Results 71 to 80 of about 2,004,297 (387)
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
core +1 more source
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
Advanced Materials Interfaces, EarlyView., 2023 This review discusses the use of Surface‐Enhanced Raman Spectroscopy (SERS) combined with Artificial Intelligence (AI) for detecting antimicrobial resistance (AMR). Various SERS studies used with AI techniques, including machine learning and deep learning, are analyzed for their advantages and limitations.
Zakarya Al‐Shaebi +4 more
wiley +1 more source
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
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
Fault Diagnosis of Rotating Machinery Based on Evolutionary Convolutional Neural Network
Shock and Vibration, 2022 This paper proposes a fault diagnosis method for rotating machinery based on evolutionary convolutional neural network (ECNN). With the time-frequency images as the network input, with the help of the global optimization ability of the genetic algorithm,
Yihao Bai +3 more
doaj +1 more source
Despeckling of SAR Images Using Residual Twin CNN and Multi-Resolution Attention Mechanism
Remote Sensing, 2023 The despeckling of synthetic aperture radar images using two different convolutional neural network architectures is presented in this paper. The first method presents a novel Siamese convolutional neural network with a dilated convolutional network in ...
Blaž Pongrac, Dušan Gleich
doaj +1 more source
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
core +1 more source
Long Short-Term Memory Spatial Transformer Network
, 2019 Spatial transformer network has been used in a layered form in conjunction with a convolutional network to enable the model to transform data spatially.
Chen, Tianyue, Feng, Shiyang, Sun, Hao
core +1 more source
Mesh-based graph convolutional neural networks for modeling materials with microstructure [PDF]
arXiv, 2021 Predicting the evolution of a representative sample of a material with microstructure is a fundamental problem in homogenization. In this work we propose a graph convolutional neural network that utilizes the discretized representation of the initial microstructure directly, without segmentation or clustering.
arxiv