scholarly journals Application of the Kohonen neural network for monitoring tissue oxygen supply under hypoxic conditions

2021 ◽  
Vol 2086 (1) ◽  
pp. 012116
Author(s):  
M S Mazing ◽  
A Y Zaitceva ◽  
R V Davydov
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
The Body ◽  
Practical Significance ◽  
Compensatory Response ◽  
Kohonen Neural Network ◽  
Hypoxic Conditions ◽  
Ecological Physiology ◽  
Neural Network Structure ◽  
Artificial Neural

Abstract The article presents the results of application of the Kohonen artificial neural network (KANN) in assessing the oxygen status of human tissues, as well as for studying the adaptive-compensatory response of the body to functional load. In the experiment, the registered digital oxygen images of the tissue of 31 subjects were distributed into three classes using the KANN. Each group is characterised by different resistance of the organism to hypoxia. The research results have shown the effectiveness of using an artificial neural network structure and the possibility of its implementation for recognition of the functional state of a person under conditions of metabolic hypoxia; it seems relevant and has theoretical and practical significance in the framework of ecological physiology.

scholarly journals Adaptation of the Diagnostic Artificial Neural Network Structure When New Training Examples Appear

2020 ◽  
Vol 6 (4) ◽  
pp. 120-126
Author(s):  
A. Malikov
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Information Security ◽  
Classification Problem ◽  
Data Set ◽  
Neural Network Structure ◽  
Artificial Neural ◽  
Training Examples ◽  
Information Image

In this paper we can see that identified computer incidents are subject for diagnostics, during which the characteristics of information security violations are clarified (purpose, causes, consequences, etc.). To diagnose computer incidents, we can use methods of automation while collection and processing the events that occur as a result of the implementation of scenarios for information security violations. Artificial neural networks can be used to solve the classification problem of assigning diagnostic data set (information image of a computer incident) to one of the possible values of the violation characteristic. The purpose of this work is to adapt the structure of an artificial neural network that allows the accuracy diagnostics of computer incidents when new training examples appear.

IDENTIFICATION OF VAGINA AND PELVIS FROM IRIS REGION USING ARTIFICIAL NEURAL NETWORK

2015 ◽  
Vol 76 (7) ◽  
Author(s):  
Nor’aini A.J. ◽  
Syahrul Akram Z. A. ◽  
Azilah S.
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Iris Recognition ◽  
Principal Component ◽  
Body Part ◽  
Medical Application ◽  
The Body ◽  
Correct Identification ◽  
Feed Forward Neural Network ◽  
Artificial Neural

Iris recognition not only can be used in biometrics technology but also in medical application by identifying the region that relates to the body part.  This paper describes a technique for identification of vagina and pelvis regions from iris region using Artificial Neural Network (ANN) based on iridology chart whereby the ANN process utilized Feed Forward Neural Network (FFNN).  The localization of the iris is carried out using two methods namely Circular Boundary Detector (CBD) and Circular Hough Transform (CHT). The iris is segmented based on the iridology chart and unwrapped into polar form using Daugman’s Rubber Sheet Model.  The vagina and pelvis regions are cropped into pixel size of 40x7 for feature extraction using Principal component Analysis (PCA) and classified using FFNN.  In the experiments, 15 pelvis and 20 vagina regions are used for classification. The best result obtained gives overall correct identification from localization using CBD and CHT of about 67% and 81% respectively.  From the experiments, it is observed that vagina and pelvis regions are able to be identified even though the results obtained are not 100% accurate. 

Prediction of 2-D Lift Motions Using Artificial Neural Networks: An Evolutionary Approach

2005 ◽  
Vol 49 (14) ◽  
pp. 1405-1409
Author(s):  
Miguel A. Perez ◽  
Maury A. Nussbaum
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Artificial Neural Networks ◽  
Model Performance ◽  
Human Movement ◽  
Two Dimensional ◽  
Modeling Paradigm ◽  
Neural Network Structure ◽  
Artificial Neural

Movement prediction is an important aspect of human simulation, where more efficient and accurate models are needed. Artificial neural networks could potentially serve as a modeling option in this realm. This investigation evaluates the performance of a particular artificial neural network structure in modeling sagittally symmetric two-dimensional lifting and lowering movements. Model performance was evaluated using three training datasets, each consisting of distinct representation levels of the overall dataset. Results are discussed in terms of their practical meaning, and suggestions for future improvements in the modeling scheme are provided. Overall, artificial neural networks show promise as a modeling paradigm for the prediction of human movement.

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