Ultrasound Thyroid Image Segmentation, Feature Extraction, and Classification of Disease Using Feed Forward Back Propagation Network

2018 ◽  
pp. 89-98 ◽  
Author(s):  
U. Snekhalatha ◽  
V. Gomathy
Keyword(s):  
Feature Extraction ◽  
Image Segmentation ◽  
Back Propagation ◽  
Feed Forward ◽  
Feed Forward Back Propagation ◽  
Propagation Network

Optimization of the Cascade Feed Forward Back Propagation network for defect classification in ultrasonic images

2016 ◽  
Vol 52 (10) ◽  
pp. 557-568 ◽  
Author(s):  
C. F. Theresa Cenate ◽  
B. Sheela Rani ◽  
R. Ramadevi ◽  
D. N. Sangeetha ◽  
B. Venkatraman
Keyword(s):  
Back Propagation ◽  
Defect Classification ◽  
Feed Forward ◽  
Feed Forward Back Propagation ◽  
Propagation Network ◽  
Ultrasonic Images

scholarly journals Effective enhancement of classification of respiratory states using feed forward back propagation neural networks

Sadhana ◽  
2013 ◽  
Vol 38 (3) ◽  
pp. 377-395 ◽  
Author(s):  
A BHAVANI SANKAR ◽  
J ARPUTHA VIJAYA SELVI ◽  
D KUMAR ◽  
K SEETHA LAKSHMI
Keyword(s):  
Neural Networks ◽  
Back Propagation ◽  
Feed Forward ◽  
Feed Forward Back Propagation ◽  
Back Propagation Neural Networks

Feed-Forward Back-Propagation Neural Networks in Side-Channel Information Characterization

2018 ◽  
Vol 28 (01) ◽  
pp. 1950003 ◽  
Author(s):  
E. Saeedi ◽  
M. S. Hossain ◽  
Y. Kong
Keyword(s):  
Neural Networks ◽  
Back Propagation ◽  
Side Channel ◽  
Side Channel Attacks ◽  
Feed Forward ◽  
Feed Forward Back Propagation ◽  
Channel Information ◽  
Components Analysis ◽  
Multi Class Classification

The safety of cryptosystems, mainly based on algorithmic improvement, is still vulnerable to side-channel attacks (SCA) based on machine learning. Multi-class classification based on neural networks and principal components analysis (PCA) can be powerful tools for pattern recognition and classification of side-channel information. In this paper, an experimental investigation was conducted to explore the efficiency of various architectures of feed-forward back-propagation (FFBP) neural networks and PCA against side-channel attacks. The experiment is performed on the data leakage of an FPGA implementation of elliptic curve cryptography (ECC). Our results show that the proposed method is a promising method for SCA with an overall accuracy of 88% correct classification.

scholarly journals A Feed-Forward Back Propagation Neural Network Approach to Predict the Life Condition of Crude Oil Pipeline

Processes ◽  
2020 ◽  
Vol 8 (6) ◽  
pp. 661 ◽  
Author(s):  
Nagoor Basha Shaik ◽  
Srinivasa Rao Pedapati ◽  
Syed Ali Ammar Taqvi ◽  
A. R. Othman ◽  
Faizul Azly Abd Dzubir
Keyword(s):  
Negative Affect ◽  
Crude Oil ◽  
Back Propagation ◽  
Metal Loss ◽  
Feed Forward ◽  
Oil Pipeline ◽  
Life Condition ◽  
Feed Forward Back Propagation ◽  
Propagation Network ◽  
Hidden Neurons

Pipelines are like a lifeline that is vital to a nation’s economic sustainability; as such, pipelines need to be monitored to optimize their performance as well as reduce the product losses incurred in the transportation of petroleum chemicals. A significant number of pipes would be underground; it is of immediate concern to identify and analyse the level of corrosion and assess the quality of a pipe. Therefore, this study intends to present the development of an intelligent model that predicts the condition of crude oil pipeline cantered on specific factors such as metal loss anomalies (over length, width and depth), wall thickness, weld anomalies and pressure flow. The model is developed using Feed-Forward Back Propagation Network (FFBPN) based on historical inspection data from oil and gas fields. The model was trained using the Levenberg-Marquardt algorithm by changing the number of hidden neurons to achieve promising results in terms of maximum Coefficient of determination (R2) value and minimum Mean Squared Error (MSE). It was identified that a strong R2 value depends on the number of hidden neurons. The model developed with 16 hidden neurons accurately predicted the Estimated Repair Factor (ERF) value with an R2 value of 0.9998. The remaining useful life (RUL) of a pipeline is estimated based on the metal loss growth rate calculations. The deterioration profiles of considered factors are generated to identify the individual impact on pipeline condition. The proposed FFBPN was validated with other published models for its robustness and it was found that FFBPN performed better than the previous approaches. The deterioration curves were generated and it was found that pressure has major negative affect on pipeline condition and weld girth has a minor negative affect on pipeline condition. This study can help petroleum and natural gas industrial operators assess the life condition of existing pipelines and thus enhances their inspection and rehabilitation forecasting.

scholarly journals Feed-Forward Back Propagation Network for the prediction of diabetic retinopathy disorder

2021 ◽  
Vol 44 (1) ◽  
pp. 67-74
Author(s):  
Luminita Moraru ◽  
Simona Moldovanu ◽  
Andreea-Monica (Lăzărescu) Dincă
Keyword(s):  
Transfer Function ◽  
Back Propagation ◽  
Retinal Vessel ◽  
Disease Classification ◽  
Diabetic Patients ◽  
Main Task ◽  
Feed Forward ◽  
Feed Forward Back Propagation ◽  
Hidden Layer ◽  
Propagation Network

Some retina disorders mainly involve some blocked blood clots so that, the retinal vessels change their structure, being unable to completely nourish the retina. For an accurate investigation of retina disorders, the extraction of the retinal vessel anatomical structures or lesions is the main task. This paper reports a combination of various features extracted from retinal images, that are further used to train a Feed-Forward Back Propagation Network (FFBPN) as a decision system. The main goal is determining the combination of the appropriate features for more accurate classification of healthy and diseased patients. To achieve this goal, 120 binary images covering both categories of patients that belong to the STARE (Structured Analysis of the Retina) database were analyzed. The input data are the number of ridges, bifurcation, and bridges for retinal vessel pattern recognition. The FFBPNs with 4, 8, 12, 16, and 20 neurons in the hidden layer are trained. The FFBNP architecture with 12 neurons in the hidden layer, using the tansig transfer function in the hidden layer and linear transfer function in the output layer provides the most appropriate model for retinopathy disease classification. The correlation between the number of ridges and bridges computed for healthy patients (as actual values) and the number of ridges and bridges for diabetic patients (as predicted values) provides the best result, a regression coefficient (R) of 0. 8575 and a mean-square error (MSE) of 0.00163.

Sign in / Sign up

Export Citation Format

Share Document