scholarly journals Evaluation of Advanced Artificial Neural Network Classification and Feature Extraction Techniques for Detecting Preterm Births Using EHG Records

2014 ◽  
pp. 309-314
Author(s):  
Paul Fergus ◽  
Ibrahim Olatunji Idowu ◽  
Abir Jaffar Hussain ◽  
Chelsea Dobbins ◽  
Haya Al-Askar
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Feature Extraction ◽  
Preterm Births ◽  
Extraction Techniques ◽  
Neural Network Classification ◽  
Artificial Neural

scholarly journals Advanced artificial neural network classification for detecting preterm births using EHG records

2016 ◽  
Vol 188 ◽  
pp. 42-49 ◽  
Author(s):  
Paul Fergus ◽  
Ibrahim Idowu ◽  
Abir Hussain ◽  
Chelsea Dobbins
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Preterm Births ◽  
Neural Network Classification ◽  
Artificial Neural

Recognition of Odia Handwritten Digits using Gradient based Feature Extraction Method and Clonal Selection Algorithm

2019 ◽  
Vol 6 (2) ◽  
pp. 19-33
Author(s):  
Puspalata Pujari ◽  
Babita Majhi
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Feature Extraction ◽  
Clonal Selection ◽  
Principal Component ◽  
Clonal Selection Algorithm ◽  
Selection Algorithm ◽  
Extraction Techniques ◽  
Gradient Based ◽  
Artificial Neural

This article aims to recognize Odia handwritten digits using gradient-based feature extraction techniques and Clonal Selection Algorithm-based (CSA) multilayer artificial neural network (MANN) classifier. For the extraction of features which contribute the most towards recognition from images, are extracted using gradient-based feature extraction techniques. Principal component analysis (PCA) is used for dimensionality reduction of extracted features. A MANN is used as a classifier for classification purposes. The weights of the MANN are adjusted using the CSA to get optimized set of weights. The proposed model is applied on Odia handwritten digits taken from the Indian Statistical Institution (ISI), Calcutta, which consists of four thousand samples. The results obtained from the experiment are compared with a genetic-based multi-layer artificial neural network (GA-MANN) model. The recognition accuracy of the CSA-MANN model is found to be 90.75%.

Sign in / Sign up

Export Citation Format

Share Document