scholarly journals Time Series Segmentation Using Neural Networks with Cross-Domain Transfer Learning

Electronics ◽  
2021 ◽  
Vol 10 (15) ◽  
pp. 1805
Author(s):  
Pedro Matias ◽  
Duarte Folgado ◽  
Hugo Gamboa ◽  
André Carreiro
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Transfer Learning ◽  
Human Activity ◽  
Data Augmentation ◽  
Inertial Sensors ◽  
Intrinsic Variability ◽  
Subsequence Matching ◽  
Augmentation Techniques ◽  
Matching Techniques

Searching for characteristic patterns in time series is a topic addressed for decades by the research community. Conventional subsequence matching techniques usually rely on the definition of a target template pattern and a searching method for detecting similar patterns. However, the intrinsic variability of time series introduces changes in patterns, either morphologically and temporally, making such techniques not as accurate as desired. Intending to improve segmentation performances, in this paper, we proposed a Mask-based Neural Network (NN) which is capable of extracting desired patterns of interest from long time series, without using any predefined template. The proposed NN has been validated, alongside a subsequence matching algorithm, in two datasets: clinical (electrocardiogram) and human activity (inertial sensors). Moreover, the reduced dimension of the data in the latter dataset led to the application of transfer learning and data augmentation techniques to reach model convergence. The results have shown the proposed model achieved better segmentation performances than the baseline one, in both domains, reaching average Precision and Recall scores of 99.0% and 97.5% (clinical domain), along with 77.0% and 71.4% (human activity domain), introducing Neural Networks and Transfer Learning as promising alternatives for pattern searching in time series.

scholarly journals Comparative Analysis on Deep Learning Approaches for Heavy-Vehicle Detection based on Data Augmentation and Transfer-Learning techniques

2021 ◽  
Vol 13 (3) ◽  
pp. 809-820
Author(s):  
V. Sowmya ◽  
R. Radha
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Real Time ◽  
Transfer Learning ◽  
Convolutional Neural Networks ◽  
Traffic Management ◽  
Data Augmentation ◽  
Vehicle Detection ◽  
Heavy Vehicles ◽  
Detection And Recognition

Vehicle detection and recognition require demanding advanced computational intelligence and resources in a real-time traffic surveillance system for effective traffic management of all possible contingencies. One of the focus areas of deep intelligent systems is to facilitate vehicle detection and recognition techniques for robust traffic management of heavy vehicles. The following are such sophisticated mechanisms: Support Vector Machine (SVM), Convolutional Neural Networks (CNN), Regional Convolutional Neural Networks (R-CNN), You Only Look Once (YOLO) model, etcetera. Accordingly, it is pivotal to choose the precise algorithm for vehicle detection and recognition, which also addresses the real-time environment. In this study, a comparison of deep learning algorithms, such as the Faster R-CNN, YOLOv2, YOLOv3, and YOLOv4, are focused on diverse aspects of the features. Two entities for transport heavy vehicles, the buses and trucks, constitute detection and recognition elements in this proposed work. The mechanics of data augmentation and transfer-learning is implemented in the model; to build, execute, train, and test for detection and recognition to avoid over-fitting and improve speed and accuracy. Extensive empirical evaluation is conducted on two standard datasets such as COCO and PASCAL VOC 2007. Finally, comparative results and analyses are presented based on real-time.

scholarly journals Transfer Learning for Clinical Time Series Analysis Using Deep Neural Networks

2019 ◽  
Vol 4 (2) ◽  
pp. 112-137 ◽  
Author(s):  
Priyanka Gupta ◽  
Pankaj Malhotra ◽  
Jyoti Narwariya ◽  
Lovekesh Vig ◽  
Gautam Shroff
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Time Series Analysis ◽  
Transfer Learning ◽  
Deep Neural Networks ◽  
Series Analysis

scholarly journals ECG Arrhythmia Time Series Classification Using 1D Convolution –LSTM Neural Networks

2021 ◽  
Vol 10 (3) ◽  
pp. 1985-1990
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Data Augmentation ◽  
Discrete Wavelet ◽  
Learning Approaches ◽  
Time Series Classification ◽  
Measuring Device ◽  
Average Accuracy ◽  
Accuracy Of Results ◽  
Ecg Arrhythmia

An electrocardiogram (ECG) can be dependablyused as a measuring device to monitor cardiovascular function. The abnormal heartbeat appears in the ECG pattern and these abnormal signals are called arrhythmias. Classification and automatic arrhythmia signals can provide a faster and more accurate result. Several machine learning approaches have been applied to enhance the accuracy of results and increase the speed and robustness of models. This paper proposes a method based on Time-series Classification using deep Convolutional -LSTM neural networks and Discrete Wavelet Transform to classify 4 different types of Arrhythmia in the MIT-BIH Database. According to the results, the suggested method gives predictions with an average accuracy of 97% without needing to do feature extraction or data augmentation.

scholarly journals Multilingual Automatic Term Extraction in Low-Resource Domains

2021 ◽  
Vol 34 (1) ◽  
Author(s):  
NGOC TAN LE ◽  
Fatiha Sadat
Keyword(s):  
Neural Networks ◽  
Language Processing ◽  
Large Scale ◽  
Data Augmentation ◽  
Shared Task ◽  
Low Resource ◽  
Term Extraction ◽  
Augmentation Techniques ◽  
Automatic Term Extraction ◽  
The Neural Networks

With the emergence of the neural networks-based approaches, research on information extraction has benefited from large-scale raw texts by leveraging them using pre-trained embeddings and other data augmentation techniques to deal with challenges and issues in Natural Language Processing tasks. In this paper, we propose an approach using sequence-to-sequence neural networks-based models to deal with term extraction for low-resource domain. Our empirical experiments, evaluating on the multilingual ACTER dataset provided in the LREC-TermEval 2020 shared task on automatic term extraction, proved the efficiency of deep learning approach, in the case of low-data settings, for the automatic term extraction task.

scholarly journals A Deep Transfer Learning Model with Classical Data Augmentation and CGAN to Detect COVID-19 from Chest CT Radiography Digital Images

2020 ◽  
Author(s):  
Mohamed Loey ◽  
Florentin Smarandache ◽  
Nour Eldeen M. Khalifa
Keyword(s):  
Transfer Learning ◽  
Data Augmentation ◽  
Digital Images ◽  
Main Idea ◽  
Learning Model ◽  
Chest Ct ◽  
Benchmark Datasets ◽  
Augmentation Techniques ◽  
Testing Accuracy ◽  
Chest Ct Scan

The coronavirus disease 2019 (COVID-19) is the fastest transmittable virus caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The detection of COVID-19 using artificial intelligence techniques and especially deep learning will help to detect this virus in early stages which will reflect in increasing the opportunities of fast recovery of patients worldwide. This will lead to release the pressure off the healthcare system around the world. In this research, classical data augmentation techniques along with CGAN based on a deep transfer learning model for COVID-19 detection in chest CT scan images will be presented. The limited benchmark datasets for covid-19 especially in chest CT images is the main motivation of this research. The main idea is to collect all the possible images for covid-19 that exists until the very writing of this research and use the classical data augmentations along with CGAN to generate more images to help in the detection of the COVID-19. In this study, five different deep convolutional neural network-based models (AlexNet, VGGNet16, VGGNet19, GoogleNet, and ResNet50) have been selected for the investigation to detect the coronavirus infected patient using chest CT radiographs digital images. The classical data augmentations along with CGAN improve the performance of classification in all selected deep transfer models. The Outcomes show that ResNet50 is the most appropriate classifier to detect the COVID-19 from chest CT dataset using the classical data augmentation and CGAN with testing accuracy of 82.91%.

scholarly journals Prediction for Chaotic Time Series-Based AE-CNN and Transfer Learning

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Baogui Xin ◽  
Wei Peng
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Transfer Learning ◽  
Convolutional Neural Networks ◽  
Prediction Performance ◽  
Chaotic Time Series ◽  
Experimental Results ◽  
Prediction Scheme

It has been a hot and challenging topic to predict the chaotic time series in the medium-to-long term. We combine autoencoders and convolutional neural networks (AE-CNN) to capture the intrinsic certainty of chaotic time series. We utilize the transfer learning (TL) theory to improve the prediction performance in medium-to-long term. Thus, we develop a prediction scheme for chaotic time series-based AE-CNN and TL named AE-CNN-TL. Our experimental results show that the proposed AE-CNN-TL has much better prediction performance than any one of the following: AE-CNN, ARMA, and LSTM.

scholarly journals Stateful Premise Selection by Recurrent Neural Networks

10.29007/j5hd ◽  
2020 ◽  
Author(s):  
Bartosz Piotrowski ◽  
Josef Urban
Keyword(s):  
Neural Networks ◽  
Recurrent Neural Networks ◽  
Data Augmentation ◽  
State Of The Art ◽  
Language Translation ◽  
New Method ◽  
New Learning ◽  
Augmentation Techniques

In this work we develop a new learning-based method for selecting facts (premises) when proving new goals over large formal libraries. Unlike previous methods that choose sets of facts independently of each other by their rank, the new method uses the notion of state that is updated each time a choice of a fact is made. Our stateful architecture is based on recurrent neural networks which have been recently very successful in stateful tasks such as language translation. The new method is combined with data augmentation techniques, evaluated in several ways on a standard large-theory benchmark and compared to state-of-the-art premise approach based on gradient boosted trees. It is shown to perform significantly better and to solve many new problems.

scholarly journals Plastic Gasket Defect Detection Based on Transfer Learning

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Xieyi Chen ◽  
Dongyun Wang ◽  
Jinjun Shao ◽  
Jun Fan
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Transfer Learning ◽  
Defect Detection ◽  
Data Augmentation ◽  
Surface Defects ◽  
Visual Detection ◽  
Training Data

To automatically detect plastic gasket defects, a set of plastic gasket defect visual detection devices based on GoogLeNet Inception-V2 transfer learning was designed and established in this study. The GoogLeNet Inception-V2 deep convolutional neural network (DCNN) was adopted to extract and classify the defect features of plastic gaskets to solve the problem of their numerous surface defects and difficulty in extracting and classifying the features. Deep learning applications require a large amount of training data to avoid model overfitting, but there are few datasets of plastic gasket defects. To address this issue, data augmentation was applied to our dataset. Finally, the performance of the three convolutional neural networks was comprehensively compared. The results showed that the GoogLeNet Inception-V2 transfer learning model had a better performance in less time. It means it had higher accuracy, reliability, and efficiency on the dataset used in this paper.

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