scholarly journals Motif-Matching Based Subgraph-Level Attentional Convolutional Network for Graph Classification

2020 ◽  
Vol 34 (04) ◽  
pp. 5387-5394
Author(s):  
Hao Peng ◽  
Jianxin Li ◽  
Qiran Gong ◽  
Yuanxin Ning ◽  
Senzhang Wang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Social Network ◽  
Spatial Information ◽  
Structural Information ◽  
Feature Learning ◽  
Drug Analysis ◽  
Classification Performance ◽  
Learning Approaches ◽  
Graph Classification ◽  
Convolutional Network

Graph classification is critically important to many real-world applications that are associated with graph data such as chemical drug analysis and social network mining. Traditional methods usually require feature engineering to extract the graph features that can help discriminate the graphs of different classes. Although recently deep learning based graph embedding approaches are proposed to automatically learn graph features, they mostly use a few vertex arrangements extracted from the graph for feature learning, which may lose some structural information. In this work, we present a novel motif-based attentional graph convolution neural network for graph classification, which can learn more discriminative and richer graph features. Specifically, a motif-matching guided subgraph normalization method is developed to better preserve the spatial information. A novel subgraph-level self-attention network is also proposed to capture the different impacts or weights of different subgraphs. Experimental results on both bioinformatics and social network datasets show that the proposed models significantly improve graph classification performance over both traditional graph kernel methods and recent deep learning approaches.

scholarly journals Change Detection in Hyperspectral Images Using Recurrent 3D Fully Convolutional Networks

2018 ◽  
Vol 10 (11) ◽  
pp. 1827 ◽  
Author(s):  
Ahram Song ◽  
Jaewan Choi ◽  
Youkyung Han ◽  
Yongil Kim
Keyword(s):  
Deep Learning ◽  
Change Detection ◽  
Spatial Information ◽  
Short Term Memory ◽  
Hyperspectral Images ◽  
Convolutional Network ◽  
Ground Truth Data ◽  
Fully Convolutional Network ◽  
Training Samples ◽  
Multi Temporal

Hyperspectral change detection (CD) can be effectively performed using deep-learning networks. Although these approaches require qualified training samples, it is difficult to obtain ground-truth data in the real world. Preserving spatial information during training is difficult due to structural limitations. To solve such problems, our study proposed a novel CD method for hyperspectral images (HSIs), including sample generation and a deep-learning network, called the recurrent three-dimensional (3D) fully convolutional network (Re3FCN), which merged the advantages of a 3D fully convolutional network (FCN) and a convolutional long short-term memory (ConvLSTM). Principal component analysis (PCA) and the spectral correlation angle (SCA) were used to generate training samples with high probabilities of being changed or unchanged. The strategy assisted in training fewer samples of representative feature expression. The Re3FCN was mainly comprised of spectral–spatial and temporal modules. Particularly, a spectral–spatial module with a 3D convolutional layer extracts the spectral–spatial features from the HSIs simultaneously, whilst a temporal module with ConvLSTM records and analyzes the multi-temporal HSI change information. The study first proposed a simple and effective method to generate samples for network training. This method can be applied effectively to cases with no training samples. Re3FCN can perform end-to-end detection for binary and multiple changes. Moreover, Re3FCN can receive multi-temporal HSIs directly as input without learning the characteristics of multiple changes. Finally, the network could extract joint spectral–spatial–temporal features and it preserved the spatial structure during the learning process through the fully convolutional structure. This study was the first to use a 3D FCN and a ConvLSTM for the remote-sensing CD. To demonstrate the effectiveness of the proposed CD method, we performed binary and multi-class CD experiments. Results revealed that the Re3FCN outperformed the other conventional methods, such as change vector analysis, iteratively reweighted multivariate alteration detection, PCA-SCA, FCN, and the combination of 2D convolutional layers-fully connected LSTM.

Graph Convolutional Networks for Exercise Motion Classification

2021 ◽  
Vol 65 (1) ◽  
pp. 685-689
Author(s):  
Parian Haghighat ◽  
Aden Prince ◽  
Heejin Jeong
Keyword(s):  
Nearest Neighbor ◽  
Current Model ◽  
Model Performance ◽  
Classification Performance ◽  
Great Promise ◽  
Graph Classification ◽  
Convolutional Network ◽  
Motion Data ◽  
Convolutional Networks ◽  
Personal Fitness

The growth in self-fitness mobile applications has encouraged people to turn to personal fitness, which entails integrating self-tracking applications with exercise motion data to reduce fatigue and mitigate the risk of injury. The advancements in computer vision and motion capture technologies hold great promise to improve exercise classification performance. This study investigates a supervised deep learning model performance, Graph Convolutional Network (GCN) to classify three workouts using the Azure Kinect device’s motion data. The model defines the skeleton as a graph and combines GCN layers, a readout layer, and multi-layer perceptrons to build an end-to-end framework for graph classification. The model achieves an accuracy of 95.86% in classifying 19,442 frames. The current model exchanges feature information between each joint and its 1-nearest neighbor, which impact fades in graph-level classification. Therefore, a future study on improved feature utilization can enhance the model performance in classifying inter-user exercise variation.

scholarly journals An Efficient Deep Learning Approach to Pneumonia Classification in Healthcare

2019 ◽  
Vol 2019 ◽  
pp. 1-7 ◽  
Author(s):  
Okeke Stephen ◽  
Mangal Sain ◽  
Uchenna Joseph Maduh ◽  
Do-Un Jeong
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Data Augmentation ◽  
Classification Performance ◽  
Learning Approaches ◽  
X Ray ◽  
Chest X Ray

This study proposes a convolutional neural network model trained from scratch to classify and detect the presence of pneumonia from a collection of chest X-ray image samples. Unlike other methods that rely solely on transfer learning approaches or traditional handcrafted techniques to achieve a remarkable classification performance, we constructed a convolutional neural network model from scratch to extract features from a given chest X-ray image and classify it to determine if a person is infected with pneumonia. This model could help mitigate the reliability and interpretability challenges often faced when dealing with medical imagery. Unlike other deep learning classification tasks with sufficient image repository, it is difficult to obtain a large amount of pneumonia dataset for this classification task; therefore, we deployed several data augmentation algorithms to improve the validation and classification accuracy of the CNN model and achieved remarkable validation accuracy.

Deep-DRM: a computational method for identifying disease-related metabolites based on graph deep learning approaches

2020 ◽  
Author(s):  
Tianyi Zhao ◽  
Yang Hu ◽  
Liang Cheng
Keyword(s):  
Deep Learning ◽  
Chemical Structure ◽  
Computational Method ◽  
Learning Approaches ◽  
Convolutional Network ◽  
Functional Changes ◽  
Chemical Structures ◽  
Association Pattern ◽  
Semantic Associations ◽  
Components Analysis

Abstract Motivation: The functional changes of the genes, RNAs and proteins will eventually be reflected in the metabolic level. Increasing number of researchers have researched mechanism, biomarkers and targeted drugs by metabolites. However, compared with our knowledge about genes, RNAs, and proteins, we still know few about diseases-related metabolites. All the few existed methods for identifying diseases-related metabolites ignore the chemical structure of metabolites, fail to recognize the association pattern between metabolites and diseases, and fail to apply to isolated diseases and metabolites. Results: In this study, we present a graph deep learning based method, named Deep-DRM, for identifying diseases-related metabolites. First, chemical structures of metabolites were used to calculate similarities of metabolites. The similarities of diseases were obtained based on their functional gene network and semantic associations. Therefore, both metabolites and diseases network could be built. Next, Graph Convolutional Network (GCN) was applied to encode the features of metabolites and diseases, respectively. Then, the dimension of these features was reduced by Principal components analysis (PCA) with retainment 99% information. Finally, Deep neural network was built for identifying true metabolite-disease pairs (MDPs) based on these features. The 10-cross validations on three testing setups showed outstanding AUC (0.952) and AUPR (0.939) of Deep-DRM compared with previous methods and similar approaches. Ten of top 15 predicted associations between diseases and metabolites got support by other studies, which suggests that Deep-DRM is an efficient method to identify MDPs. Contact: [email protected]. Availability and implementation: https://github.com/zty2009/GPDNN-for-Identify-ing-Disease-related-Metabolites.

scholarly journals Time Series Classification with InceptionFCN

Sensors ◽  
2021 ◽  
Vol 22 (1) ◽  
pp. 157
Author(s):  
Saidrasul Usmankhujaev ◽  
Bunyodbek Ibrokhimov ◽  
Shokhrukh Baydadaev ◽  
Jangwoo Kwon
Keyword(s):  
Time Series ◽  
Deep Learning ◽  
Deep Neural Networks ◽  
Learning Approaches ◽  
Time Series Classification ◽  
Convolutional Network ◽  
Training Time ◽  
Learning Techniques ◽  
Significant Research ◽  
Previous State

Deep neural networks (DNN) have proven to be efficient in computer vision and data classification with an increasing number of successful applications. Time series classification (TSC) has been one of the challenging problems in data mining in the last decade, and significant research has been proposed with various solutions, including algorithm-based approaches as well as machine and deep learning approaches. This paper focuses on combining the two well-known deep learning techniques, namely the Inception module and the Fully Convolutional Network. The proposed method proved to be more efficient than the previous state-of-the-art InceptionTime method. We tested our model on the univariate TSC benchmark (the UCR/UEA archive), which includes 85 time-series datasets, and proved that our network outperforms the InceptionTime in terms of the training time and overall accuracy on the UCR archive.

scholarly journals Deep Learning Approaches for Intrusion Detection

2021 ◽  
pp. 50-64
Author(s):  
Azar Abid Salih ◽  
Siddeeq Y. Ameen ◽  
Subhi R. M. Zeebaree ◽  
Mohammed A. M. Sadeeq ◽  
Shakir Fattah Kak ◽  
...  
Keyword(s):  
Deep Learning ◽  
Intrusion Detection ◽  
Computer Security ◽  
Intrusion Detection System ◽  
Detection System ◽  
Feature Learning ◽  
Detailed Comparison ◽  
Learning Approaches ◽  
Network Intrusion ◽  
Research Problems

Recently, computer networks faced a big challenge, which is that various malicious attacks are growing daily. Intrusion detection is one of the leading research problems in network and computer security. This paper investigates and presents Deep Learning (DL) techniques for improving the Intrusion Detection System (IDS). Moreover, it provides a detailed comparison with evaluating performance, deep learning algorithms for detecting attacks, feature learning, and datasets used to identify the advantages of employing in enhancing network intrusion detection.

scholarly journals On Training Targets for Deep Learning Approaches to Clean Speech Magnitude Spectrum Estimation

2020 ◽  
Author(s):  
Aaron Nicolson ◽  
Kuldip K. Paliwal
Keyword(s):  
Deep Learning ◽  
Minimum Mean Square Error ◽  
Auditory Scene Analysis ◽  
Spectrum Estimation ◽  
Learning Approaches ◽  
Computational Auditory Scene Analysis ◽  
Convolutional Network ◽  
Magnitude Spectrum ◽  
Front End ◽  
Asr System

The estimation of the clean speech short-time magnitude spectrum (MS) is key for speech enhancement and separation. Moreover, an automatic speech recognition (ASR) system that employs a front-end relies on clean speech MS estimation to remain robust. Training targets for deep learning approaches to clean speech MS estimation fall into three main categories: computational auditory scene analysis (CASA), MS, and minimum mean-square error (MMSE) training targets. In this study, we aim to determine which training target produces enhanced/separated speech at the highest quality and intelligibility, and which is most suitable as a front-end for robust ASR. The training targets were evaluated using a temporal convolutional network (TCN) on the DEMAND Voice Bank and Deep Xi datasets---which include real-world non-stationary and coloured noise sources at multiple SNR levels. Seven objective measures were used, including the word error rate (WER) of the Deep Speech ASR system. We find that MMSE training targets produce the highest objective quality scores. We also find that CASA training targets, in particular the ideal ratio mask (IRM), produce the highest intelligibility scores and perform best as a front-end for robust ASR.

scholarly journals On Training Targets for Deep Learning Approaches to Clean Speech Magnitude Spectrum Estimation

2020 ◽  
Author(s):  
Aaron Nicolson ◽  
Kuldip K. Paliwal
Keyword(s):  
Deep Learning ◽  
Minimum Mean Square Error ◽  
Auditory Scene Analysis ◽  
Spectrum Estimation ◽  
Learning Approaches ◽  
Computational Auditory Scene Analysis ◽  
Convolutional Network ◽  
Magnitude Spectrum ◽  
Front End ◽  
Asr System

The estimation of the clean speech short-time magnitude spectrum (MS) is key for speech enhancement and separation. Moreover, an automatic speech recognition (ASR) system that employs a front-end relies on clean speech MS estimation to remain robust. Training targets for deep learning approaches to clean speech MS estimation fall into three main categories: computational auditory scene analysis (CASA), MS, and minimum mean-square error (MMSE) training targets. In this study, we aim to determine which training target produces enhanced/separated speech at the highest quality and intelligibility, and which is most suitable as a front-end for robust ASR. The training targets were evaluated using a temporal convolutional network (TCN) on the DEMAND Voice Bank and Deep Xi datasets---which include real-world non-stationary and coloured noise sources at multiple SNR levels. Seven objective measures were used, including the word error rate (WER) of the Deep Speech ASR system. We find that MMSE training targets produce the highest objective quality scores. We also find that CASA training targets, in particular the ideal ratio mask (IRM), produce the highest intelligibility scores and perform best as a front-end for robust ASR.

scholarly journals Classification and Representation Joint Learning via Deep Networks

2017 ◽  
Author(s):  
Ya Li ◽  
Xinmei Tian ◽  
Xu Shen ◽  
Dacheng Tao
Keyword(s):  
Spatial Distribution ◽  
Deep Learning ◽  
Feature Learning ◽  
Classification Performance ◽  
Local Distribution ◽  
Classification Problems ◽  
Class Label ◽  
Channel Network ◽  
Training Samples ◽  
Label Information

Deep learning has been proven to be effective for classification problems. However, the majority of previous works trained classifiers by considering only class label information and ignoring the local information from the spatial distribution of training samples. In this paper, we propose a deep learning framework that considers both class label information and local spatial distribution information between training samples. A two-channel network with shared weights is used to measure the local distribution. The classification performance can be improved with more detailed information provided by the local distribution, particularly when the training samples are insufficient. Additionally, the class label information can help to learn better feature representations compared with other feature learning methods that use only local distribution information between samples. The local distribution constraint between sample pairs can also be viewed as a regularization of the network, which can efficiently prevent the overfitting problem. Extensive experiments are conducted on several benchmark image classification datasets, and the results demonstrate the effectiveness of our proposed method.

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