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 +4 more
doaj +1 more source
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
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
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
Deep Neural Network Architectures for Modulation Classification
, 2018 In this work, we investigate the value of employing deep learning for the task of wireless signal modulation recognition. Recently in [1], a framework has been introduced by generating a dataset using GNU radio that mimics the imperfections in a real ...
Gamal, Aly El, Liu, Xiaoyu, Yang, Diyu
core +1 more source
A Method Based on GA-CNN-LSTM for Daily Tourist Flow Prediction at Scenic Spots
Entropy, 2020 Accurate tourist flow prediction is key to ensuring the normal operation of popular scenic spots. However, one single model cannot effectively grasp the characteristics of the data and make accurate predictions because of the strong nonlinear ...
Wenxing Lu +5 more
doaj +1 more source
CNN-Siam: multimodal siamese CNN-based deep learning approach for drug‒drug interaction prediction
BMC Bioinformatics, 2023 Background Drug‒drug interactions (DDIs) are reactions between two or more drugs, i.e., possible situations that occur when two or more drugs are used simultaneously. DDIs act as an important link in both drug development and clinical treatment. Since it
Zihao Yang +5 more
doaj +1 more source
ViP-CNN: Visual Phrase Guided Convolutional Neural Network
, 2017 As the intermediate level task connecting image captioning and object detection, visual relationship detection started to catch researchers' attention because of its descriptive power and clear structure.
Li, Yikang +3 more
core +1 more source
SANet: Structure-Aware Network for Visual Tracking
, 2017 Convolutional neural network (CNN) has drawn increasing interest in visual tracking owing to its powerfulness in feature extraction. Most existing CNN-based trackers treat tracking as a classification problem.
Fan, Heng, Ling, Haibin
core +1 more source
RUL prediction of lithium ion battery based on CEEMDAN-CNN BiLSTM model
Energy Reports, 2023 With the wide application of lithium ion batteries, the importance of life prediction is also highlighted. The prediction of the remaining life of lithium ion battery is an important part of its health management, and accurate prediction can improve the ...
Xifeng Guo +4 more
doaj +1 more source