Results 41 to 50 of about 849,090 (345)
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
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A graph neural network-enhanced knowledge graph framework for intelligent analysis of policing cases
Mathematical Biosciences and Engineering, 2023 In this paper, we model a knowledge graph based on graph neural networks, conduct an in-depth study on building knowledge graph embeddings for policing cases, and design a graph neural network-enhanced knowledge graph framework.
Hongqiang Zhu
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Toward Understanding Convolutional Neural Networks from Volterra Convolution Perspective [PDF]
arXiv, 2021 We make an attempt to understanding convolutional neural network by exploring the relationship between (deep) convolutional neural networks and Volterra convolutions. We propose a novel approach to explain and study the overall characteristics of neural networks without being disturbed by the horribly complex architectures.
arxiv
INVESTIGATIONS ON THE POTENTIAL OF CONVOLUTIONAL NEURAL NETWORKS FOR VEHICLE CLASSIFICATION BASED ON RGB AND LIDAR DATA [PDF]
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2017 In recent years, there has been a significant improvement in the detection, identification and classification of objects and images using Convolutional Neural Networks.
R. Niessner, H. Schilling, B. Jutzi
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Orthogonal Features Extraction Method and Its Application in Convolution Neural Network
Shanghai Jiaotong Daxue xuebao, 2021 In view of feature redundancy in the convolutional neural network, the concept of orthogonal vectors is introduced into features. Then, a method for orthogonal features extraction of convolutional neural network is proposed from the perspective of ...
LI Chen, LI Jianxun
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Adverse Drug Reaction Classification With Deep Neural Networks [PDF]
, 2016 We study the problem of detecting sentences describing adverse drug reactions (ADRs) and frame the problem as binary classification. We investigate different neural network (NN) architectures for ADR classification.
He, Yulan +3 more
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FEBS Open Bio, EarlyView.This review highlights how foundation models enhance predictive healthcare by integrating advanced digital twin modeling with multiomics and biomedical data. This approach supports disease management, risk assessment, and personalized medicine, with the goal of optimizing health outcomes through adaptive, interpretable digital simulations, accessible ...
Sakhaa Alsaedi +2 more
wiley +1 more source
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
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Regularization for convolutional kernel tensors to avoid unstable gradient problem in convolutional neural networks [PDF]
arXiv, 2021 Convolutional neural networks are very popular nowadays. Training neural networks is not an easy task. Each convolution corresponds to a structured transformation matrix. In order to help avoid the exploding/vanishing gradient problem, it is desirable that the singular values of each transformation matrix are not large/small in the training process. We
arxiv
Learning text representation using recurrent convolutional neural network with highway layers [PDF]
, 2016 Recently, the rapid development of word embedding and neural networks has brought new inspiration to various NLP and IR tasks. In this paper, we describe a staged hybrid model combining Recurrent Convolutional Neural Networks (RCNN) with highway layers ...
Luo, Rui +3 more
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