Performance Analysis of Different Learning Algorithms of Feed Forward Neural Network Regarding Fetal Abnormality Detection

2018 ◽  
pp. 118-132
Author(s):  
Vidhi Rawat ◽  
Alok Jain ◽  
Vibhakar Shrimali ◽  
Sammer Raghuvanshi
Keyword(s):  
Neural Network ◽  
Performance Analysis ◽  
Learning Algorithms ◽  
Abnormality Detection ◽  
Feed Forward Neural Network ◽  
Fetal Abnormality ◽  
Feed Forward

scholarly journals Performance Analysis of Bagging Feed-Forward Neural Network for Forecasting Building Energy Demand

2018 ◽  
Vol 30 (2) ◽  
pp. 1-12 ◽  
Author(s):  
E. Juan Zarate Perez ◽  
Mariana Palumbo Fernández ◽  
Ana Lúcia Torres Seroa da Motta
Keyword(s):  
Neural Network ◽  
Performance Analysis ◽  
Energy Demand ◽  
Building Energy ◽  
Feed Forward Neural Network ◽  
Feed Forward

scholarly journals The effects of multiple layers feed-forward neural network transfer function in digital based Ethiopian soil classification and moisture prediction

2020 ◽  
Vol 10 (4) ◽  
pp. 4073
Author(s):  
Belete Biazen Bezabeh ◽  
Abrham Debasu Mengistu
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Performance Analysis ◽  
Transfer Function ◽  
Soil Classification ◽  
Learning Performance ◽  
Machine Learning Techniques ◽  
Feature Descriptor ◽  
Feed Forward Neural Network ◽  
Feed Forward

In the area of machine learning performance analysis is the major task in order to get a better performance both in training and testing model. In addition, performance analysis of machine learning techniques helps to identify how the machine is performing on the given input and also to find any improvements needed to make on the learning model. Feed-forward neural network (FFNN) has different area of applications, but the epoch convergences of the network differs from the usage of transfer function. In this study, to build the model for classification and moisture prediction of soil, rectified linear units (ReLU), Sigmoid, hyperbolic tangent (Tanh) and Gaussian transfer function of feed-forward neural network had been analyzed to identify an appropriate transfer function. Color, texture, shape and brisk local feature descriptor are used as a feature vector of FFNN in the input layer and 4 hidden layers were considered in this study. In each hidden layer 26 neurons are used. From the experiment, Gaussian transfer function outperforms than ReLU, sigmoid and tanh transfer function. But the convergence rate of Gaussian transfer function took more epoch than ReLU, Sigmoid and tanh.

scholarly journals Abnormality Detection Using LBP Features and K-Means Labelling based Feed-Forward Neural Network in Video Sequence

2019 ◽  
Vol 8 (9S) ◽  
pp. 629-633
Keyword(s):  
Neural Network ◽  
Real Time ◽  
Video Surveillance ◽  
Local Binary Pattern ◽  
Video Sequence ◽  
Detection Method ◽  
Experimental Results ◽  
Abnormality Detection ◽  
Feed Forward Neural Network ◽  
Feed Forward

Video surveillance is widely used in various domains like military, commercial and consumer areas. One of the objectives in video surveillance is the detection of normal and abnormal behavior.It has always been a challenge to accurately identify such events in any real time video sequence. In this paper, abnormality detection method using Local Binary Pattern and k-means labeling basedfeed-forward neural network has been proposed. The performance of the proposed method has also been compared with four other techniques in literature to show its worthiness. It can be seen in the experimental results that an accuracy of up to 98% has been achieved for the proposed technique.

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