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.

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.

scholarly journals Convolutional Neural Network Classification of Telematics Car Driving Data

Risks ◽  
2019 ◽  
Vol 7 (1) ◽  
pp. 6 ◽  
Author(s):  
Guangyuan Gao ◽  
Mario Wüthrich
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Time Series ◽  
Convolutional Neural Network ◽  
High Frequency ◽  
Location Data ◽  
Neural Network Classification ◽  
Feature Information ◽  
Car Driving

The aim of this project is to analyze high-frequency GPS location data (second per second) of individual car drivers (and trips). We extract feature information about speeds, acceleration, deceleration, and changes of direction from this high-frequency GPS location data. Time series of this feature information allow us to appropriately allocate individual car driving trips to selected drivers using convolutional neural networks.

scholarly journals Time-Series Prediction for Amount of Airworthiness Based on Time-Delay Neural Networks

2020 ◽  
Vol 26 (5) ◽  
pp. 28-32
Author(s):  
Ali Tatli ◽  
Sinem Kahvecioglu ◽  
Hikmet Karakoc
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Time Delay ◽  
Meteorological Parameters ◽  
Time Series Prediction ◽  
Flight Training ◽  
Weather Events ◽  
Visual Flight Rules ◽  
Different Types ◽  
Different Levels

Troposphere and the first stratum of the stratosphere are intensely utilized atmosphere layers for the aviation activities. Due to the different performances, capabilities, designs, and equipment of the aerial vehicles, meteorological weather events that occur in the troposphere affect these vehicles at different levels during their aeronautical activities. Although simple aircrafts are more sensitive to the effects of meteorological events, they are especially preferred by flight training organizations (FTOs) in pilotage training when they are considered in terms of maintenance and equipment costs. In cases where inexperienced pilot candidates and simple aircrafts that are more vulnerable to weather events come together, analysis and prediction of meteorological parameters becomes more important in terms of preventing accidents and reducing risks, as well as proper planning for flight and maintenance. The purposes of this study are, first, to derive flight availability time-series for two different types of aircraft according to visual flight rules by using Meteorological Terminal Air Report (METAR), and then to establish and evaluate a prediction model by using Time-Delay Neural Networks (TDNNs).

Prediction of corrections for the Polish time scale UTC(PL) using artificial neural networks

2013 ◽  
Vol 61 (3) ◽  
pp. 589-594 ◽  
Author(s):  
M. Luzar ◽  
Ł. Sobolewski ◽  
W. Miczulski ◽  
J. Korbicz
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Artificial Neural Networks ◽  
Time Scale ◽  
Theoretical Background ◽  
Group Method ◽  
Prediction Errors ◽  
Advantages And Disadvantages ◽  
Different Types ◽  
Artificial Neural

Abstract In this paper, the effectiveness of using Artificial Neural Networks (ANNs) for predicting the corrections of the Polish time scale UTC(PL) (Universal Coordinated Time) is presented. In particular, prediction results for the different types of neural networks, i.e., the MLP (MultiLayer Perceprton), the RBF (Radial Basis Function) and the GMDH (Group Method of Data Handling) are shown. The main advantages and disadvantages of using such types of neural networks are discussed. The prediction of corrections is performed using two methods: the time series analysis method and the regression method. The input data were prepared suitable for the above mentioned methods, based on two time series, ts1 and ts2. The designation of prediction errors for specified days and the influence of data quantity for the prediction error are considered. The paper consists of five sections. After Introduction, in Sec. 2, the theoretical background for different types of neural networks is presented. Section 3 shows data preparation for the appropriate type of neural network. The experimental results are presented in Sec. 4. Finally, Sec. 5 concludes the paper.

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