Results 31 to 40 of about 293,560 (163)

Convolutional neural networks: an overview and application in radiology

Insights into Imaging, 2018
Convolutional neural network (CNN), a class of artificial neural networks that has become dominant in various computer vision tasks, is attracting interest across a variety of domains, including radiology.
Rikiya Yamashita, Mizuho Nishio, Richard Kinh Gian Do, Kaori Togashi   +3 more
doaj   +1 more source

Sub-Millisecond Phase Retrieval for Phase-Diversity Wavefront Sensor

Sensors, 2020
We propose a convolutional neural network (CNN) based method, namely phase diversity convolutional neural network (PD-CNN) for the speed acceleration of phase-diversity wavefront sensing.
Yu Wu, Youming Guo, Hua Bao, Changhui Rao   +3 more
doaj   +1 more source

Research on epileptic EEG recognition based on improved residual networks of 1-D CNN and indRNN

BMC Medical Informatics and Decision Making, 2021
Background Epilepsy is one of the diseases of the nervous system, which has a large population in the world. Traditional diagnosis methods mostly depended on the professional neurologists’ reading of the electroencephalogram (EEG), which was time ...
Mengnan Ma, Yinlin Cheng, Xiaoyan Wei, Ziyi Chen, Yi Zhou   +4 more
doaj   +1 more source

SPEAKER IDENTIFICATION SYSTEM USING AUDIO SIGNAL AND DEEP LEARNING METHOD [PDF]

Proceedings on Engineering Sciences
Automatic Speaker Identification (ASI) does not result in high accuracy, so it is essential to develop a highly accurate Speaker Identification (SI) system. Artificial Intelligence has shown remarkable improvement in the development of such systems using
Neelam Nehra , Pardeep Sangwan , Neelu Trivedi   +2 more
doaj   +1 more source

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

Improved Handwritten Digit Recognition Using Convolutional Neural Networks (CNN) [PDF]

Sensors, 2020
Traditional systems of handwriting recognition have relied on handcrafted features and a large amount of prior knowledge. Training an Optical character recognition (OCR) system based on these prerequisites is a challenging task. Research in the handwriting recognition field is focused around deep learning techniques and has achieved breakthrough ...
Savita Ahlawat, Amit Choudhary, Anand Nayyar, Saurabh Singh, Byungun Yoon   +4 more
openaire   +3 more sources

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

Tube Convolutional Neural Network (T-CNN) for Action Detection in Videos

, 2017
Deep learning has been demonstrated to achieve excellent results for image classification and object detection. However, the impact of deep learning on video analysis (e.g.
Chen, Chen, Hou, Rui, Shah, Mubarak
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

Topical Behavior Prediction from Massive Logs

, 2017
In this paper, we study the topical behavior in a large scale. We use the network logs where each entry contains the entity ID, the timestamp, and the meta data about the activity.
Su, Shih-Chieh
core   +1 more source