Back propagation neural network for classification of induction machine faults

2011 ◽  
Author(s):  
A. Medoued ◽  
A. Lebaroud ◽  
A. Boukadoum ◽  
T. Boukra ◽  
G. Clerc
Keyword(s):  
Neural Network ◽  
Back Propagation ◽  
Induction Machine ◽  
Back Propagation Neural Network

Application of Genetic Algorithm and Back Propagation Neural Network for Effective Personalize Web Search-Based on Clustered Query Sessions

2016 ◽  
Vol 7 (1) ◽  
pp. 33-49 ◽  
Author(s):  
Suruchi Chawla
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Web Search ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Information Need ◽  
Data Set ◽  
User Query ◽  
User Queries

In this paper novel method is proposed using hybrid of Genetic Algorithm (GA) and Back Propagation (BP) Artificial Neural Network (ANN) for learning of classification of user queries to cluster for effective Personalized Web Search. The GA- BP ANN has been trained offline for classification of input queries and user query session profiles to a specific cluster based on clustered web query sessions. Thus during online web search, trained GA –BP ANN is used for classification of new user queries to a cluster and the selected cluster is used for web page recommendations. This process of classification and recommendations continues till search is effectively personalized to the information need of the user. Experiment was conducted on the data set of web user query sessions to evaluate the effectiveness of Personalized Web Search using GA optimized BP ANN and the results confirm the improvement in the precision of search results.

A back propagation neural network for the classification of visual field data

1993 ◽  
Vol 38 (9) ◽  
pp. 1263-1270 ◽  
Author(s):  
D Keating ◽  
E Mutlukan ◽  
A Evans ◽  
J McGarvie ◽  
B Damato
Keyword(s):  
Neural Network ◽  
Visual Field ◽  
Field Data ◽  
Back Propagation ◽  
Back Propagation Neural Network

scholarly journals Identification and classification of bulk paddy, brown, and white rice cultivars with colour features extraction using image analysis and neural network 

2014 ◽  
Vol 32 (No. 3) ◽  
pp. 280-287 ◽  
Author(s):  
I. Golpour ◽  
ParianJA ◽  
R.A. Chayjan
Keyword(s):  
Neural Network ◽  
Feature Selection ◽  
Classification Accuracy ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Rice Cultivars ◽  
Image Processing Algorithm ◽  
White Rice ◽  
Colour Features

We identify five rice cultivars by mean of developing an image processing algorithm. After preprocessing operations, 36 colour features in RGB, HSI, HSV spaces were extracted from the images. These 36 colour features were used as inputs in back propagation neural network. The feature selection operations were performed using STEPDISC analysis method. The mean classification accuracy with 36 features for paddy, brown and white rice cultivars acquired 93.3, 98.8, and 100%, respectively. After the feature selection to classify paddy cultivars, 13 features were selected for this study. The highest mean classification accuracy (96.66%) was achieved with 13 features. With brown and white rice, 20 and 25 features acquired the highest mean classification accuracy (100%, for both of them). The optimised neural networks with two hidden layers and 36-6-5-5, 36-9-6-5, 36-6-6-5 topologies were obtained for the classification of paddy, brown, and white rice cultivars, respectively. These structures of neural network had the highest mean classification accuracy for bulk paddy, brown and white rice identification (98.8, 100, and 100%, respectively).

Automatic Detection and Classification of Diabetes Using Artificial Intelligence

2021 ◽  
Vol 8 (1) ◽  
pp. 01-05
Author(s):  
V. Nithyalakshmi ◽  
Dr.R. Sivakumar ◽  
Dr.A. Sivaramakrishnan
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Diabetes Diagnosis ◽  
Artificial Intelligence Technique ◽  
Classification Rate ◽  
Neural Network Algorithm ◽  
Health Complications

Diabetes is characterized as a chronic disease that may cause many health complications. Artificial intelligence techniques are adopted diagnose diabetes more accurately. This paper presents an artificial intelligence technique for diabetes diagnosis. Efficacy of the technique is evaluated using diabetes database. Experimental results show that the back propagation neural network algorithm yields the highest classification rate compared to k-nearest neighbourhood classifier. Additionally, the back propagation neural network provides error with the highest area under curve of 90 %.

Offline Signature Recognition using Back Propagation Neural Network

2016 ◽  
Vol 4 (3) ◽  
pp. 678
Author(s):  
Asyrofa Rahmi ◽  
Vivi Nur Wijayaningrum ◽  
Wayan Firdaus Mahmudy ◽  
Andi Maulidinnawati A. K. Parewe
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Recognition Accuracy ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Learning Rate ◽  
Signature Recognition ◽  
Artificial Neural Network Ann ◽  
Number Of Iterations

The signature recognition is a difficult process as it requires several phases. A failure in a phase will significantly reduce the recognition accuracy. Artificial Neural Network (ANN) believed to be used to assist in the recognition or classification of the signature. In this study, the ANN algorithm used is Back Propagation. A mechanism to adaptively adjust the learning rate is developed to improve the system accuracy. The purpose of this study is to conduct the recognition of a number of signatures so that can be known whether the recognition which is done by using the Back Propagation is appropriate or not. The testing results performed by using learning rate of 0.64, the number of iterations is 100, and produces an accuracy value of 63%.

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