Approximating properties of artificial neural network in time domain for the analysis of electromagnetic fields reflected from model of human body surface

2010 ◽  
Author(s):  
O.M. Dumin ◽  
D.V. Shyrokorad ◽  
O.O. Dumina ◽  
V.A. Katrich ◽  
V.I. Chebotarev
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Electromagnetic Fields ◽  
Body Surface ◽  
Human Body ◽  
Time Domain ◽  
Artificial Neural ◽  
Human Body Surface

Performance evaluation of pattern recognition networks using electromyography signal and time-domain features for the classification of hand gestures

2020 ◽  
Vol 234 (6) ◽  
pp. 639-648
Author(s):  
S Mary Vasanthi ◽  
T Jayasree
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Support Vector Machine ◽  
Time Domain ◽  
Support Vector ◽  
Finger Movement ◽  
Finger Movements ◽  
Feed Forward ◽  
Artificial Neural ◽  
Deep Learning Neural Network

The problem of classifying individual finger movements of one hand is focused in this article. The input electromyography signal is processed and eight time-domain features are extracted for classifying hand gestures. The classified finger movements are thumb, middle, index, little, ring, hand close, thumb index, thumb ring, thumb little and thumb middle and the hand grasps are palmar class, spherical class, hook class, cylindrical class, tip class and lateral class. Four state-of-the-art classifiers namely feed forward artificial neural network, cascaded feed forward artificial neural network, deep learning neural network and support vector machine are selected for this work to classify the finger movements and hand grasps using the extracted time-domain features. The experimental results show that the artificial neural network classifier is stabilized at 6 epochs for finger movement dataset and at 4 epochs for hand grasps dataset with low mean square error. However, the support vector machine classifier attains the maximum accuracy of 97.3077% for finger movement dataset and 98.875% for hand grasp dataset which is significantly greater than feed forward artificial neural network, cascaded feed forward artificial neural network and deep learning neural network classifiers.

scholarly journals Artificial Neural Network for Classifying Injected Materials under Ultrasonography

2021 ◽  
Vol 3 (1) ◽  
pp. 0210101
Author(s):  
Galuh Retno Utari ◽  
Giner Maslebu ◽  
Suryasatriya Trihandaru
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Human Body ◽  
Data Augmentation ◽  
Test Dataset ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Human Eyes

We have constructed an artificial neural network (ANN) architecture to classify four different classes of ultrasonography recorded from a jelly box phantom  that was injected by iron, glass, or plastic marble, or without any injection. This jelly box was made as a phantom of a human body, and the injected materials were the cancers. The small size of the injected materials caused only  little disturbances those could not easily distinguished by human eyes. Therefore, ANN was used for classifying the different kind of the injected materials. The number of original images  taken from ultrasonographs were not so many, therefore we did data augmentation for providing large enough dataset that fed into ANN. The data augmentation was constructed by pixel shifting in horizontal and vertical directions. The procedure proposed here produced 98.2% accuracy for predicting test dataset,  though the result was sensitive to the choice of augmentation area.

Blood glucose prediction model for type 1 diabetes based on artificial neural network with time-domain features

2020 ◽  
Vol 40 (4) ◽  
pp. 1586-1599
Author(s):  
Ganjar Alfian ◽  
Muhammad Syafrudin ◽  
Muhammad Anshari ◽  
Filip Benes ◽  
Fransiskus Tatas Dwi Atmaji ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Type 1 Diabetes ◽  
Blood Glucose ◽  
Prediction Model ◽  
Time Domain ◽  
Artificial Neural ◽  
Glucose Prediction
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