scholarly journals Hybrid Representation of Sensor Data for the Classification of Driving Behaviour

2021 ◽  
Vol 11 (18) ◽  
pp. 8574
Author(s):  
Michalis Savelonas ◽  
Ioannis Vernikos ◽  
Dimitris Mantzekis ◽  
Evaggelos Spyrou ◽  
Athanasia Tsakiri ◽  
...  
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Event Detection ◽  
Domain Knowledge ◽  
Short Term Memory ◽  
Main Idea ◽  
Sensor Data ◽  
Support Vector ◽  
Driving Behaviour

Monitoring driving behaviour is important in controlling driving risk, fuel consumption, and CO2 emissions. Recent advances in machine learning, which include several variants of convolutional neural networks (CNNs), and recurrent neural networks (RNNs), such as long short-term memory (LSTM) and gated recurrent unit (GRU) networks, could be valuable for the development of objective and efficient computational tools in this direction. The main idea in this work is to complement data-driven classification of driving behaviour with rules derived from domain knowledge. In this light, we present a hybrid representation approach, which employs NN-based time-series encoding and rule-guided event detection. Histograms derived from the output of these two components are concatenated, normalized, and used to train a standard support vector machine (SVM). For the NN-based component, CNN-based, LSTM-based, and GRU-based variants are investigated. The CNN-based variant uses image-like representations of sensor measurements, whereas the RNN-based variants (LSTM and GRU) directly process sensor measurements in the form of time-series. Experimental evaluation on three datasets leads to the conclusion that the proposed approach outperforms a state-of-the-art camera-based approaches in distinguishing between normal and aggressive driving behaviour without using data derived from a camera. Moreover, it is demonstrated that both NN-guided time-series encoding and rule-guided event detection contribute to overall classification accuracy.

scholarly journals Stock Index Prices Prediction via Temporal Pattern Attention and Long-Short-Term Memory

2020 ◽  
Vol 2020 ◽  
pp. 1-7
Author(s):  
Xiaolu Wei ◽  
Binbin Lei ◽  
Hongbing Ouyang ◽  
Qiufeng Wu
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Time Series ◽  
Temporal Pattern ◽  
Short Term Memory ◽  
Stock Index ◽  
Support Vector ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory

This study attempts to predict stock index prices using multivariate time series analysis. The study’s motivation is based on the notion that datasets of stock index prices involve weak periodic patterns, long-term and short-term information, for which traditional approaches and current neural networks such as Autoregressive models and Support Vector Machine (SVM) may fail. This study applied Temporal Pattern Attention and Long-Short-Term Memory (TPA-LSTM) for prediction to overcome the issue. The results show that stock index prices prediction through the TPA-LSTM algorithm could achieve better prediction performance over traditional deep neural networks, such as recurrent neural network (RNN), convolutional neural network (CNN), and long and short-term time series network (LSTNet).

scholarly journals Membrane System-Based Improved Neural Networks for Time-Series Anomaly Detection

Processes ◽  
2020 ◽  
Vol 8 (9) ◽  
pp. 1168
Author(s):  
Wenxiang Guo ◽  
Xiyu Liu ◽  
Laisheng Xiang
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Anomaly Detection ◽  
Domain Knowledge ◽  
Time Series Data ◽  
Short Term Memory ◽  
Membrane System ◽  
Superior Performance ◽  
Series Data ◽  
Training Time

Anomaly detection in time series has attracted much attention recently and is quite a challenging task. In this paper, a novel deep-learning approach (AL-CNN) that classifies the time series as normal or abnormal with less domain knowledge is proposed. The proposed algorithm combines Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) to effectively model the spatial and temporal information contained in time-series data, the techniques of Squeeze-and-Excitation are applied to implement the feature recalibration. However, the difficulty of selecting multiple parameters and the long training time of a single model make AL-CNN less effective. To alleviate these challenges, a hybrid dynamic membrane system (HM-AL-CNN) is designed which is a new distributed and parallel computing model. We have performed a detailed evaluation of this proposed approach on three well-known benchmarks including the Yahoo S5 datasets. Experiments show that the proposed method possessed a robust and superior performance than the state-of-the-art methods and improved the average on three used indicators significantly.

scholarly journals Deep Neural Networks for the Classification of Pure and Impure Strawberry Purees

Sensors ◽  
2020 ◽  
Vol 20 (4) ◽  
pp. 1223 ◽  
Author(s):  
Zhong Zheng ◽  
Xin Zhang ◽  
Jinxing Yu ◽  
Rui Guo ◽  
Lili Zhangzhong
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Classification Accuracy ◽  
Deep Neural Networks ◽  
Short Term Memory ◽  
Convolutional Network ◽  
Different Types ◽  
Long Short Term Memory ◽  
Gated Recurrent Unit

In this paper, a comparative study of the effectiveness of deep neural networks (DNNs) in the classification of pure and impure purees is conducted. Three different types of deep neural networks (DNNs)—the Gated Recurrent Unit (GRU), the Long Short Term Memory (LSTM), and the temporal convolutional network (TCN)—are employed for the detection of adulteration of strawberry purees. The Strawberry dataset, a time series spectroscopy dataset from the UCR time series classification repository, is utilized to evaluate the performance of different DNNs. Experimental results demonstrate that the TCN is able to obtain a higher classification accuracy than the GRU and LSTM. Moreover, the TCN achieves a new state-of-the-art classification accuracy on the Strawberry dataset. These results indicates the great potential of using the TCN for the detection of adulteration of fruit purees in the future.

Classification of Legislations using Deep Learning

2021 ◽  
Vol 18 (5) ◽  
Author(s):  
Sameerchand Pudaruth ◽  
Sunjiv Soyjaudah ◽  
Rajendra Gunputh
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Short Term Memory ◽  
Support Vector ◽  
Word Embeddings ◽  
Legal Professionals ◽  
Domain Specific ◽  
The Republic ◽  
Learning Architectures

Laws are often developed in a piecemeal approach and many provisions of similar nature are often found in different legislations. Therefore, there is a need to classify legislations into various legal topics to help legal professionals in their daily activities. In this study, we have experimented with various deep learning architectures for the automatic classification of 490 legislations from the Republic of Mauritius into 30 categories. Our results demonstrate that a Deep Neural Network (DNN) with three hidden layers delivered the best performance compared with other architectures such as Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs). A mean classification accuracy of 60.9% was achieved using DNN, 56.5% for CNN and 33.7% for Long Short-Term Memory (LSTM). Comparisons were also made with traditional machine learning classifiers such as support vector machines and decision trees and it was found that the performance of DNN was superior, by at least 10%, in all runs. Both general pre-trained word embeddings such as Word2vec and domain-specific word embeddings such as Law2vec were used in combination with the above deep learning architectures but Word2vec had the best performance. To our knowledge, this is the first application of deep learning in the categorisation of legislations.

scholarly journals Time–frequency time–space LSTM for robust classification of physiological signals

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Tuan D. Pham
Keyword(s):  
Time Series ◽  
Network Architecture ◽  
Time Series Data ◽  
Short Term Memory ◽  
Physiological Signals ◽  
Series Data ◽  
Time Frequency ◽  
Time Space ◽  
Deep Recurrent Neural Network

AbstractAutomated analysis of physiological time series is utilized for many clinical applications in medicine and life sciences. Long short-term memory (LSTM) is a deep recurrent neural network architecture used for classification of time-series data. Here time–frequency and time–space properties of time series are introduced as a robust tool for LSTM processing of long sequential data in physiology. Based on classification results obtained from two databases of sensor-induced physiological signals, the proposed approach has the potential for (1) achieving very high classification accuracy, (2) saving tremendous time for data learning, and (3) being cost-effective and user-comfortable for clinical trials by reducing multiple wearable sensors for data recording.

scholarly journals An Adversarial Generative Network for Crop Classification from Remote Sensing Timeseries Images

2020 ◽  
Vol 13 (1) ◽  
pp. 65
Author(s):  
Jingtao Li ◽  
Yonglin Shen ◽  
Chao Yang
Keyword(s):  
Remote Sensing ◽  
Neural Networks ◽  
Short Term Memory ◽  
Complete Classification ◽  
Support Vector ◽  
Classification Models ◽  
Training Samples ◽  
Agricultural Applications ◽  
Crop Classification ◽  
Increasing Demand

Due to the increasing demand for the monitoring of crop conditions and food production, it is a challenging and meaningful task to identify crops from remote sensing images. The state-of the-art crop classification models are mostly built on supervised classification models such as support vector machines (SVM), convolutional neural networks (CNN), and long- and short-term memory neural networks (LSTM). Meanwhile, as an unsupervised generative model, the adversarial generative network (GAN) is rarely used to complete classification tasks for agricultural applications. In this work, we propose a new method that combines GAN, CNN, and LSTM models to classify crops of corn and soybeans from remote sensing time-series images, in which GAN’s discriminator was used as the final classifier. The method is feasible on the condition that the training samples are small, and it fully takes advantage of spectral, spatial, and phenology features of crops from satellite data. The classification experiments were conducted on crops of corn, soybeans, and others. To verify the effectiveness of the proposed method, comparisons with models of SVM, SegNet, CNN, LSTM, and different combinations were also conducted. The results show that our method achieved the best classification results, with the Kappa coefficient of 0.7933 and overall accuracy of 0.86. Experiments in other study areas also demonstrate the extensibility of the proposed method.

scholarly journals Deep Learning Methods for Classification of Certain Abnormalities in Echocardiography

Electronics ◽  
2021 ◽  
Vol 10 (4) ◽  
pp. 495
Author(s):  
Imayanmosha Wahlang ◽  
Arnab Kumar Maji ◽  
Goutam Saha ◽  
Prasun Chakrabarti ◽  
Michal Jasinski ◽  
...  
Keyword(s):  
Deep Learning ◽  
Short Term Memory ◽  
Support Vector ◽  
Variational Autoencoder ◽  
Different Types ◽  
Static Images ◽  
Long Short Term Memory ◽  
2D And 3D ◽  
Better Than

This article experiments with deep learning methodologies in echocardiogram (echo), a promising and vigorously researched technique in the preponderance field. This paper involves two different kinds of classification in the echo. Firstly, classification into normal (absence of abnormalities) or abnormal (presence of abnormalities) has been done, using 2D echo images, 3D Doppler images, and videographic images. Secondly, based on different types of regurgitation, namely, Mitral Regurgitation (MR), Aortic Regurgitation (AR), Tricuspid Regurgitation (TR), and a combination of the three types of regurgitation are classified using videographic echo images. Two deep-learning methodologies are used for these purposes, a Recurrent Neural Network (RNN) based methodology (Long Short Term Memory (LSTM)) and an Autoencoder based methodology (Variational AutoEncoder (VAE)). The use of videographic images distinguished this work from the existing work using SVM (Support Vector Machine) and also application of deep-learning methodologies is the first of many in this particular field. It was found that deep-learning methodologies perform better than SVM methodology in normal or abnormal classification. Overall, VAE performs better in 2D and 3D Doppler images (static images) while LSTM performs better in the case of videographic images.

scholarly journals ECG Prediction Based on Classification via Neural Networks and Linguistic Fuzzy Logic Forecaster

2015 ◽  
Vol 2015 ◽  
pp. 1-10 ◽  
Author(s):  
Eva Volna ◽  
Martin Kotyrba ◽  
Hashim Habiballa
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Ecg Signals ◽  
Detection Systems ◽  
Language Interpretation ◽  
Different Types ◽  
Time Courses ◽  
Natural Language Interpretation ◽  
Unique Capability

The paper deals with ECG prediction based on neural networks classification of different types of time courses of ECG signals. The main objective is to recognise normal cycles and arrhythmias and perform further diagnosis. We proposed two detection systems that have been created with usage of neural networks. The experimental part makes it possible to load ECG signals, preprocess them, and classify them into given classes. Outputs from the classifiers carry a predictive character. All experimental results from both of the proposed classifiers are mutually compared in the conclusion. We also experimented with the new method of time series transparent prediction based on fuzzy transform with linguistic IF-THEN rules. Preliminary results show interesting results based on the unique capability of this approach bringing natural language interpretation of particular prediction, that is, the properties of time series.

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