scholarly journals Artificial Neural Network (ANN) based Object Recognition Using Multiple Feature Sets

2011 ◽  
pp. 32-36
Author(s):  
Manami Barthakur ◽  
Tapashi Thakuria ◽  
Kandarpa Kumar Sarma
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Back Propagation ◽  
Principal Component ◽  
Multi Layer Perceptron ◽  
Multiple Features ◽  
Error Back Propagation ◽  
Multiple Feature ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

In this work, a simplified Artificial Neural Network (ANN) based approach for recognition of various objects is explored using multiple features. The objective is to configure and train an ANN to be capable of recognizing an object using a feature set formed by Principal Component Analysis (PCA), Frequency Domain and Discrete Cosine Transform (DCT) components. The idea is to use these varied components to form a unique hybrid feature set so as to capture relevant details of objects for recognition using a ANN which for the work is a Multi Layer Perceptron (MLP) trained with (error) Back Propagation learning.

scholarly journals Artificial Neural Network Approach for Solving Fuzzy Fractional order initial value problems under H-differentiability

2020 ◽  
Author(s):  
Somayeh Ezadi ◽  
Tofigh Allahviranloo
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Initial Conditions ◽  
Error Function ◽  
Back Propagation ◽  
Back Propagation Algorithm ◽  
Neural Network Approach ◽  
Error Back Propagation ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

This paper aims to solve the celebrated Fuzzy Fractional Differential Equations (FFDE) using an Artificial Neural Network (ANN) technique. Compared to the integer order differential equation, the proposed FFDE can better describe several real application problems of various physical systems. To accomplish the aforementioned aim, the error back propagation algorithm and a multi-layer feed forward neural architecture are utilized using the unsupervised learning in order to minimize the error function as well as the modification of the parameters such as weights and biases. By combining the initial conditions with the ANN, output provides an appropriate approximate solution of the proposed FFDE. Then, two illustrative examples are solved to confirm the applicability of the concept as well as to demonstrate both the precision and effectiveness of the developed method. By comparing with some traditional methods, the obtained results reveals a close match that confirms both accuracy and correctness of the proposed method.

Non-invasive prediction of bloodstain age using the principal component and a back propagation artificial neural network

2017 ◽  
Vol 14 (9) ◽  
pp. 095601 ◽  
Author(s):  
Huimin Sun ◽  
Yaoyong Meng ◽  
Pingli Zhang ◽  
Yajing Li ◽  
Nan Li ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Back Propagation ◽  
Principal Component ◽  
Non Invasive ◽  
Artificial Neural

ARTIFICIAL NEURAL NETWORK (ANN)-BASED PREDICTIVE TOOL FOR ESTIMATING LIGHTNING DAMAGE IN COMPOSITES

2021 ◽  
Author(s):  
DEVIN NIELSEN ◽  
TYLER LOTT ◽  
SOM DUTTA ◽  
JUHYEONG LEE
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Network Architecture ◽  
Back Propagation ◽  
Training Dataset ◽  
Predictive Tool ◽  
Ann Models ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Hidden Layer

In this study, three artificial neural network (ANN) models are developed with back propagation (BP) optimization algorithms to predict various lightning damage modes in carbon/epoxy laminates. The proposed ANN models use three input variables associated with lightning waveform parameters (i.e., the peak current amplitude, rising time, and decaying time) to predict fiber damage, matrix damage, and through-thickness damage in the composites. The data used for training and testing the networks was actual lightning damage data collected from peer-reviewed published literature. Various BP training algorithms and network architecture configurations (i.e., data splitting, the number of neurons in a hidden layer, and the number of hidden layers) have been tested to improve the performance of the neural networks. Among the various BP algorithms considered, the Bayesian regularization back propagation (BRBP) showed the overall best performance in lightning damage prediction. When using the BRBP algorithm, as expected, the greater the fraction of the collected data that is allocated to the training dataset, the better the network is trained. In addition, the optimal ANN architecture was found to have a single hidden layer with 20 neurons. The ANN models proposed in this work may prove useful in preliminary assessments of lightning damage and reduce the number of expensive experimental lightning tests.

scholarly journals Predicting hearing loss from otoacoustic emissions using an artificial neural network

2002 ◽  
Vol 49 (1) ◽  
Author(s):  
Rouviere De Waal ◽  
René Hugo ◽  
Maggi Soer ◽  
Johann J. Krüger
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Otoacoustic Emissions ◽  
Back Propagation ◽  
Normal Hearing ◽  
Hearing Impaired ◽  
Distortion Product ◽  
Hearing Ability ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Normal and impaired pure tone thresholds (PTTs) were predicted from distortion product otoacoustic emissions (DP using a feed-forward artificial neural network (ANN) with a back-propagation training algorithm. The ANN used a present and absent DPOAEs from eight DP grams, (2fl -f2 = 406 - 4031 Hz) to predict PTTs at 0.5, 1, 2 and 4 kHz. With normal hearing as < 25 dB HL, prediction accuracy of normal hearing was 94% at 500, 88% at 1000, 88% at 2000 and 93% at 4000 Hz. Prediction of hearing-impaired categories was less accurate, due to insufficient data for the ANN to train on. This research indicates the possibility of accurately predicting hearing ability within 10 dB in normal hearing individuals and in hearing-impaired listeners with DPOAEs and ANNsfrom 500 - 4000 Hz.

Speckle Noise Filtering Using Back-Propagation Multi-Layer Perceptron Network in Synthetic Aperture Radar Image

2020 ◽  
pp. 489-510
Author(s):  
Khwairakpam Amitab ◽  
Debdatta Kandar ◽  
Arnab K. Maji
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Synthetic Aperture Radar ◽  
Back Propagation ◽  
Speckle Noise ◽  
Synthetic Aperture ◽  
Sar Image ◽  
Multi Layer Perceptron ◽  
Sar Images ◽  
Artificial Neural

Synthetic Aperture Radar (SAR) are imaging Radar, it uses electromagnetic radiation to illuminate the scanned surface and produce high resolution images in all-weather condition, day and night. Interference of signals causes noise and degrades the quality of the image, it causes serious difficulty in analyzing the images. Speckle is multiplicative noise that inherently exist in SAR images. Artificial Neural Network (ANN) have the capability of learning and is gaining popularity in SAR image processing. Multi-Layer Perceptron (MLP) is a feed forward artificial neural network model that consists of an input layer, several hidden layers, and an output layer. We have simulated MLP with two hidden layer in Matlab. Speckle noises were added to the target SAR image and applied MLP for speckle noise reduction. It is found that speckle noise in SAR images can be reduced by using MLP. We have considered Log-sigmoid, Tan-Sigmoid and Linear Transfer Function for the hidden layers. The MLP network are trained using Gradient descent with momentum back propagation, Resilient back propagation and Levenberg-Marquardt back propagation and comparatively evaluated the performance.

scholarly journals Artificial neural network classification-based skin cancer detection

2017 ◽  
Vol 7 (1.1) ◽  
pp. 591
Author(s):  
M. Shyamala Devi ◽  
A.N. Sruthi ◽  
P. Balamurugan
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Skin Cancer ◽  
Back Propagation ◽  
Modern Technology ◽  
Data Set ◽  
Processing Strategies ◽  
Skin Cancers ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

At present, skin cancers are extremely the most severe and life-threatening kind of cancer. The majority of the pores and skin cancers are completely remediable at premature periods. Therefore, a premature recognition of pores and skin cancer can effectively protect the patients. Due to the progress of modern technology, premature recognition is very easy to identify. It is not extremely complicated to discover the affected pores and skin cancers with the exploitation of Artificial Neural Network (ANN). The treatment procedure exploits image processing strategies and Artificial Intelligence. It must be noted that, the dermoscopy photograph of pores and skin cancer is effectively determined and it is processed to several pre-processing for the purpose of noise eradication and enrichment in image quality. Subsequently, the photograph is distributed through image segmentation by means of thresholding. Few components distinctive for skin most cancers regions. These features are mined the practice of function extraction scheme - 2D Wavelet Transform scheme. These outcomes are provides to the Back-Propagation Neural (BPN) Network for effective classification. This completely categorizes the data set into either cancerous or non-cancerous. 

scholarly journals Multiple regression models and Artificial Neural Network (ANN) as prediction tools of changes in overall quality during the storage of spreadable processed Gouda cheese

2019 ◽  
Vol 245 (11) ◽  
pp. 2539-2547 ◽  
Author(s):  
J. Stangierski ◽  
D. Weiss ◽  
A. Kaczmarek
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Multiple Regression ◽  
Regression Models ◽  
Principal Component ◽  
Multiple Regression Models ◽  
Gouda Cheese ◽  
Artificial Neural ◽  
Validation Set ◽  
Artificial Neural Network Ann

Abstract The aim of the study was to compare the ability of multiple linear regression (MLR) and Artificial Neural Network (ANN) to predict the overall quality of spreadable Gouda cheese during storage at 8 °C, 20 °C and 30 °C. The ANN used five factors selected by Principal Component Analysis, which was used as input data for the ANN calculation. The datasets were divided into three subsets: a training set, a validation set, and a test set. The multiple regression models were highly significant with high determination coefficients: R2 = 0.99, 0.87 and 0.87 for 8, 20 and 30 °C, respectively, which made them a useful tool to predict quality deterioration. Simultaneously, the artificial neural networks models with determination coefficient of R2 = 0.99, 0.96 and 0.96 for 8, 20 and 30 °C, respectively were built. The models based on ANNs with higher values of determination coefficients and lower RMSE values proved to be more accurate. The best fit of the model to the experimental data was found for processed cheese stored at 8 °C.

Sign in / Sign up

Export Citation Format

Share Document