scholarly journals Prediction of fragmentation of kidney stones: A statistical approach from NCCT images

2016 ◽  
Vol 10 (7-8) ◽  
pp. 237 ◽  
Author(s):  
Krishna Moorthy ◽  
Meenakshy Krishnan
Keyword(s):  
Neural Network ◽  
Renal Stone ◽  
Statistical Approach ◽  
Region Of Interest ◽  
Stone Disease ◽  
True Negative ◽  
The Neural Network ◽  
Neural Network System ◽  
Percent Accuracy ◽  
And Training

<p><strong>Introduction:</strong> We sought to develop a system to predict the fragmentation of stones using non-contrast computed tomography (NCCT) image analysis of patients with renal stone disease.</p><p><strong>Methods:</strong> The features corresponding to first order statistical (FOS) method were extracted from the region of interest in the NCCT scan image of patients undergoing extracorporeal shockwave lithotripsy (ESWL) treatment and the breakability was predicted using neural network.</p><p><strong> Results:</strong> When mean was considered as the feature, the results indicated that the model developed for prediction had sensitivity of 80.7% in true positive (TP) cases. The percent accuracy in identifying correctly the TP and true negative (TN) cases was 90%. TN cases were identified with a specificity of 98.4%.</p><p><strong>Conclusions:</strong> Application of statistical methods and training the neural network system will enable accurate prediction of the fragmentation and outcome of ESWL treatment.</p>

Study on Data Security Technology of Neural Network System

2014 ◽  
Vol 701-702 ◽  
pp. 1041-1044
Author(s):  
Yan Wei Hong
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Security Technology ◽  
Fusion Algorithm ◽  
Basic Principles ◽  
Network Algorithm ◽  
The Neural Network ◽  
Neural Network System ◽  
Neural Network Algorithm ◽  
And Training

This paper analyzes the neural network algorithm model, introduces the basic principles and training process of BP neural network algorithm, analyzes the BP neural network weights adjustment processand the method of determining the number of nodes in each layer; in improved protocol algorithm basis LEACH-E, combined with the BP neural network algorithm, we propose a new data fusion algorithm BPDFA to reduce energy consumption to attain the network lifetime goal.

scholarly journals APPLICATION OF NEURAL NETWORK TECHNOLOGIES IN THE DENTAL CARIES FORECAST

2020 ◽  
Vol 73 (7) ◽  
pp. 1499-1504
Author(s):  
Oleksandr A. Udod ◽  
Hanna S. Voronina ◽  
Olena Yu. Ivchenkova
Keyword(s):  
Neural Network ◽  
Dental Caries ◽  
Old Patients ◽  
Software Application ◽  
The Neural Network ◽  
Software Product ◽  
High Level ◽  
Network Technologies ◽  
Python Programming ◽  
And Training

The aim: of the work was to develop and apply in the clinical trial a software product for the dental caries prediction based on neural network programming. Materials and methods: Dental examination of 73 persons aged 6-7, 12-15 and 35-44 years was carried out. The data obtained during the survey were used as input for the construction and training of the neural network. The output index was determined by the increase in the intensity of caries, taking into account the number of cavities. To build a neural network, a high-level Python programming language with the NumPay extension was used. Results: The intensity of carious dental lesions was the highest in 35-44 years old patients – 6.69 ± 0.38, in 6-7 years old children and 12-15 years old children it was 3.85 ± 0.27 and 2.15 ± 0.24, respectively (p <0.05). After constructing and training the neural network, 61 true and 12 false predictions were obtained based on these indices, the accuracy of predicting the occurrence of caries was 83.56%. Based on these results, a graphical user interface for the “CariesPro” software application was created. Conclusions: The resulting neural network and the software product based on it permit to predict the development of dental caries in persons of all ages with a probability of 83.56%.

scholarly journals Reinforcement learning and convolutional neural network system for firefighting rescue robot

2018 ◽  
Vol 161 ◽  
pp. 03028 ◽  
Author(s):  
Tien Kun Yu ◽  
Yang Ming Chieh ◽  
Hooman Samani
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Region Of Interest ◽  
Infrared Sensors ◽  
Sliding Windows ◽  
Q Learning ◽  
Rescue Robot ◽  
Neural Network System ◽  
The Face ◽  
Color Sensors

In this paper, we combine the machine learning and neural network to build some modules for the fire rescue robot application. In our research, we build the robot legs module with Q-learning. We also finish the face detection with color sensors and infrared sensors. It is usual that image fusion is done when we want to use two kinds of sensors. Kalman filter is chosen to meet our requirement. After we finish some indispensable steps, we use sliding windows to choose our region of interest to make the system’s calculation lower. The least step is convolutional neural network. We design a seven layers neural network to find the face feature and distinguish it or not.

scholarly journals Using neural networks to describe tracer correlations

2004 ◽  
Vol 4 (1) ◽  
pp. 143-146 ◽  
Author(s):  
D. J. Lary ◽  
M. D. Müller ◽  
H. Y. Mussa
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Chemical Models ◽  
The Neural Network ◽  
Pressure Time ◽  
Fortran Code ◽  
Time Of Year ◽  
Hidden Layer ◽  
And Training

Abstract. Neural networks are ideally suited to describe the spatial and temporal dependence of tracer-tracer correlations. The neural network performs well even in regions where the correlations are less compact and normally a family of correlation curves would be required. For example, the CH4-N2O correlation can be well described using a neural network trained with the latitude, pressure, time of year, and CH4 volume mixing ratio (v.m.r.). In this study a neural network using Quickprop learning and one hidden layer with eight nodes was able to reproduce the CH4-N2O correlation with a correlation coefficient between simulated and training values of 0.9995. Such an accurate representation of tracer-tracer correlations allows more use to be made of long-term datasets to constrain chemical models. Such as the dataset from the Halogen Occultation Experiment (HALOE) which has continuously observed CH4  (but not N2O) from 1991 till the present. The neural network Fortran code used is available for download.

scholarly journals FORECASTING OF THE SURFACE ROUGHNESS IN FINISHING MILLING USING NEURAL NETWORKS

2019 ◽  
Vol 4 (10) ◽  
pp. 135-141
Author(s):  
Е. Ерыгин ◽  
E. Erygin ◽  
Т. Дуюн ◽  
T. Duyun
Keyword(s):  
Neural Network ◽  
Cutting Tool ◽  
Learning Algorithm ◽  
Network Modeling ◽  
Training Sample ◽  
Neural Network Modeling ◽  
Network Learning ◽  
The Neural Network ◽  
And Training ◽  
Positive Results

This article describes the task of predicting roughness when finishing milling using neural network modeling. As a basis for the creation and training of an artificial neural network, a progressive formu-la for determining the roughness during finishing milling is chosen. The thermoEMF of the processing and processed materials is used as one of the parameters for calculating the roughness. The use of thermoEMF allows to take into account the material of the workpiece and the cutting tool, which af-fects the accuracy of the results. A training sample is created with data for five inputs and one output. The architecture, features and network learning algorithm are described. A neural network that de-termines the roughness for finishing milling has been created and configured. The process of learning and debugging of the neural network by means of graphs is clearly displayed. The network operability is checked on the test data, which allows obtaining positive results.

scholarly journals NEURAL NETWORK CLASSIFICATION METHOD FOR AIRCRAFT IN ISAR IMAGES

2019 ◽  
Vol 2 ◽  
pp. 141
Author(s):  
Krasimir Ognyanov Slavyanov
Keyword(s):  
Neural Network ◽  
Network Architecture ◽  
Synthetic Aperture ◽  
Neural Network Architecture ◽  
The Neural Network ◽  
Full Explanation ◽  
Network Method ◽  
High Level ◽  
And Training

This article offers a neural network method for automatic classification of Inverse Synthetic Aperture Radar objects represented in images with high level of post-receive optimization. A full explanation of the procedures of two-layer neural network architecture creating and training is described. The classification in the recognition stage is proposed, based on several main classes or sets of flying objects. The classification sets are designed according to distinctive specifications in the structural models of the aircrafts. The neural network is experimentally simulated in MATLAB environment. Numerical results of the experiments carried, prove the correct classification of the objects in ISAR optimized images.

Forecasting Emerging Market Indexes with Neural Networks

2011 ◽  
pp. 80-101 ◽  
Author(s):  
Steven Walczah
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Time Series ◽  
Global Knowledge ◽  
Index Change ◽  
Market Index ◽  
The Neural Network ◽  
Neural Network System ◽  
Homogeneous Models ◽  
Using Data

Forecasting financial time series with neural networks is problematic. Multiple decisions, each of which affects the performance of the neural network forecasting model, must be made, including which data to use and the size and architecture of the neural network system. While most previous research with neural networks has focused on homogenous models, that is, only using data from the single time series to be forecast, the ever more global nature of the world’s financial markets necessitates the inclusion of more global knowledge into neural network design. This chapter demonstrates how specific markets are at least partially dependent on other global markets and that inclusion of heterogeneous market information will improve neural network forecasting performance over similar homogeneous models by as much as 12 percent (i.e., moving from a near 51% prediction accuracy for the direction of the market index change to a 63% accuracy of predicting the direction of the market index change).

Based on RBF Neural Network Modeling of Well Test System

2014 ◽  
Vol 535 ◽  
pp. 606-609
Author(s):  
Jia Tian
Keyword(s):  
Neural Network ◽  
Rbf Neural Network ◽  
Effective Means ◽  
Test System ◽  
Neural Network Modeling ◽  
Well Test ◽  
Fast Learning ◽  
The Neural Network ◽  
Neural Network System ◽  
Neural Network Toolbox

The Neural Network Toolbox in MATLAB is a powerful instrument of analyzing and designing a neural network system. RBF Neural Network has small computational burden and fast learning rate and is not liable to be trapped by local minimal points etc. So it is an effective means to identify and model a system. In this paper, the Neural Network Toolbox in MATLAB and RBF Neural Network are combined to solve the problem of modeling the pressure in oilfield test well systems and the result is excellent.

On-Track Measurement of Lateral/Vertical Wheel Loads of Running Railway Vehicles Based on the Neural Network

2008 ◽  
Author(s):  
Davood Younesian ◽  
Fahim Javid ◽  
Ebrahim Esmailzadeh
Keyword(s):  
Neural Network ◽  
Probability Distribution ◽  
Contact Forces ◽  
Strain Gauges ◽  
Network Algorithm ◽  
The Neural Network ◽  
Neural Network System ◽  
Neural Network Algorithm ◽  
Vertical Contact ◽  
Electric Signals

A new approach for on-track measurement of the lateral/vertical contact forces is presented in this paper. The proposed method is based on measurement of the strain at two sides of the wheel web. Electric signals generated by the strain gauges are fed into a neural network algorithm in order to predict the lateral/vertical contact forces. Feed-forward technique is used in the neural network algorithm. A sensitivity analysis has been carried out to find the best position for the strain gauges. A dynamic model of a freight wagon is provided and a variety of numerical simulations are performed to obtain the probability distribution of the lateral and vertical contact forces. The obtained probability distribution function is then utilized to generate lateral/vertical contact forces within the practical range. In order to train the neural network system, the generated contact forces are applied to the wheel flange and the strain signals are obtained. More than 100 configurations are fed into the system in order to train it. Reliability, accuracy and sensitivity of the proposed measurement system are then investigated.

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