scholarly journals A Novel Prediction Method for ATP-Binding Sites From Protein Primary Sequences Based on Fusion of Deep Convolutional Neural Network and Ensemble Learning

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 21485-21495
Author(s):  
Jiazhi Song ◽  
Yanchun Liang ◽  
Guixia Liu ◽  
Rongquan Wang ◽  
Liyan Sun ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Ensemble Learning ◽  
Binding Sites ◽  
Prediction Method ◽  
Deep Convolutional Neural Network ◽  
Atp Binding

Prediction of ATP-binding sites in membrane proteins using a two-dimensional convolutional neural network

2019 ◽  
Vol 92 ◽  
pp. 86-93 ◽  
Author(s):  
Trinh-Trung-Duong Nguyen ◽  
Nguyen-Quoc-Khanh Le ◽  
Rosdyana Mangir Irawan Kusuma ◽  
Yu-Yen Ou
Keyword(s):  
Neural Network ◽  
Membrane Proteins ◽  
Convolutional Neural Network ◽  
Binding Sites ◽  
Atp Binding ◽  
Two Dimensional

scholarly journals A RUL prediction method of equipments based on MSDCNN-LSTM

2021 ◽  
Vol 39 (2) ◽  
pp. 407-413
Author(s):  
Chang Liu ◽  
Wenbai Chen
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Short Term Memory ◽  
Prediction Method ◽  
Remaining Useful Life ◽  
Deep Convolutional Neural Network ◽  
Short Term ◽  
Term Memory ◽  
Multi Scale ◽  
Long Short Term Memory

In order to solve the problems of high data dimension and insufficient consideration of time series correlation information, a multi-scale deep convolutional neural network and long-short-term memory (MSDCNN-LSTM) hybrid model is proposed for remaining useful life (RUL) of equipments. First, the sensor data is processed through normalization and sliding time window to obtain input samples; then multi-scale deep convolutional neural network (MSDCNN) is used to capture detailed spatial features, at the same time, time-dependent features are extracted for effective prediction combining with long short-term memory (LSTM). Experiments on simulation dataset of commercial modular aero-propulsion system show that, compared with other state-of-the-art methods, the prediction method proposed in this paper has achieved better RUL prediction results, especially for the prediction of the life of equipment with complex failure modes and operating conditions. The effect is obvious. It can be seen that the prediction method proposed in this paper is feasible and effective.

scholarly journals Deep Convolutional Neural Network with Structured Prediction for Weakly Supervised Audio Event Detection

2019 ◽  
Vol 9 (11) ◽  
pp. 2302 ◽  
Author(s):  
Inkyu Choi ◽  
Soo Hyun Bae ◽  
Nam Soo Kim
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Event Detection ◽  
Large Scale ◽  
Prediction Method ◽  
Deep Convolutional Neural Network ◽  
Structured Prediction ◽  
Temporal Position ◽  
Audio Event ◽  
Weakly Supervised

Audio event detection (AED) is a task of recognizing the types of audio events in an audio stream and estimating their temporal positions. AED is typically based on fully supervised approaches, requiring strong labels including both the presence and temporal position of each audio event. However, fully supervised datasets are not easily available due to the heavy cost of human annotation. Recently, weakly supervised approaches for AED have been proposed, utilizing large scale datasets with weak labels including only the occurrence of events in recordings. In this work, we introduce a deep convolutional neural network (CNN) model called DSNet based on densely connected convolution networks (DenseNets) and squeeze-and-excitation networks (SENets) for weakly supervised training of AED. DSNet alleviates the vanishing-gradient problem and strengthens feature propagation and models interdependencies between channels. We also propose a structured prediction method for weakly supervised AED. We apply a recurrent neural network (RNN) based framework and a prediction smoothness cost function to consider long-term contextual information with reduced error propagation. In post-processing, conditional random fields (CRFs) are applied to take into account the dependency between segments and delineate the borders of audio events precisely. We evaluated our proposed models on the DCASE 2017 task 4 dataset and obtained state-of-the-art results on both audio tagging and event detection tasks.

scholarly journals Predicting protein-peptide binding sites with a deep convolutional neural network

2020 ◽  
Vol 496 ◽  
pp. 110278 ◽  
Author(s):  
Wafaa Wardah ◽  
Abdollah Dehzangi ◽  
Ghazaleh Taherzadeh ◽  
Mahmood A. Rashid ◽  
M.G.M. Khan ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Binding Sites ◽  
Peptide Binding ◽  
Deep Convolutional Neural Network

scholarly journals Link Prediction Based on Deep Convolutional Neural Network

Information ◽  
2019 ◽  
Vol 10 (5) ◽  
pp. 172
Author(s):  
Wentao Wang ◽  
Lintao Wu ◽  
Ye Huang ◽  
Hao Wang ◽  
Rongbo Zhu
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Link Prediction ◽  
Prediction Method ◽  
Representation Learning ◽  
Deep Convolutional Neural Network ◽  
Published Data ◽  
Neighborhood Structure ◽  
Structure Information ◽  
Network Representation

In recent years, endless link prediction algorithms based on network representation learning have emerged. Network representation learning mainly constructs feature vectors by capturing the neighborhood structure information of network nodes for link prediction. However, this type of algorithm only focuses on learning topology information from the simple neighbor network node. For example, DeepWalk takes a random walk path as the neighborhood of nodes. In addition, such algorithms only take advantage of the potential features of nodes, but the explicit features of nodes play a good role in link prediction. In this paper, a link prediction method based on deep convolutional neural network is proposed. It constructs a model of the residual attention network to capture the link structure features from the sub-graph. Further study finds that the information flow transmission efficiency of the residual attention mechanism was not high, so a densely convolutional neural network model was proposed for link prediction. We evaluate our proposed method on four published data sets. The results show that our method is better than several other benchmark algorithms on link prediction.

LENS CLASSIFICATION ACCORDING TO THE TYPE OF LIGHT SPOT USING A NEURAL NETWORK

2020 ◽  
Vol 2020 (4) ◽  
pp. 4-14
Author(s):  
Vladimir Budak ◽  
Ekaterina Ilyina
Keyword(s):  
Neural Network ◽  
Light Intensity ◽  
Convolutional Neural Network ◽  
Deep Convolutional Neural Network ◽  
Beam Angle ◽  
New Model ◽  
Technical Parameters ◽  
Transfer Training ◽  
Trained Network

The article proposes the classification of lenses with different symmetrical beam angles and offers a scale as a spot-light’s palette. A collection of spotlight’s images was created and classified according to the proposed scale. The analysis of 788 pcs of existing lenses and reflectors with different LEDs and COBs carried out, and the dependence of the axial light intensity from beam angle was obtained. A transfer training of new deep convolutional neural network (CNN) based on the pre-trained GoogleNet was performed using this collection. GradCAM analysis showed that the trained network correctly identifies the features of objects. This work allows us to classify arbitrary spotlights with an accuracy of about 80 %. Thus, light designer can determine the class of spotlight and corresponding type of lens with its technical parameters using this new model based on CCN.

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