A Study on Classification of Food Texture with Recurrent Neural Network

2016 ◽  
pp. 247-256 ◽  
Author(s):  
Shuhei Okada ◽  
Hiroyuki Nakamoto ◽  
Futoshi Kobayashi ◽  
Fumio Kojima
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Food Texture

Classification of snoring sound based on a recurrent neural network

2019 ◽  
Vol 123 ◽  
pp. 237-245 ◽  
Author(s):  
Seung Ju Lim ◽  
Seong Jin Jang ◽  
Jee Young Lim ◽  
Jae Hoon Ko
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network

scholarly journals Solving the problem of classification of material properties using a neural network

2021 ◽  
Vol 2131 (3) ◽  
pp. 032084
Author(s):  
N E Babushkina ◽  
A A Lyapin
Keyword(s):  
Neural Network ◽  
Experimental Data ◽  
Recurrent Neural Network ◽  
Material Properties ◽  
Practical Significance ◽  
Experimental Measurements ◽  
Statistical Parameters ◽  
The Neural Network ◽  
Main Components

Abstract The article sets the task of classifying various materials and determining their belonging to a specified group using a recurrent neural network. The practical significance of the article is to obtain the results of the neural network, confirming the possibility of classifying materials by the hardness parameter using a neural network. As part of the study, a number of experimental measurements were carried out. The structure of the neural network and its main components are described. The statistical parameters of the experimental data are estimated.

scholarly journals Classification of Eog Signal using Elman Recurrent Neural Network for Different Age Groups

2019 ◽  
Vol 9 (2S2) ◽  
pp. 842-847
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Age Groups ◽  
Computer Interface ◽  
World Population ◽  
Age Group ◽  
The Individual ◽  
Elman Recurrent Neural Network ◽  
Male Subjects

Disabled people in the world population were increasing constantly, So need of rehabilitative system also increasing every day. To overcome such wretched condition, we can use the biosignal techniques to device the rehabilitative devices. Rehabilitative devices may be called as Brain Computer Interface (BCI) or Human Computer Interface (HCI). We studied the performances of ten male subjects between the age group of 18 to 25 using mean features and Elman Recurrent Neural Network (ERNN). We conducted our study with two different age group from 18 to 21 and 22 to 25. The average classification accuracy of 91.00%, 93.57% were attained for the age group of 18 to 21 and 22 to 25. From the individual analysis we identified that performances from the age group 22 to 25 were appreciated then that of the age group from 18 to 21. In between the study we analyzed that subject s from the age group 22 to 25 performed all the following five tasks neatly and accurately without any deviation and disturbance compared with age group from 18 to 21. Finally from the obtained result we concluded that subject from the age group 22 to 25 was higher than that of age group from 18 to 21.

scholarly journals Classification of Analyzed Text in Speech Recognition Using RNN-LSTM in Comparison with Convolutional Neural Network to Improve Precision for Identification of Keywords

2021 ◽  
Vol 11 (2) ◽  
pp. 1097-1108
Author(s):  
Bathaloori Reddy Prasad
Keyword(s):  
Neural Network ◽  
Speech Recognition ◽  
Convolutional Neural Network ◽  
Recurrent Neural Network ◽  
Short Term Memory ◽  
Statistical Significance ◽  
Language Translation ◽  
Accuracy And Precision ◽  
Long Short Term Memory

Aim: Text classification is a method to classify the features from language translation in speech recognition from English to Telugu using a recurrent neural network- long short term memory (RNN-LSTM) comparison with convolutional neural network (CNN). Materials and Methods: Accuracy and precision are performed with dataset alexa and english-telugu of size 8166 sentences. Classification of language translation is performed by the recurrent neural network where a number of the samples (N=62) and convolutional neural network were a number of samples (N=62) techniques, the algorithm RNN implies speech recognition that can be compared with convolutional is the second technique. Results and Discussion: RNN-LSTM from the dataset speech recognition, feature Telugu_id produce accuracy 93% and precision 68.04% which can be comparatively higher than CNN accuracy 66.11%, precision 61.90%. It shows a statistical significance as 0.007 from Independent Sample T-test. Conclusion: The RNN-LSTM performs better in finding accuracy and precision when compared to CNN.

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