scholarly journals DLFF-ACP: prediction of ACPs based on deep learning and multi-view features fusion

PeerJ ◽  
2021 ◽  
Vol 9 ◽  
pp. e11906
Author(s):  
Ruifen Cao ◽  
Meng Wang ◽  
Yannan Bin ◽  
Chunhou Zheng
Keyword(s):  
Neural Network ◽  
Feature Fusion ◽  
Predictive Ability ◽  
Acid Group ◽  
The Other ◽  
Neural Network Ensemble ◽  
Amino Acid Group ◽  
Test Set ◽  
Dual Channel ◽  
Fusion Features

An emerging type of therapeutic agent, anticancer peptides (ACPs), has attracted attention because of its lower risk of toxic side effects. However process of identifying ACPs using experimental methods is both time-consuming and laborious. In this study, we developed a new and efficient algorithm that predicts ACPs by fusing multi-view features based on dual-channel deep neural network ensemble model. In the model, one channel used the convolutional neural network CNN to automatically extract the potential spatial features of a sequence. Another channel was used to process and extract more effective features from handcrafted features. Additionally, an effective feature fusion method was explored for the mutual fusion of different features. Finally, we adopted the neural network to predict ACPs based on the fusion features. The performance comparisons across the single and fusion features showed that the fusion of multi-view features could effectively improve the model’s predictive ability. Among these, the fusion of the features extracted by the CNN and composition of k-spaced amino acid group pairs achieved the best performance. To further validate the performance of our model, we compared it with other existing methods using two independent test sets. The results showed that our model’s area under curve was 0.90, which was higher than that of the other existing methods on the first test set and higher than most of the other existing methods on the second test set. The source code and datasets are available at https://github.com/wame-ng/DLFF-ACP.

scholarly journals Hand-drawn sketch recognition with a double-channel convolutional neural network

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Lei Zhang
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Feature Fusion ◽  
Recognition Rate ◽  
Input Image ◽  
Contour Extraction ◽  
Sketch Recognition ◽  
Softmax Classifier ◽  
Dual Channel ◽  
Extraction Algorithm

AbstractIn hand-drawn sketch recognition, the traditional deep learning method has the problems of insufficient feature extraction and low recognition rate. To solve this problem, a new algorithm based on a dual-channel convolutional neural network is proposed. Firstly, the sketch is preprocessed to get a smooth sketch. The contour of the sketch is obtained by the contour extraction algorithm. Then, the sketch and contour are used as the input image of CNN. Finally, feature fusion is carried out in the full connection layer, and the classification results are obtained by using a softmax classifier. Experimental results show that this method can effectively improve the recognition rate of a hand-drawn sketch.

scholarly journals Environmental sound classification based on feature fusion

2018 ◽  
Vol 173 ◽  
pp. 03059
Author(s):  
Huimin Zhao ◽  
Xianglin Huang ◽  
Wei Liu ◽  
Lifang Yang
Keyword(s):  
Deep Learning ◽  
Classification Accuracy ◽  
Feature Fusion ◽  
Fusion Method ◽  
Environmental Sound ◽  
Environmental Sounds ◽  
Test Set ◽  
Sound Classification ◽  
Music Recognition ◽  
Fusion Features

With deep great breakthroughs of deep learning in the field of computer vision, the field of audio recognition has gradually introduced deep learning methods and achieved excellent results. These results are mainly for speech and music recognition research, and there is very little research on environmental sound classification. In recent years, people have begun to expand the research object of deep learning to the environmental sound, and achieved certain results. In this paper, we use ESC-50 as our test set, based on the SoundNet network and EnvNet network to propose a feature fusion method[1]. After the features extracted by SoundNet and EnvNet were merged, they were classified using fusion features. Experimental results show that this method has better classification accuracy for the recognition of environmental sounds than using either of the two networks separately for classification.

Neural Network Ensemble Approach to Pushed Convoys Dispatching Problems

2020 ◽  
Vol 27 (1) ◽  
pp. 70-82 ◽  
Author(s):  
Aleksandar Radonjić ◽  
Danijela Pjevčević ◽  
Vladislav Maraš
Keyword(s):  
Neural Network ◽  
Decision Support Tool ◽  
Primary Objective ◽  
Danube River ◽  
Decision Making Process ◽  
Neural Network Ensemble ◽  
Support Tool ◽  
Shipping Company ◽  
The Republic ◽  
Power Measurements

AbstractThis paper investigates the use of neural networks (NNs) for the problem of assigning push boats to barge convoys in inland waterway transportation (IWT). Push boat–barge convoy assignmentsare part of the daily decision-making process done by dispatchers in IWT companiesforwhich a decision support tool does not exist. The aim of this paper is to develop a Neural Network Ensemble (NNE) model that will be able to assist in push boat–barge convoy assignments based on the push boat power.The primary objective of this paper is to derive an NNE model for calculation of push boat Shaft Powers (SHPs) by using less than 100% of the experimental data available. The NNE model is applied to a real-world case of more than one shipping company from the Republic of Serbia, which is encountered on the Danube River. The solution obtained from the NNE model is compared toreal-world full-scale speed/power measurements carried out on Serbian push boats, as well as with the results obtained from the previous NNE model. It is found that the model is highly accurate, with scope for further improvements.

Key phrase Extraction by Improving TextRank with an Integration of Word Embedding and Syntactic Information

2020 ◽  
Vol 13 ◽  
Author(s):  
Sheng Zhang ◽  
Qi Luo ◽  
Yukun Feng ◽  
Ke Ding ◽  
Daniela Gifu ◽  
...  
Keyword(s):  
Semantic Information ◽  
Performance Enhancement ◽  
Word Embedding ◽  
The Other ◽  
Test Set ◽  
Pagerank Algorithm ◽  
Phrase Extraction ◽  
Extraction Algorithm ◽  
Syntactic Information ◽  
Key Phrase Extraction

Background: As a known key phrase extraction algorithm, TextRank is an analogue of PageRank algorithm, which relied heavily on the statistics of term frequency in the manner of co-occurrence analysis. Objective: The frequency-based characteristic made it a neck-bottle for performance enhancement, and various improved TextRank algorithms were proposed in the recent years. Most of improvements incorporated semantic information into key phrase extraction algorithm and achieved improvement. Method: In this research, taking both syntactic and semantic information into consideration, we integrated syntactic tree algorithm and word embedding and put forward an algorithm of Word Embedding and Syntactic Information Algorithm (WESIA), which improved the accuracy of the TextRank algorithm. Results: By applying our method on a self-made test set and a public test set, the result implied that the proposed unsupervised key phrase extraction algorithm outperformed the other algorithms to some extent.

scholarly journals A Novel Method for Credit Scoring based on Cost-sensitive Neural Network Ensemble

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Wirot Yotsawat ◽  
Pakaket Wattuya ◽  
Anongnart Srivihok
Keyword(s):  
Neural Network ◽  
Credit Scoring ◽  
Neural Network Ensemble ◽  
Novel Method

A Siamese convolutional neural network with high–low level feature fusion for change detection in remotely sensed images

2021 ◽  
Vol 12 (4) ◽  
pp. 387-396
Author(s):  
Hao Zhou ◽  
Mi Zhang ◽  
Xiangyun Hu ◽  
Kun Li ◽  
Jing Sun
Keyword(s):  
Neural Network ◽  
Change Detection ◽  
Convolutional Neural Network ◽  
Feature Fusion ◽  
Remotely Sensed ◽  
Low Level ◽  
Remotely Sensed Images

An improved deep convolution neural network for predicting the remaining useful life of rolling bearings

2020 ◽  
pp. 1-9
Author(s):  
Yiming Guo ◽  
Hui Zhang ◽  
Zhijie Xia ◽  
Chang Dong ◽  
Zhisheng Zhang ◽  
...  
Keyword(s):  
Neural Network ◽  
Degradation Process ◽  
Rolling Bearing ◽  
Remaining Useful Life ◽  
Convolution Neural Network ◽  
Dual Channel ◽  
Time Domain Data ◽  
Channel Input ◽  
Deep Convolution Neural Network ◽  
Useful Life

The rolling bearing is the crucial component in the rotating machinery. The degradation process monitoring and remaining useful life prediction of the bearing are necessary for the condition-based maintenance. The commonly used deep learning methods use the raw or processed time domain data as the input. However, the feature extracted by these approaches is insufficient and incomprehensive. To tackle this problem, this paper proposed an improved Deep Convolution Neural Network with the dual-channel input from the time and frequency domain in parallel. The proposed methodology consists of two stages: the incipient failure identification and the degradation process fitting. To verify the effectiveness of the method, the IEEE PHM 2012 dataset is adopted to compare the proposed method and other commonly used approaches. The results show that the improved Deep Convolution Neural Network can effectively describe the degradation process for the rolling bearing.

scholarly journals Deep neural network ensemble for on-the-fly quality control-driven segmentation of cardiac MRI T1 mapping

2021 ◽  
Vol 71 ◽  
pp. 102029
Author(s):  
Evan Hann ◽  
Iulia A. Popescu ◽  
Qiang Zhang ◽  
Ricardo A. Gonzales ◽  
Ahmet Barutçu ◽  
...  
Keyword(s):  
Neural Network ◽  
Quality Control ◽  
Cardiac Mri ◽  
Deep Neural Network ◽  
T1 Mapping ◽  
Neural Network Ensemble

scholarly journals Repeatability, Completion Time, and Predictive Ability of Four Diabetes-Related Foot Ulcer Classification Systems

2021 ◽  
pp. 193229682098654
Author(s):  
Chanika Alahakoon ◽  
Malindu Fernando ◽  
Charith Galappaththy ◽  
Peter Lazzarini ◽  
Joseph V. Moxon ◽  
...  
Keyword(s):  
Foot Ulcer ◽  
Predictive Ability ◽  
Classification Systems ◽  
The Other ◽  
Observer Agreement ◽  
Foot Ulcers ◽  
Substantial Agreement ◽  
University Of Texas ◽  
Trained Observers ◽  
Completion Times

Introduction: The inter and intra-observer reproducibility of measuring the Wound Ischemia foot Infection (WIfI) score is unknown. The aims of this study were to compare the reproducibility, completion times and ability to predict 30-day amputation of the WIfI, University of Texas Wound Classification System (UTWCS), Site, Ischemia, Neuropathy, Bacterial Infection and Depth (SINBAD) and Wagner classifications systems using photographs of diabetes-related foot ulcers. Methods: Three trained observers independently scored the diabetes-related foot ulcers of 45 participants on two separate occasions using photographs. The inter- and intra-observer reproducibility were calculated using Krippendorff’s α. The completion times were compared with Kruskal-Wallis and Dunn’s post-hoc tests. The ability of the scores to predict 30-day amputation rates were assessed using receiver operator characteristic curves and area under the curves. Results: There was excellent intra-observer agreement (α >0.900) and substantial agreement between observers (α=0.788) in WIfI scoring. There was moderate, substantial, or excellent agreement within the three observers (α>0.599 in all instances except one) and fair or moderate agreement between observers (α of UTWCS=0.306, α of SINBAD=0.516, α of Wagner=0.374) for the other three classification systems. The WIfI score took significantly longer ( P<.001) to complete compared to the other three scores (medians and inter quartile ranges of the WIfI, UTWCS, SINBAD, and Wagner being 1.00 [0.88-1.00], 0.75 [0.50-0.75], 0.50 [0.50-0.50], and 0.25 [0.25-0.50] minutes). None of the classifications were predictive of 30-day amputation ( P>.05 in all instances). Conclusion: The WIfI score can be completed with substantial agreement between trained observers but was not predictive of 30-day amputation.

scholarly journals Application of Artificial Neural Networks to Predict Beach Nourishment Volume Requirements

2021 ◽  
Vol 9 (8) ◽  
pp. 786
Author(s):  
Damjan Bujak ◽  
Tonko Bogovac ◽  
Dalibor Carević ◽  
Suzana Ilic ◽  
Goran Lončar
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Spatial Variability ◽  
Coastal Management ◽  
Wind Wave ◽  
Predictive Ability ◽  
Test Set ◽  
Adriatic Coast ◽  
Important Input ◽  
Artificial Neural

The volume of material required for the construction of new and expansion of existing beach sites is an important parameter for coastal management. This information may play a crucial role when deciding which beach sites to develop. This work examines whether artificial neural networks (ANNs) can predict the spatial variability of nourishment requirements on the Croatian coast. We use survey data of the nourishment volume requirements and gravel diameter from 2016 to 2020, fetch length, beach area and orientation derived from national maps which vary from location to location due to a complex coastal configuration on the East Adriatic coast, and wind, tide, and rainfall data from nearby meteorological/oceanographic stations to train and test ANNs. The results reported here confirm that an ANN can adequately predict the spatial variability of observed nourishment volumes (R and MSE for the test set equal 0.87 and 2.24 × 104, respectively). The contributions of different parameters to the ANN’s predictive ability were examined. Apart from the most obvious parameters like the beach length and the beach areas, the fetch length proved to be the most important input contribution to ANN’s predictive ability, followed by the beach orientation. Fetch length and beach orientation are parameters governing the wind wave height and direction and hence are proxies for forcing.

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