METHOD OF DIFFERENTIAL DIAGNOSTICS OF THE NOSOLOGICAL FORM OF VIRAL HEPATITIS WITH THE APPLICATION OF NEURAL NETWORK OF CASCADE CORRELATION
An important aspect of determining the nosological form of hepatitis is the combination of input data at the beginning of the study. The use of neural networks in medicine, which have the ability to search for hidden dependencies by learning from the experience of doctors, makes it easier to work in the role of advisor. However, the question of selecting the most effective topology for a specific task remains open. This paper substantiates the need to use neural network algorithms to solve the problem of determining the nosological form of hepatitis. The analysis and selection of input factors characterizing the clinical condition of the patient, and output factors characterizing the specific nosological form of hepatitis, neural network. The algorithm, its use is described, and a cascade neural network is compared with others in the context of the problem under consideration. At the end, a description is made of the established system for determining the nosological form of hepatitis using a cascade correlation neural network, and also describes the clinical efficacy.
Keywords: neural network, viral hepatitis, nosological form of hepatitis, neural network of cascade correlation, classification.
NEURAL NETWORK MODELING OF THE INTERACTION OF LABOR MARKET SUBJECTS AND EDUCATIONAL SERVICES
T.V. Azarnova, I.L. Kashirina, A.N. Schwindt
The economy of modern Russia is characterized by a number of problems: unemployment and the unemployed population, new requirements on the part of employers for vocational education, the discrepancy between the employers ’personnel needs and the professional capabilities of university graduates. All this is a consequence of the disagreement of the most important areas of modern society – the labor market and education. The increasing complexity of tasks requiring solutions in practice leads to an increase in employers’ requirements for the level of training of graduates, which underlies the existing imbalance in the labor market according to qualitative criteria. The article presents the results of neural network modeling of the interaction of subjects of labor markets and educational services. It is shown that to assess the quality of training of specialists according to the criterion of meeting the needs of the regional labor market, indicators of the efficiency of higher education institutions can be used. The rationale for the feasibility of using graduate employment indicators for solving the problems of evaluating and analyzing the effectiveness of higher educational institutions is given. Neural network models of classification, clustering and regression are built for a comprehensive analysis of the relationship between the subjects of the labor market and educational services. Revealed the presence of a strong relationship between the performance indicators of the university and the average salary of graduates in the first year after graduation.
Keywords: : labor market, monitoring, evaluation, university efficiency, neural network.
PREDICTION OF EYE-DIAGRAM PARAMETERS FROM TRANSIENT AND GAIN-FREQUENCY CHARACTERISTICS USING NEURAL NETWORK
A capability of prediction of the eye-diagram width and height with using artificial neural network (ANN) was investigated. For this purpose, were simulated more than 750 examples of telecommunication channels with different transfer functions. Eye-diagrams were composed for all examples by means of convolution of random pulse sequence and pulse response and parameters of these eye-diagrams were measured. Some ANN was learned. Their input variables were transient characteristic delay time, raise time, magnitude of voltage peak and oscillation duration as well as a gain value at the half of clock rate. For each of predicted parameters distinct ANN was chosen for different ranges of input variables. Root mean square errors of eye-diagram parameters prediction using these ANN were in the range of 2 – 4%. Correlation coefficient of predicted and known values was more then 0,98. Sufficient decreasing of computational time is achieved compare with estimation of the eye width and height using eye-diagram modeling. This method can be used for optimization of communication channel characteristics when eye-diagram parameters are the components of the goal function.
Keywords: eye-diagram, transient characteristic, gain-frequency characteristic, neural network, approximation.
APPLICATION OF FUZZY ARTMAP NETWORK IN INTELLIGENT SYSTEMS OF INVASION DETECTION
The article deals with the organization of intelligent intrusion detection and detection systems. Research in the field of development of information security tools shows that today the most promising and flexible solutions are based on machine learning methods that can prevent damage from intrusions that were not noticed by standard means of combating computer attacks. In the proposed approach, it is proposed to use a sequential reverse search with a return to select significant features and the Fuzzy neural network ARTMAP to detect and diagnose attacks. Network Fuzzy АRTMAP is able to adapt to the dynamics of computer attacks and allows you to recognize intrusions in the information system in real time, without the need to load datasets in batches. This makes it possible to automate the analysis of safety protocols in a continuous mode. The extensive use of ART family networks in intrusion detection tasks makes it possible to consider the search for approaches that improve their performance. In this paper, the control hyperparameters network Fuzzy ARTMAP proposed to adjust automatically with the use of a genetic algorithm According to the results of the computational experiment, the reduced set of characteristics reduces the computation time by 21%. The accuracy of the classification algorithm was 100% and 99.89% for the detection stage and the diagnostic stage, respectively.
Keywords: neural network, Fuzzy ARTMAP, genetic algorithm, intrusion detection, intelligent information security systems.
APPLICATION OF A NEURO NETWORK APPROACH TO LOGICAL DATA PROBLEMS AND BUILDING INTELLIGENT DECISION-MAKING SYSTEMS
D.P. Dimitrichenko, R.A. Zhilov
The need to reduce the dimensionality of large data sets while maintaining the logical structure, as well as the detection of hidden patterns and the removal of information noise and redundancy in the description of diagnostic (recognition) objects leads to the need to construct an effective method for classifying objects in weakly formalized areas of knowledge. Logical functions that describe objects using variable-valued predicates allow us to reveal hidden regularities and eliminate redundancy in the description of objects. Ordered by means of variable-valued logical functions, object classes are the basis for the formation of the structure of cognitive maps. The purpose of this study is to create an algorithm for constructing a logical neural network based on the variable-valued logic function and justifying the possibility of applying the results obtained in the construction of cognitive maps. The theoretical possibility and algorithms allowing to make the transition from variable-valued logic functions to cognitive maps using the neural network approach are grounded. The result of this work is the procedure for constructing a cognitive map using logical neural networks built on the basis of variable-valued logical functions. The advantage of the obtained cognitive map is the possibility of functioning within the framework of fuzzy logic.
Keywords: predicate, predicate significance, variable-valued logical function, logical neural network, cognitive map, cluster analysis, neural network.
NETWORK-BASED SIMULATION OF THE RESULTS MONITORING EVALUATION OF ACTIVITY OF UNIVERSITIES
Y.E. Lvovich, I.L. Kashirina, A.N. Schwindt
The article presents the results of neural network modeling of the interrelation between the indicators of monitoring the effectiveness of the activity of universities and monitoring of the employment of graduates. The proposed models provide an opportunity to predict the impact of certain indicators of the effectiveness of the university on changing conditions for the success of graduates and identify indicators that have the strongest impact on the effectiveness of the employment process. Since the employment of graduates is the resultant indicator of the educational activity of the university and one of the key indicators of its relevance, the problem solved in the article seems to be relevant. In the course of the study it was possible to build neural network models with a sufficiently high degree of reliability. At the same time, it was found that 57 indicators of monitoring the performance of HEIs are significant for forecasting the proportion of graduates who have found employment during the calendar year following the year of release, and only 3 monitoring indicators are significant for forecasting the average amount of monthly payments to graduates in the first year after graduation from the university effectiveness. Based on the results of the study, a conclusion was made about the impact of modeling results on the relationship between the indicators of employment monitoring and performance monitoring on adjusting management decisions and improving the educational activity of the university.
Keywords: : monitoring, evaluation, efficiency of the university, neural network, multilayer perceptron.