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.

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 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 PIXEL-BASED AND OBJECT-BASED TERRACE EXTRACTION USING FEED-FORWARD DEEP NEURAL NETWORK

2019 ◽  
Vol IV-3/W1 ◽  
pp. 1-7
Author(s):  
H. T. Do ◽  
V. Raghavan ◽  
G. Yonezawa
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
Reference Data ◽  
Back Propagation ◽  
Activation Function ◽  
Rate Of Change ◽  
Feed Forward ◽  
Feed Forward Back Propagation ◽  
Object Based ◽  
Hidden Layer

<p><strong>Abstract.</strong> In this paper, we present the identification of terrace field by using Feed-forward back propagation deep neural network in pixel-based and several cases of object-based approaches. Terrace field of Lao Cai area in Vietnam is identified from 5-meter RapidEye image. The image includes 5 bands: red, green, blue, rededge and nir-infrared. Reference data are set of terrace points and nonterrace points, which are generated by randomly selected from reference map. The reference data is separated into three sets: training set for training processing, validation set for generating optimal parameters of deep neural network model, and test set for assessing the accuracy of classification. Six optimal thresholds (T): 0.06, 0.09, 0.12, 0.14, 0.2 and 0.22 are chosen from Rate of Change graph, and then used to generate six cases of object-based classification. Deep neural network (DNN) model is built with 8 hidden layers, input units are 5 bands of RapidEye, and output is terrace and non-terrace classes. Each hidden layer includes 256 units – a large number, to avoid under-fitting. Activation function is Rectifier. Dropout and two regularization parameters are applied to avoid overfitting. Seven terrace maps are generated. The classification results show that the DNN is able to identify terrace field effectively in both pixel-based and object-based approaches. Pixel-based classification is the most accurate approach, achieves 90% accuracy. The values of object-based approaches are 88.5%, 87.3%, 86.7%, 86.6%, 85% and 85.3% correspond to the segmentation thresholds.</p>

New Artificial Neural Network Models for Bio Medical Image Compression

2022 ◽  
pp. 913-932
Author(s):  
G. Vimala Kumari ◽  
G. Sasibhushana Rao ◽  
B. Prabhakara Rao
Keyword(s):  
Image Compression ◽  
Network Architecture ◽  
Back Propagation ◽  
Data Representation ◽  
Training Algorithms ◽  
Neural Network Models ◽  
Feed Forward ◽  
Feed Forward Back Propagation ◽  
Transmission Problems ◽  
Lm Algorithm

This article presents an image compression method using feed-forward back-propagation neural networks (NNs). Marked progress has been made in the area of image compression in the last decade. Image compression removing redundant information in image data is a solution for storage and data transmission problems for huge amounts of data. NNs offer the potential for providing a novel solution to the problem of image compression by its ability to generate an internal data representation. A comparison among various feed-forward back-propagation training algorithms was presented with different compression ratios and different block sizes. The learning methods, the Levenberg Marquardt (LM) algorithm and the Gradient Descent (GD) have been used to perform the training of the network architecture and finally, the performance is evaluated in terms of MSE and PSNR using medical images. The decompressed results obtained using these two algorithms are computed in terms of PSNR and MSE along with performance plots and regression plots from which it can be observed that the LM algorithm gives more accurate results than the GD algorithm.

scholarly journals A Framework for Prediction of Household Energy Consumption Using Feed Forward Back Propagation Neural Network

Technologies ◽  
2019 ◽  
Vol 7 (2) ◽  
pp. 30 ◽  
Author(s):  
Muhammad Fayaz ◽  
Habib Shah ◽  
Ali Aseere ◽  
Wali Mashwani ◽  
Abdul Shah
Keyword(s):  
Neural Network ◽  
Performance Evaluation ◽  
Energy Consumption ◽  
Data Acquisition ◽  
Residential Buildings ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Statistical Moments ◽  
Feed Forward ◽  
Feed Forward Back Propagation

Energy is considered the most costly and scarce resource, and demand for it is increasing daily. Globally, a significant amount of energy is consumed in residential buildings, i.e., 30–40% of total energy consumption. An active energy prediction system is highly desirable for efficient energy production and utilization. In this paper, we have proposed a methodology to predict short-term energy consumption in a residential building. The proposed methodology consisted of four different layers, namely data acquisition, preprocessing, prediction, and performance evaluation. For experimental analysis, real data collected from 4 multi-storied buildings situated in Seoul, South Korea, has been used. The collected data is provided as input to the data acquisition layer. In the pre-processing layer afterwards, several data cleaning and preprocessing schemes are applied to the input data for the removal of abnormalities. Preprocessing further consisted of two processes, namely the computation of statistical moments (mean, variance, skewness, and kurtosis) and data normalization. In the prediction layer, the feed forward back propagation neural network has been used on normalized data and data with statistical moments. In the performance evaluation layer, the mean absolute error (MAE), mean absolute percentage error (MAPE), and root mean squared error (RMSE) have been used to measure the performance of the proposed approach. The average values for data with statistical moments of MAE, MAPE, and RMSE are 4.3266, 11.9617, and 5.4625 respectively. These values of the statistical measures for data with statistical moments are less as compared to simple data and normalized data which indicates that the performance of the feed forward back propagation neural network (FFBPNN) on data with statistical moments is better when compared to simple data and normalized data.

An intelligent system for selection of grinding wheels

1997 ◽  
Vol 211 (8) ◽  
pp. 635-641 ◽  
Author(s):  
Y Li ◽  
B Mills ◽  
W B Rowe
Keyword(s):  
Neural Network ◽  
Intelligent System ◽  
Back Propagation ◽  
Grinding Wheel ◽  
Neural Network System ◽  
Grinding Conditions ◽  
Training Examples ◽  
Using Data ◽  
Hidden Layer ◽  
Propagation Network

This paper describes the development of a neural network system for grinding wheel selection. The system employs a back-propagation network with one hidden layer and was trained using data from reference handbooks. It is shown that a neural network is capable of learning the relationship between the wheel and the grinding process without a requirement for rules or equations. It was further found that a relatively small number of training examples allows the system to produce reliable recommendations for a much greater number of combinations of grinding conditions. The system was developed on a PC using the C++ programming language.

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