Bian, Zhen-Peng, Hou, Junhui   +3 more
core   +1 more source

Clinical Validation of Artificial Intelligence (AI)‐based Cartilage Segmentation Predicting Knee Replacement

Arthritis Care &Research, Accepted Article.
Objective For cartilage morphology to serve as a scalable endpoint in clinical trials, analyses should be performed automatically without human interaction. To clinically validate artificial intelligence (AI)‐based analysis, we studied cartilage loss from MRI prior to knee replacement.
Felix Eckstein, David J. Hunter, C. Kent Kwoh, Wolfgang Wirth   +3 more
wiley   +1 more source