Multi-Label Topic Classification of Hadith of Bukhari (Indonesian Language Translation)Using Information Gain and Backpropagation Neural Network

2018 ◽  
Author(s):  
Muhammad Yuslan Abu Bakar ◽  
Adiwijaya ◽  
Said Al Faraby
Keyword(s):  
Neural Network ◽  
Information Gain ◽  
Language Translation ◽  
Backpropagation Neural Network

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.

Classification of Road Damage from Digital Image Using Backpropagation Neural Network

2017 ◽  
Vol 6 (4) ◽  
pp. 159
Author(s):  
Sutikno Sutikno ◽  
Helmie Arif Wibawa ◽  
Prima Yusuf Budiarto
Keyword(s):  
Neural Network ◽  
Image Processing ◽  
Digital Image ◽  
Traffic Accident ◽  
Traffic Accidents ◽  
Detection System ◽  
Backpropagation Neural Network ◽  
Accuracy Rate ◽  
Neural Network Algorithm

One of the biggest causes of death in the world is a traffic accident. Road damage is one of the cause factors from the traffic accident. To reduce this problem is required an early detection against road damage. This paper describes how to classify road damage using image processing and backpropagation neural network. Image processing is used to obtain binary image consists of a normalization, grayscaling, edge detection and thresholding, while the backpropagation neural network algorithm is used for classifying. The conclusion of this test that the algorithm is able to provide the accuracy rate of 83%. The results of this research may contribute to the development of road damage detection system based on the digital image so that the traffic accidents caused by road damage can be reduced.

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