scholarly journals Spatio-temporal warping for myoelectric control: an offline, feasibility study

2021 ◽  
Author(s):  
Milad Jabbari ◽  
Rami Khushaba ◽  
Kianoush Nazarpour
Keyword(s):  
Deep Learning ◽  
Feasibility Study ◽  
Short Term Memory ◽  
Myoelectric Control ◽  
Classification Error ◽  
Feature Extraction Method ◽  
Spatio Temporal ◽  
New Feature ◽  
Prosthesis Control ◽  
The Time Domain

Abstract Objective: The efficacy of an adopted feature extraction method directly affects the classification of the electromyographic (EMG) signals in myoelectric control applications. Most methods attempt to extract the dynamics of the multi-channel EMG signals in the time domain and on a channel-by-channel, or at best pairs of channels, basis. However, considering multi-channel information to build a similarity matrix has not been taken into account. Approach: Combining methods of long and short-term memory (LSTM) and dynamic temporal warping (DTW), we developed a new feature, called spatio-temporal warping (STW), for myoelectric signals. This method captures the spatio-temporal relationships of multi channels EMG signals. Main Results: Across four online databases, we show that in terms of average classification error and standard deviation values, the STW feature outperforms traditional features by 5% to 17%. In comparison to the more recent deep learning models, e.g. convolutional neural networks (CNN), STW outperformed by 5% to 18%. Also, STW showed enhanced performance when compared to the CNN+LSTM model by 2% to 14%. All differences were statistically significant with a large effect size. Significance: This feasibility study provides evidence supporting the hypothesis that spatio-temporal warping of the EMG signals can enhance the classification accuracy in an explainable way when compared to recent deep learning methods. Future work includes real-time implementation of the method and testing for prosthesis control.

scholarly journals Automated Individual Cattle Identification Using Video Data: A Unified Deep Learning Architecture Approach

2021 ◽  
Vol 2 ◽  
Author(s):  
Yongliang Qiao ◽  
Cameron Clark ◽  
Sabrina Lomax ◽  
He Kong ◽  
Daobilige Su ◽  
...  
Keyword(s):  
Deep Learning ◽  
Short Term Memory ◽  
Attention Mechanism ◽  
Video Data ◽  
Identification Accuracy ◽  
Sequence Length ◽  
Livestock Farming ◽  
Precision Livestock Farming ◽  
Learning Framework ◽  
Spatio Temporal

Individual cattle identification is a prerequisite and foundation for precision livestock farming. Existing methods for cattle identification require radio frequency or visual ear tags, all of which are prone to loss or damage. Here, we propose and implement a new unified deep learning approach to cattle identification using video analysis. The proposed deep learning framework is composed of a Convolutional Neural Network (CNN) and Bidirectional Long Short-Term Memory (BiLSTM) with a self-attention mechanism. More specifically, the Inception-V3 CNN was used to extract features from a cattle video dataset taken in a feedlot with rear-view. Extracted features were then fed to a BiLSTM layer to capture spatio-temporal information. Then, self-attention was employed to provide a different focus on the features captured by BiLSTM for the final step of cattle identification. We used a total of 363 rear-view videos from 50 cattle at three different times with an interval of 1 month between data collection periods. The proposed method achieved 93.3% identification accuracy using a 30-frame video length, which outperformed current state-of-the-art methods (Inception-V3, MLP, SimpleRNN, LSTM, and BiLSTM). Furthermore, two different attention schemes, namely, additive and multiplicative attention mechanisms were compared. Our results show that the additive attention mechanism achieved 93.3% accuracy and 91.0% recall, greater than multiplicative attention mechanism with 90.7% accuracy and 87.0% recall. Video length also impacted accuracy, with video sequence length up to 30-frames enhancing identification performance. Overall, our approach can capture key spatio-temporal features to improve cattle identification accuracy, enabling automated cattle identification for precision livestock farming.

scholarly journals DeepHTTP: Anomalous HTTP Traffic Detection and Malicious Pattern Mining Based on Deep Learning

2020 ◽  
pp. 141-161
Author(s):  
Yuqi Yu ◽  
Hanbing Yan ◽  
Yuan Ma ◽  
Hao Zhou ◽  
Hongchao Guan
Keyword(s):  
Deep Learning ◽  
Web Application ◽  
Pattern Mining ◽  
Short Term Memory ◽  
Attack Detection ◽  
Attention Mechanism ◽  
Discriminative Ability ◽  
Feature Extraction Method ◽  
Detection Model ◽  
Traffic Detection

AbstractHypertext Transfer Protocol (HTTP) accounts for a large portion of Internet application-layer traffic. Since the payload of HTTP traffic can record website status and user request information, many studies use HTTP protocol traffic for web application attack detection. In this work, we propose DeepHTTP, an HTTP traffic detection framework based on deep learning. Unlike previous studies, this framework not only performs malicious traffic detection but also uses the deep learning model to mine malicious fields of the traffic payload. The detection model is called AT-Bi-LSTM, which is based on Bidirectional Long Short-Term Memory (Bi-LSTM) with attention mechanism. The attention mechanism can improve the discriminative ability and make the result interpretable. To enhance the generalization ability of the model, this paper proposes a novel feature extraction method. Experiments show that DeepHTTP has an excellent performance in malicious traffic discrimination and pattern mining.

scholarly journals Video-Based Sentiment Analysis with hvnLBP-TOP Feature and bi-LSTM

2019 ◽  
Vol 33 ◽  
pp. 9963-9964 ◽  
Author(s):  
Haoran Li ◽  
Hua Xu
Keyword(s):  
Sentiment Analysis ◽  
Extraction Method ◽  
Short Term Memory ◽  
Recognition Accuracy ◽  
Principal Component ◽  
Short Term ◽  
Term Memory ◽  
Feature Extraction Method ◽  
Long Short Term Memory ◽  
New Feature

In this paper, we propose a new feature extraction method called hvnLBP-TOP for video-based sentiment analysis. Furthermore, we use principal component analysis (PCA) and bidirectional long short term memory (bi-LSTM) for dimensionality reduction and classification. We achieved an average recognition accuracy of 71.1% on the MOUD dataset and 63.9% on the CMU-MOSI dataset.

Deep-learning GIS hybrid approach in precipitation modeling based on spatio-temporal variables in the coastal zone of Turkey

2020 ◽  
Vol 81 ◽  
pp. 149-165
Author(s):  
H Apaydin ◽  
MT Sattari
Keyword(s):  
Artificial Intelligence ◽  
Deep Learning ◽  
Water Resource Management ◽  
Short Term Memory ◽  
Hybrid Approach ◽  
Support Vector ◽  
Monthly Precipitation ◽  
Artificial Intelligence Methods ◽  
Temporal Variables ◽  
Spatio Temporal

It is clearly known that precipitation is essential for fauna and flora. Studies have shown that location and temporal factors have an effect on precipitation. Accurate prediction of precipitation is very important for water resource management, and artificial intelligence methods are frequently used to make such predictions. In this study, the deep-learning and geographic information system (GIS) hybrid approach based on spatio-temporal variables was applied in order to model the amount of precipitation on Turkey’s coastline. Information about latitude, longitude, altitude, distance to the sea, and aspect was taken from meteorological stations, and these factors were utilized as spatial variables. The change in monthly precipitation was taken into account as a temporal variable. Artificial intelligence methods such as Gaussian process regression, support vector regression, the Broyden-Fletcher-Goldfarb-Shanno artificial neural network, M5, random forest, and long short-term memory (LSTM) were used. According to the results of the study, in which different input variable alternatives were also evaluated, LSTM was the most successful method for predicting precipitation with a value of 0.93 R. The study shows that the amount of precipitation can be estimated and a distribution map can be drawn by using spatio-temporal data and the deep-learning and GIS hybrid method at points where the measurement is not performed.

scholarly journals Improving Deep Learning for Forecasting Accuracy in Financial Data

2020 ◽  
Vol 2020 ◽  
pp. 1-12 ◽  
Author(s):  
Shih-Lin Lin ◽  
Hua-Wei Huang
Keyword(s):  
Deep Learning ◽  
Short Term Memory ◽  
Real Data ◽  
Financial Data ◽  
Complex Nature ◽  
Intrinsic Mode Functions ◽  
Financial Forecasting ◽  
Analytical Results ◽  
The Future ◽  
The Time Domain

Financial forecasting is based on the use of past and present financial information to make the best prediction of the future financial situation, to avoid high-risk situations, and to increase benefits. Such forecasts are of interest to anyone who wants to know the state of possible finances in the future, including investors and decision-makers. However, the complex nature of financial data makes it difficult to get accurate forecasts. Artificial intelligence, which has been shown to be suitable for analyzing very complex problems, can be applied to financial forecasting. Financial data is both nonlinear and nonstationary, with broadband frequency features. In other words, there is a large range of fluctuation, meaning that predictions made only using long short-term memory (LSTM) are not enough to ensure accuracy. This study uses an LSTM model for analysis of financial data, followed by a comparison of the analytical results with the actual data to see which has a larger root-mean-square-error (RMSE). The proposed method combines deep learning with empirical mode decomposition (EMD) to understand and predict financial trends from financial data. The financial data for this study are from the Taiwan corporate social responsibility (CSR) index. First, the EMD method is used to transform the CSR index data into a limited number of intrinsic mode functions (IMF). The bandwidth of these IMFs becomes narrower, with regular cyclic, periodic, or seasonal components in the time domain. In other words, the range of fluctuation is small. LSTM is a good way to forecast cyclic or seasonal data. The forecast result is obtained by adding all the IMFs together. It has been verified in past studies that only the LSTM and LSTM combined with the EMD can be used. The analytical results show that smaller RMSEs can be obtained using the LSTM combined with EMD compared to real data.

scholarly journals Sensor-Based Human Activity Recognition with Spatio-Temporal Deep Learning

Sensors ◽  
2021 ◽  
Vol 21 (6) ◽  
pp. 2141
Author(s):  
Ohoud Nafea ◽  
Wadood Abdul ◽  
Ghulam Muhammad ◽  
Mansour Alsulaiman
Keyword(s):  
Deep Learning ◽  
Activity Recognition ◽  
Human Activity ◽  
Short Term Memory ◽  
Human Activity Recognition ◽  
Sensor Data ◽  
Effective Selection ◽  
Spatio Temporal ◽  
High Level ◽  
Improved Accuracy

Human activity recognition (HAR) remains a challenging yet crucial problem to address in computer vision. HAR is primarily intended to be used with other technologies, such as the Internet of Things, to assist in healthcare and eldercare. With the development of deep learning, automatic high-level feature extraction has become a possibility and has been used to optimize HAR performance. Furthermore, deep-learning techniques have been applied in various fields for sensor-based HAR. This study introduces a new methodology using convolution neural networks (CNN) with varying kernel dimensions along with bi-directional long short-term memory (BiLSTM) to capture features at various resolutions. The novelty of this research lies in the effective selection of the optimal video representation and in the effective extraction of spatial and temporal features from sensor data using traditional CNN and BiLSTM. Wireless sensor data mining (WISDM) and UCI datasets are used for this proposed methodology in which data are collected through diverse methods, including accelerometers, sensors, and gyroscopes. The results indicate that the proposed scheme is efficient in improving HAR. It was thus found that unlike other available methods, the proposed method improved accuracy, attaining a higher score in the WISDM dataset compared to the UCI dataset (98.53% vs. 97.05%).

scholarly journals Improved Spike-based Brain-Machine Interface Using Bayesian Adaptive Kernel Smoother and Deep Learning

2020 ◽  
Author(s):  
Nur Ahmadi ◽  
Timothy Constandinou ◽  
Christos-Savvas Bouganis
Keyword(s):  
Feature Extraction ◽  
Deep Learning ◽  
Extraction Method ◽  
Short Term Memory ◽  
Wiener Filter ◽  
Decoding Algorithm ◽  
Neural Data ◽  
Feature Extraction Method ◽  
Adaptive Kernel ◽  
Machine Interface

Multiunit activity (MUA) has been proposed to mitigate the robustness issue faced by single-unit activity (SUA)-based brain-machine interfaces (BMIs). Most MUA-based BMIs still employ a binning method for extracting firing rates and linear decoder for decoding behavioural parameters. The limitations of binning and linear decoder lead to suboptimal performance of MUA-based BMIs. To address this issue, we propose Bayesian adaptive kernel smoother (BAKS) as the feature extraction method and long short-term memory (LSTM)-based deep learning as the decoding algorithm. We evaluated the proposed methods for reconstructing (offline) hand kinematics from intracortical neural data chronically recorded from the motor cortex of a monkey. Experimental results showed that BAKS coupled with LSTM outperformed other combinations of feature extraction method (binning or fixed kernel smoother) and decoding algorithm (Kalman filter or Wiener filter). Overall results demonstrate the effectiveness of BAKS and LSTM for improving the decoding performance of MUA-based BMIs.

scholarly journals Improved Spike-based Brain-Machine Interface Using Bayesian Adaptive Kernel Smoother and Deep Learning

2020 ◽  
Author(s):  
Nur Ahmadi ◽  
Timothy Constandinou ◽  
Christos-Savvas Bouganis
Keyword(s):  
Feature Extraction ◽  
Deep Learning ◽  
Extraction Method ◽  
Short Term Memory ◽  
Wiener Filter ◽  
Decoding Algorithm ◽  
Neural Data ◽  
Feature Extraction Method ◽  
Adaptive Kernel ◽  
Machine Interface

Multiunit activity (MUA) has been proposed to mitigate the robustness issue faced by single-unit activity (SUA)-based brain-machine interfaces (BMIs). Most MUA-based BMIs still employ a binning method for extracting firing rates and linear decoder for decoding behavioural parameters. The limitations of binning and linear decoder lead to suboptimal performance of MUA-based BMIs. To address this issue, we propose Bayesian adaptive kernel smoother (BAKS) as the feature extraction method and long short-term memory (LSTM)-based deep learning as the decoding algorithm. We evaluated the proposed methods for reconstructing (offline) hand kinematics from intracortical neural data chronically recorded from the motor cortex of a monkey. Experimental results showed that BAKS coupled with LSTM outperformed other combinations of feature extraction method (binning or fixed kernel smoother) and decoding algorithm (Kalman filter or Wiener filter). Overall results demonstrate the effectiveness of BAKS and LSTM for improving the decoding performance of MUA-based BMIs.

CBC-Based Synthetic Speech Detection

2019 ◽  
Vol 11 (2) ◽  
pp. 63-74 ◽  
Author(s):  
Jichen Yang ◽  
Qianhua He ◽  
Yongjian Hu ◽  
Weiqiang Pan
Keyword(s):  
Power Spectrum ◽  
Time Domain ◽  
Extraction Method ◽  
Synthetic Speech ◽  
Equal Error Rate ◽  
Speech Detection ◽  
Feature Extraction Method ◽  
Block Transform ◽  
New Feature ◽  
The Time Domain

In previous studies of synthetic speech detection (SSD), the most widely used features are based on a linear power spectrum. Different from conventional methods, this article proposes a new feature extraction method for SSD from octave power spectrum which is obtained from constant-Q transform (CQT). By combining CQT, block transform (BT) and discrete cosine transform (DCT), a new feature is obtained, namely, constant-Q block coefficients (CBC). In which, CQT is used to transform speech from the time domain into the frequency domain, BT is used to segment octave power spectrum into many blocks and DCT is used to extract principal information of every block. The experimental results on ASVspoof 2015 corpus shows that CBC is superior to other front-ends features that have been benchmarked on ASVspoof 2015 evaluation set in terms of equal error rate (EER).

scholarly journals A Hybrid GLM Model for Predicting Citywide Spatio-Temporal Metro Passenger Flow

2021 ◽  
Vol 10 (4) ◽  
pp. 222
Author(s):  
Yong Han ◽  
Tongxin Peng ◽  
Cheng Wang ◽  
Zhihao Zhang ◽  
Ge Chen
Keyword(s):  
Deep Learning ◽  
Short Term Memory ◽  
Percentage Error ◽  
Spatial Dependency ◽  
Short Term ◽  
Attention Networks ◽  
Passenger Flow ◽  
Typical Data ◽  
Proposed Model ◽  
Spatio Temporal

Accurate prediction of citywide short-term metro passenger flow is essential to urban management and transport scheduling. Recently, an increasing number of researchers have applied deep learning models to passenger flow prediction. Nevertheless, the task is still challenging due to the complex spatial dependency on the metro network and the time-varying traffic patterns. Therefore, we propose a novel deep learning architecture combining graph attention networks (GAT) with long short-term memory (LSTM) networks, which is called the hybrid GLM (hybrid GAT and LSTM Model). The proposed model captures the spatial dependency via the graph attention layers and learns the temporal dependency via the LSTM layers. Moreover, some external factors are embedded. We tested the hybrid GLM by predicting the metro passenger flow in Shanghai, China. The results are compared with the forecasts from some typical data-driven models. The hybrid GLM gets the smallest root-mean-square error (RMSE) and mean absolute percentage error (MAPE) in different time intervals (TIs), which exhibits the superiority of the proposed model. In particular, in the TI 10 min, the hybrid GLM brings about 6–30% extra improvements in terms of RMSE. We additionally explore the sensitivity of the model to its parameters, which will aid the application of this model.

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