Results 41 to 50 of about 534,506 (301)

Application of Deep Learning in the Prediction of Benign and Malignant Thyroid Nodules on Ultrasound Images

IEEE Access, 2020
In this paper, ultrasound imaging of benign and malignant thyroid nodules to predict the depth of the learning algorithm, built on circulation volume product thyroid ultrasound image neural network forecasting model.
Yinghui Lu, Yi Yang, Wan Chen
doaj   +1 more source

Sequential Convolutional Recurrent Neural Networks for Fast Automatic Modulation Classification

IEEE Access, 2021
A novel and efficient end-to-end learning model for automatic modulation classification is proposed for wireless spectrum monitoring applications, which automatically learns from the time domain in-phase and quadrature data without requiring the design ...
Kaisheng Liao, Yaodong Zhao, Jie Gu, Yaping Zhang, Yi Zhong   +4 more
doaj   +1 more source

Deep neural networks for direct, featureless learning through observation: the case of 2d spin models

, 2018
We demonstrate the capability of a convolutional deep neural network in predicting the nearest-neighbor energy of the 4x4 Ising model. Using its success at this task, we motivate the study of the larger 8x8 Ising model, showing that the deep neural ...
Mills, Kyle, Tamblyn, Isaac
core   +1 more source

Cervical Spinal Cord Magnetization Transfer Ratio and Its Relationship With Clinical Outcomes in Multiple Sclerosis

Annals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Objective The cervical spinal cord (cSC) is highly relevant to clinical dysfunction in multiple sclerosis (MS) but remains understudied using quantitative magnetic resonance imaging (MRI). We assessed magnetization transfer ratio (MTR), a semi‐quantitative MRI measure sensitive to MS‐related tissue microstructural changes, in the cSC and its ...
Lisa Eunyoung Lee, Julien Cohen‐Adad, Irene M. Vavasour, Melanie Guenette, Katherine Sawicka, Neda Rashidi‐Ranjbar, Nathan Churchill, Akash Chopra, Adelia Adelia, Pierre‐Louis Benveniste, Anthony Traboulsee, Nathalie Arbour, Fabrizio Giuliani, Larry D. Lynd, Scott B. Patten, Alexandre Prat, Alice Schabas, Penelope Smyth, Roger Tam, Yunyan Zhang, Simon J. Graham, Mojgan Hodaie, Anthony Feinstein, Shannon Kolind, Tom A. Schweizer, Jiwon Oh, CanProCo Study Group   +26 more
wiley   +1 more source

ICU‐EEG Pattern Detection by a Convolutional Neural Network

Annals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Objective Patients in the intensive care unit (ICU) often require continuous EEG (cEEG) monitoring due to the high risk of seizures and rhythmic and periodic patterns (RPPs). However, interpreting cEEG in real time is resource‐intensive and heavily relies on specialized expertise, which is not always available.
Giulio Degano, Hervé Quintard, Andreas Kleinschmidt, Nikita Francini, Oana E. Sarbu, Pia De Stefano   +5 more
wiley   +1 more source

An Optimized Convolutional Neural Network for the 3D Point-Cloud Compression

Sensors, 2023
Due to the tremendous volume taken by the 3D point-cloud models, knowing how to achieve the balance between a high compression ratio, a low distortion rate, and computing cost in point-cloud compression is a significant issue in the field of virtual ...
Guoliang Luo, Bingqin He, Yanbo Xiong, Luqi Wang, Hui Wang, Zhiliang Zhu, Xiangren Shi   +6 more
doaj   +1 more source

Dependency-based Convolutional Neural Networks for Sentence Embedding

, 2015
In sentence modeling and classification, convolutional neural network approaches have recently achieved state-of-the-art results, but all such efforts process word vectors sequentially and neglect long-distance dependencies. To exploit both deep learning
Huang, Liang, Ma, Mingbo, Xiang, Bing, Zhou, Bowen   +3 more
core   +1 more source

Detecting Dengue in Flight: Leveraging Machine Learning to Analyze Mosquito Flight Patterns for Infection Detection

Advanced Biology, EarlyView.
Dengue infection alters mosquito flight behavior, enabling detection using machine learning classifiers. This study analyzes 3D flight trajectories and evaluates multiple models, showing that longer sequence lengths improve classification performance.
Nouman Javed, Adam J. López‐Denman, Prasad N. Paradkar, Asim Bhatti   +3 more
wiley   +1 more source

Research onconvolutional neural network for reservoir parameter prediction

Tongxin xuebao, 2016
As the branch of artificial intelligence,artificial neural network solved many difficult practical problems in pattern recognition and classification prediction field successfully.However,they cannot learn the feature from networks.In recent years,deep ...
You-xiang DUAN, Gen-tian LI, Qi-feng SUN
doaj   +2 more sources

Research on road extraction of remote sensing image based on convolutional neural network

EURASIP Journal on Image and Video Processing, 2019
Road is an important kind of basic geographic information. Road information extraction plays an important role in traffic management, urban planning, automatic vehicle navigation, and emergency management.
Yuantao Jiang
doaj   +1 more source