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 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.

scholarly journals An artificial neural network for analysis of ionograms obtained by ionosonde at the Ukrainian Antarctic Akademik Vernadsky station

2020 ◽  
pp. 59-67
Author(s):  
O. Bogomaz ◽  
◽  
M. Shulha ◽  
D. Kotov ◽  
A. Koloskov ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Optimization Method ◽  
Network Development ◽  
Number Of Layers ◽  
The Neural Network ◽  
Manual Processing ◽  
Artificial Neural ◽  
Python Programming ◽  
And Training

The article presents the developed artificial neural network for F2 ionosphere layer traces scaling on ionograms obtained using the IPS-42 ionosonde installed at the Ukrainian Antarctic Akademik Vernadsky station. The parameters of the IPS-42 ionosonde and the features of the data obtained with it, in particular the format of the output files, are presented. The advantages of using an artificial neural network for identification of traces on ionograms are demonstrated. Usually, an automatic scaling of the ionograms requires a lot of machine time however implementation of an artificial neural network speeds up computations significantly allowing to process incoming ionograms even in the real time mode. The choice of architecture of an artificial neural network is substantiated. The U-Net architecture was chosen. The method of creating and training the neural network is described. The artificial neural network development process included choosing the number of layers, types of activation functions, optimization method and input layer size. Software developed was written in Python programming language with use of the Keras library. Examples of data used for training of the artificial neural network are shown. The results of testing an artificial neural network are presented. The data obtained with the artificial neural network are compared with the results of manual processing of ionograms. Data for training the artificial neural network were obtained in March, 2017 using the IPS-42 ionosonde installed at the Ukrainian Antarctic Akademik Vernadsky station; data for testing were obtained in 2017 and 2020. The developed artificial neural network has minor flaws but they are easily eliminated by retraining the network on a more representative dataset (obtained in various years and seasons). The general results of testing indicate good prospects in further developing this artificial neural network and software for working with it.

scholarly journals Real-Time Adversarial Attack Detection with Deep Image Prior Initialized as a High-Level Representation Based Blurring Network

Electronics ◽  
2020 ◽  
Vol 10 (1) ◽  
pp. 52
Author(s):  
Richard Evan Sutanto ◽  
Sukho Lee
Keyword(s):  
Neural Network ◽  
Attack Detection ◽  
Detection Methods ◽  
Defense System ◽  
Image Prior ◽  
The Neural Network ◽  
Adversarial Examples ◽  
Deep Image ◽  
Adversarial Attack ◽  
High Level

Several recent studies have shown that artificial intelligence (AI) systems can malfunction due to intentionally manipulated data coming through normal channels. Such kinds of manipulated data are called adversarial examples. Adversarial examples can pose a major threat to an AI-led society when an attacker uses them as means to attack an AI system, which is called an adversarial attack. Therefore, major IT companies such as Google are now studying ways to build AI systems which are robust against adversarial attacks by developing effective defense methods. However, one of the reasons why it is difficult to establish an effective defense system is due to the fact that it is difficult to know in advance what kind of adversarial attack method the opponent is using. Therefore, in this paper, we propose a method to detect the adversarial noise without knowledge of the kind of adversarial noise used by the attacker. For this end, we propose a blurring network that is trained only with normal images and also use it as an initial condition of the Deep Image Prior (DIP) network. This is in contrast to other neural network based detection methods, which require the use of many adversarial noisy images for the training of the neural network. Experimental results indicate the validity of the proposed method.

scholarly journals Prediction of Mining Conditions in Geotechnically Complex Sites

Mining ◽  
2021 ◽  
Vol 1 (3) ◽  
pp. 279-296
Author(s):  
Marc Elmouttie ◽  
Jane Hodgkinson ◽  
Peter Dean
Keyword(s):  
Neural Network ◽  
Historical Data ◽  
Synthetic Data ◽  
Mining Operations ◽  
Body Of Knowledge ◽  
Future Production ◽  
The Neural Network ◽  
Mining Conditions ◽  
Parameter Interactions ◽  
High Level

Geotechnical complexity in mining often leads to geotechnical uncertainty which impacts both safety and productivity. However, as mining progresses, particularly for strip mining operations, a body of knowledge is acquired which reduces this uncertainty and can potentially be used by mining engineers to improve the prediction of future mining conditions. In this paper, we describe a new method to support this approach based on modelling and neural networks. A high-level causal model of the mining operations based on historical data for a number of parameters was constructed which accounted for parameter interactions, including hydrogeological conditions, weather, and prior operations. An artificial neural network was then trained on this historical data, including production data. The network can then be used to predict future production based on presently observed mining conditions as mining proceeds and compared with the model predictions. Agreement with the predictions indicates confidence that the neural network predictions are properly supported by the newly available data. The efficacy of this approach is demonstrated using semi-synthetic data based on an actual mine.

Fitting potential energy surfaces to sum-of-products form with neural networks using exponential neurons

2017 ◽  
Vol 16 (05) ◽  
pp. 1730001 ◽  
Author(s):  
Alex Brown ◽  
E. Pradhan
Keyword(s):  
Neural Network ◽  
Potential Energy ◽  
Ab Initio ◽  
Potential Energy Surfaces ◽  
Quantum Dynamics ◽  
The Neural Network ◽  
Sum Of Products ◽  
High Level ◽  
Energy Surfaces ◽  
Dynamics Problems

In this paper, the use of the neural network (NN) method with exponential neurons for directly fitting ab initio data to generate potential energy surfaces (PESs) in sum-of-product form will be discussed. The utility of the approach will be highlighted using fits of CS2, HFCO, and HONO ground state PESs based upon high-level ab initio data. Using a generic interface between the neural network PES fitting, which is performed in MATLAB, and the Heidelberg multi-configuration time-dependent Hartree (MCTDH) software package, the PESs have been tested via comparison of vibrational energies to experimental measurements. The review demonstrates the potential of the PES fitting method, combined with MCTDH, to tackle high-dimensional quantum dynamics problems.

A neural network potential energy surface for the NaH2 system and dynamics studies on the H(2S) + NaH(X1Σ+) → Na(2S) + H2(X1Σg+) reaction

2017 ◽  
Vol 19 (30) ◽  
pp. 19873-19880 ◽  
Author(s):  
Shufen Wang ◽  
Jiuchuang Yuan ◽  
Huixing Li ◽  
Maodu Chen
Keyword(s):  
Neural Network ◽  
Potential Energy ◽  
Potential Energy Surface ◽  
Energy Surface ◽  
Neural Network Method ◽  
The Neural Network ◽  
Network Method ◽  
High Level

A new potential energy surface of the NaH2 system is obtained using the neural network method based on high-level energies.

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 Feature-Based Interpretation of the Deep Neural Network

Electronics ◽  
2021 ◽  
Vol 10 (21) ◽  
pp. 2687
Author(s):  
Eun-Hun Lee ◽  
Hyeoncheol Kim
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Deep Neural Network ◽  
Lower Layer ◽  
Network Models ◽  
Neural Network Models ◽  
Network Pruning ◽  
The Neural Network ◽  
Single Instance ◽  
High Level

The significant advantage of deep neural networks is that the upper layer can capture the high-level features of data based on the information acquired from the lower layer by stacking layers deeply. Since it is challenging to interpret what knowledge the neural network has learned, various studies for explaining neural networks have emerged to overcome this problem. However, these studies generate the local explanation of a single instance rather than providing a generalized global interpretation of the neural network model itself. To overcome such drawbacks of the previous approaches, we propose the global interpretation method for the deep neural network through features of the model. We first analyzed the relationship between the input and hidden layers to represent the high-level features of the model, then interpreted the decision-making process of neural networks through high-level features. In addition, we applied network pruning techniques to make concise explanations and analyzed the effect of layer complexity on interpretability. We present experiments on the proposed approach using three different datasets and show that our approach could generate global explanations on deep neural network models with high accuracy and fidelity.

scholarly journals Assessment of Technical Condition of Polyurethane Foam Thermal Insulation Pipelines of Heating Networks Using Neural Network Technologies

2018 ◽  
Vol 7 (4.7) ◽  
pp. 241 ◽  
Author(s):  
Emil R. Saifullin ◽  
Shamil G. Ziganshin ◽  
Yury V. Vankov ◽  
Airat R. Zagretdinov
Keyword(s):  
Neural Network ◽  
Polyurethane Foam ◽  
Experimental Studies ◽  
Back Propagation ◽  
Acoustic Signals ◽  
Technical Condition ◽  
The Neural Network ◽  
Network Technologies ◽  
Heating Networks ◽  
Neural Network Technologies

Pipelines of heat networks are an important element of heat supply to cities and industrial facilities. To increase the reliability of the operation of pipelines of heating networks, reducing the number of their accidents and increasing the economic parameters of transportation of heat energy, it is required to constantly increase the volumes and quality of complex diagnostics. The instruments currently used for the diagnosis of pipelines have many shortcomings. Among them, low reliability of detection of defects and subjectivity of decision-making, as well as lack of funds for diagnostics of pre-insulated pipelines (in polyurethane foam insulation). To simplify, accelerate and improve the reliability of monitoring the technical condition of pipelines, the authors set the goal of diagnosing the object of research using acoustic methods, using neural network technologies to process acoustic signals. The article describes experimental studies of pipelines of heating networks in polyurethane foam insulation with various sizes of defects and an analysis of the acoustic signals obtained at the same time is made. The frequency of natural oscillations of the pipeline is chosen as the determining parameter of the acoustic signal. To process and analyze the frequencies obtained as a result of the experiments, a neural network of back propagation of the error was constructed.The results of the classification of the neural network of back propagation of the error trained by the neural network showed its good ability to analyze unknown samples and a high degree of reliability of their recognition.   

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