scholarly journals Classification of Prostate Cancer using Wavelet Neural Network

2018 ◽  
Vol 12 (3) ◽  
pp. 968
Author(s):  
Mohanad Najm Abdulwahed
Keyword(s):  
Neural Network ◽  
Prostate Cancer ◽  
Mean Squared Error ◽  
Early Stage ◽  
Back Propagation ◽  
Activation Function ◽  
Wavelet Neural Network ◽  
Squared Error ◽  
The Neural Network

<p>Prostate cancer is the century disease that endanger the life of men. The earlier to diagnose the disease, the probability of curing this disease is higher. Therefore, new approaches of diagnosis is required to effectively detect the prostate cancer in early stage compared to the traditional methods. Therefore, WNN is a new adopted approach in prostate cancer diagnosis. Morlet function is used as an activation function of wavelet neural network (WNN) and back propagation (BP) is applied to train the Wavelet network. WNN classifies prostate cancer according to three factors: patient age, PSA level, and prostate volume. WNN performance is evaluated based on the percentage of classification and the computational complexity of several cases. The results of the simulation show that WNN has lower mean squared error (MSE) than the Neural Network (NN).</p>

Using BPNN to Predict Number of Low-Income Households in Taiwan Based on Seasonally Adjusted Annualized Rates for Real GDP

2014 ◽  
Vol 1044-1045 ◽  
pp. 1824-1827
Author(s):  
Yi Ti Tung ◽  
Tzu Yi Pai
Keyword(s):  
Neural Network ◽  
Low Income ◽  
Mean Squared Error ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Percentage Error ◽  
Real Gdp ◽  
Squared Error ◽  
Input Variables ◽  
Low Income Households

In this study, the back-propagation neural network (BPNN) was used to predict the number of low-income households (NLIH) in Taiwan, taking the seasonally adjusted annualized rates (SAAR) for real gross domestic product (GDP) as input variables. The results indicated that the lowest mean absolute percentage error (MAPE), mean squared error (MSE), root mean squared error (RMSE), and highest correlation coefficient (R) for training and testing were 4.759 % versus 19.343 %, 24429972.268 versus 781839890.859, 4942.669 versus 27961.400, and 0.945 versus 0.838, respectively.

scholarly journals Fault Identification of Gearbox Degradation with Optimized Wavelet Neural Network

2013 ◽  
Vol 20 (2) ◽  
pp. 247-262 ◽  
Author(s):  
Hanxin Chen ◽  
Yanjun Lu ◽  
Ling Tu
Keyword(s):  
Neural Network ◽  
Wavelet Packet ◽  
Back Propagation ◽  
Experimental System ◽  
Vibration Signal ◽  
Wavelet Neural Network ◽  
Acceleration Sensors ◽  
Gear Fault ◽  
Gear Crack

A novel intelligent method based on wavelet neural network (WNN) was proposed to identify the gear crack degradation in gearbox in this paper. The wavelet packet analysis (WPA) is applied to extract the fault feature of the vibration signal, which is collected by two acceleration sensors mounted on the gearbox along the vertical and horizontal direction. The back-propagation (BP) algorithm is studied and applied to optimize the scale and translation parameters of the Morlet wavelet function, the weight coefficients, threshold values in WNN structure. Four different gear crack damage levels under three different loads and three various motor speeds are presented to obtain the different gear fault modes and gear crack degradation in the experimental system. The results show the feasibility and effectiveness of the proposed method by the identification and classification of the four gear modes and degradation.

scholarly journals Early Dropout Prediction Model: A Case Study of University Leveling Course Students

2020 ◽  
Vol 12 (22) ◽  
pp. 9314 ◽  
Author(s):  
Iván Sandoval-Palis ◽  
David Naranjo ◽  
Jack Vidal ◽  
Raquel Gilar-Corbi
Keyword(s):  
Neural Network ◽  
Early Stage ◽  
Activation Function ◽  
Dropping Out ◽  
Student Dropout ◽  
The Neural Network ◽  
Early Dropout ◽  
Substantial Investment ◽  
University Dropout ◽  
National Governments

The school-dropout problem is a serious issue that affects both a country’s education system and its economy, given the substantial investment in education made by national governments. One strategy for counteracting the problem at an early stage is to identify students at risk of dropping out. The present study introduces a model to predict student dropout rates in the Escuela Politécnica Nacional leveling course. Data related to 2097 higher education students were analyzed; a logistic regression model and an artificial neural network model were trained using four variables, which incorporated student academic and socio-economic information. After comparing the two models, the neural network, with an experimentally defined architecture of 4–7–1 architecture and a logistic activation function, was selected as the model that should be applied to early predict dropout in the leveling course. The study findings show that students with the highest risk of dropping out are those in vulnerable situations, with low application grades, from the Costa regime, who are enrolled in the leveling course for technical degrees. This model can be used by the university authorities to identify possible dropout cases, as well as to establish policies to reduce university dropout and failure rates.

Research on Classification of Gait Signal Based on Neural Network

2013 ◽  
Vol 303-306 ◽  
pp. 1081-1084
Author(s):  
Jing Yin
Keyword(s):  
Neural Network ◽  
Neural Network Model ◽  
Recognition Rate ◽  
Back Propagation ◽  
Healthy People ◽  
Signal Recognition ◽  
Characteristic Parameters ◽  
Error Back Propagation ◽  
The Neural Network

To effectively recognize gait signal between healthy people and patients with Parkinson, a gait signal recognition model is established based on neural network of error back propagation (EBP), and a method is proposed to effectively extract characteristic parameters. In this paper, coefficient of variation is applied in the research of gait-pressure multi-characteristic parameters through gait-pressure signal, and the neural network model can automatically recognize gait-pressure characteristics between healthy people and patients with Parkinson. This can contribute to the recognition and diagnosis of patients with Parkinson. Experiment results show a recognition rate of 90%.

scholarly journals Multidimensional wavelet neural networks Based on polynomial powers of sigmoid

DAT Journal ◽  
2016 ◽  
Vol 1 (2) ◽  
pp. 106-123
Author(s):  
João Fernando Marar ◽  
Aron Bordin
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Back Propagation ◽  
Activation Function ◽  
Wavelet Neural Network ◽  
Back Propagation Algorithm ◽  
Wavelet Neural Networks ◽  
Low Dimensionality ◽  
Feed Forward Neural Networks ◽  
Sigmoid Functions

Wavelet functions have been used as the activation function in feed forward neural networks. An abundance of R&D has been produced on wavelet neural network area. Some successful algorithms and applications in wavelet neural network have been developed and reported in the literature. However, most of the aforementioned reports impose many restrictions in the classical back propagation algorithm, such as low dimensionality, tensor product of wavelets, parameters initialization, and, in general, the output is one dimensional, etc. In order to remove some of these restrictions, a family of polynomial wavelets generated from powers of sigmoid functions is presented. We described how a multidimensional wavelet neural networks based on these functions can be constructed, trained and applied in pattern recognition tasks. As examples of applications for the method proposed a framework for face verfication is presented.

Intelligent Classification and Measurement of Drill Wear

1994 ◽  
Vol 116 (3) ◽  
pp. 392-397 ◽  
Author(s):  
T. I. Liu ◽  
K. S. Anantharaman
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Flank Wear ◽  
Estimation Error ◽  
Back Propagation ◽  
Activation Function ◽  
The Neural Network ◽  
On Line ◽  
Thrust And Torque ◽  
Line Classification

Artificial neural networks are used for on-line classification and measurement of drill wear. The input vector of the neural network is obtained by processing the thrust and torque signals. Outputs are the wear states and flank wear measurements. The learning process can be performed by back propagation along with adaptive activation-function slope. The results of neural networks with and without adaptive activation-function slope, as well as various neural network architectures are compared. On-line classification of drill wear using neural networks has 100 percent reliability. The average flank wear estimation error using neural networks can be as low as 7.73 percent.

scholarly journals Classification and Forecasting of Bollywood Movies by Commercial Success using Back-Propagation Neural Network model

2019 ◽  
Vol 9 (2) ◽  
pp. 3600-3607
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Data Filtering ◽  
Commercial Success ◽  
The Neural Network ◽  
Proposed Model

Forecasting commercial success of motion pictures remained challenging for producers, critics and other industry leaders in this changing world of web and online media. In this study, the author has explored a back-propagation neural network model with 23 numeric input (BPNN-N23) for classification of Bollywood movies released during the years 2014 through 2017. The proposed model classifies movies in three classes namely “HIT”, “AVERAGE” and “FLOP”. Common procedures like data filtering, data cleaning and data normalization have been followed prior to feeding those data to the neural network. After comparing the performance of the proposed model with the benchmark models and works, the results show that the said model shows performance that is comparable to the published ones with respect to the assumed Indian empirical settings. This research reveals the extent of the effects and roles of the considered factors as well as the proposed model in predicting the fate of a Bollywood movie in India.

scholarly journals Faint Object Classification Using Artificial Neural Networks

1994 ◽  
Vol 161 ◽  
pp. 249-252
Author(s):  
M. Serra-Ricart
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Back Propagation ◽  
Neural Network Classifier ◽  
The Neural Network ◽  
Faint Object ◽  
Artificial Neural ◽  
Astronomical Images

Artificial Neural Network techniques are applied to the classification of faint objects, detected in digital astronomical images, and a Bayesian classifier (the neural network classifier, NNC hereafter) is proposed. This classifier can be implemented using a feedforward multilayered neural network trained by the back-propagation procedure (Werbos 1974).

scholarly journals Human Gait Analysis and Prediction Using the Levenberg-Marquardt Method

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Abdullah Alharbi ◽  
Kamran Equbal ◽  
Sultan Ahmad ◽  
Haseeb Ur Rahman ◽  
Hashem Alyami
Keyword(s):  
Neural Network ◽  
Lower Limb ◽  
Mean Squared Error ◽  
Human Gait ◽  
Network Function ◽  
Squared Error ◽  
Data Prediction ◽  
The Neural Network ◽  
Movement Artifacts ◽  
Human Gait Analysis

A high-accuracy gait data prediction model can be used to design prosthesis and orthosis for people having amputations or ailments of the lower limb. The objective of this study is to observe the gait data of different subjects and design a neural network to predict future gait angles for fixed speeds. The data were recorded via a Biometrics goniometer, while the subjects were walking on a treadmill for 20 seconds each at 2.4 kmph, 3.6 kmph, and 5.4 kmph. The data were then imported into Matlab, filtered to remove movement artifacts, and then used to design a neural network with 60% data for training, 20% for validation, and remaining 20% for testing using the LevenbergMarquardt method. The mean-squared error for all the cases was in the order of 10−3 or lower confirming that our method is correct. For further comparison, we randomly tested the neural network function with untrained data and compared the expected output with actual output of the neural network function using Pearson’s correlation coefficient and correlation plots. We conclude that our framework can be successfully used to design prosthesis and orthosis for lower limb. It can also be used to validate gait data and compare it to expected data in rehabilitation engineering.

scholarly journals Modeling of Solar Radiation Using the Wavelet Neural Network Model in Mataram City Lombok Island

2020 ◽  
Vol 11 (3) ◽  
pp. 178
Author(s):  
Syamsul Bahri
Keyword(s):  
Neural Network ◽  
Solar Radiation ◽  
Network Model ◽  
Neural Network Model ◽  
Learning Process ◽  
Human Life ◽  
Activation Function ◽  
Wavelet Neural Network ◽  
Neural Network Learning ◽  
The Neural Network

Sunlight is a source of energy for living things in general. In reality, the intensity of solar radiation is an environmental parameter that has positive and negative impacts on human life in particular. Furthermore, the knowledge on the characteristics of solar radiation, including its distribution pattern, is considered by many circles, both policy-makers and researchers in the environmental field. This study aims to create a solar radiation model in response to meteorological factors such as wind speed, air pressure and temperature, humidity, and rainfall using the Wavelet Neural Network (WNN). The modeling of solar radiation in this study is carried out by simultaneously utilizing its advantages as a hybrid model that combines the neural network model and the wavelet method. These advantages through the learning process (supervised learning) are multiplied through the use of the wavelet transform as a pre-processing data method and two type wavelets function, B-spline and Morlet wavelets, as an activation function in the neural network learning process. The WNN model was analyzed in two cases of meteorological variables, which are with and without rainfall. The results based on the root of the mean square error (RMSE) indicator show that the WNN model in these two cases is quite accurate. Meanwhile, the other indicator shows that the interval of the data distribution from the model is within the actual range. This implies that the predicted intensity of the solar radiation will be in a safe position in its adverse effect when the model is used as a reference.

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