Results 61 to 70 of about 142,621 (312)

A lightweight multi-path convolutional neural network architecture using optimal features selection for multiclass classification of brain tumor using magnetic resonance images

Results in Engineering
Brain tumor diagnosis requires precision due to the high mortality rate associated with the growth of abnormal cells in the brain. Early disease identification, improved survival rates, and less reliance on professional MRI analysis is possible with ...
Amreen Batool, Yung-Cheol Byun
doaj   +1 more source

ST-CNN: Spatial-Temporal Convolutional Neural Network for crowd counting in videos

, 2019
The task of crowd counting and density maps estimating from videos is challenging due to severe occlusions, scene perspective distortions and diverse crowd distributions.
Gao, Y, Han, J, Zhang, B, Miao, Y
core   +1 more source

Transferable Convolutional Neural Network for Weed Mapping With Multisensor Imagery

, 2021
Automatic weed monitoring and classification are critical for effective site-specific weed management. With the increasing availability of different sensors, it is possible for weed management to be achieved by processing a wide range of images captured ...
Hu, J, Zhou, J, Jia, X, Farooq, A
core   +1 more source

Short Text Aspect-Based Sentiment Analysis Based on CNN + BiGRU

Applied Sciences, 2022
This paper describes the construction a short-text aspect-based sentiment analysis method based on Convolutional Neural Network (CNN) and Bidirectional Gating Recurrent Unit (BiGRU).
Ziwen Gao, Zhiyi Li, Jiaying Luo, Xiaolin Li   +3 more
doaj   +1 more source

Nondestructive prediction of physicochemical properties of kimchi sauce with artificial and convolutional neural networks

International Journal of Food Properties, 2023
This study presents a comparison of prediction performances by an artificial neural network (ANN), well-known deep convolutional neural network (D-CNN) models, and four proposed shallow convolutional neural network (S-CNN) models to forecast three key ...
Hae-Il Yang, Sung-Gi Min, Hye-Young Seo, Seong Youl Lee, Ji-Hee Yang, Mi-Ai Lee, Sung-Hee Park, Jong-Bang Eun, Young Bae Chung   +8 more
doaj   +1 more source

Deep Learning Approaches on Defect Detection in High Resolution Aerial Images of Insulators

Sensors, 2021
By detecting the defect location in high-resolution insulator images collected by unmanned aerial vehicle (UAV) in various environments, the occurrence of power failure can be timely detected and the caused economic loss can be reduced.
Qiaodi Wen, Ziqi Luo, Ruitao Chen, Yifan Yang, Guofa Li   +4 more
doaj   +1 more source

3D convolutional neural network for schizophrenia detection using as EEG-based functional brain network [PDF]

Schizophrenia (ScZ) is a chronic mental disorder affecting the function of the brain, which causes emotional, social, and cognitive problems.
Wen, Peng, Shen, Mingkan, Li, Yan, Song, Bo   +3 more
core   +1 more source

Deep Convolutional Neural Network Algorithm for Prediction of the Mechanical Properties of Friction Stir Welded Copper Joints from its Microstructures

, 2021
Convolutional Neural Network (CNN) is a special type of Artificial Neural Network which takes input in the form of an image. Like Artificial Neural Network they consist of weights that are estimated during training, neurons (activation functions), and an
Asmita, Suman, Akshansh, Mishra
core   +1 more source

Prediction of Surface Topography Parameters in Direct Laser Interference Patterning of Stainless Steel Using Infrared Monitoring and Convolutional Neural Networks

Advanced Engineering Materials, EarlyView.
This study presents an infrared monitoring approach for direct laser interference patterning (DLIP) combined with a convolutional neural network (CNN). Thermal emission data captured during structuring are used to predict surface topography parameters.
Lukas Olawsky, Marcelo Sallese, Leander Kläber, Clemens Kuhn, Keming Du, Andrés Fabián Lasagni   +5 more
wiley   +1 more source

Structural Damage Detection Based on One-Dimensional Convolutional Neural Network

Applied Sciences, 2022
This paper proposes a structural damage detection method based on one-dimensional convolutional neural network (CNN). The method can automatically extract features from data to detect structural damage.
Zhigang Xue, Chenxu Xu, Dongdong Wen
doaj   +1 more source