COVID-19 CORONAVIRUS SCREENING ANALYSIS NEURAL NETWORK TECHNOLOGY

2021 ◽  
Vol 2 (64) ◽  
pp. 53-57
Author(s):  
S. Aloshyn ◽  
I. Khomenko ◽  
N. Fursova
Keyword(s):  
Neural Networks ◽  
Pattern Recognition ◽  
Artificial Neural Networks ◽  
Low Cost ◽  
Signs And Symptoms ◽  
Multidimensional Space ◽  
Diagnostic Systems ◽  
Coronavirus Infection ◽  
Artificial Neural ◽  
High Degree

Low-cost, reliable and quick screening diagnosis of coronavirus can be implemented on the basis of intelligent technologies for analyzing a set of signs and symptoms with solving the problem of pattern recognition in the basis of artificial neural networks. The high degree of coronavirus infection diagnostic procedure uncertainty, the vector dimension of input factor-symptoms, fuzzy conditioning and poor formalizability of the subject condition connection with these symptoms require appropriate analytical tools. An analysis of the problem and possible solutions allows justifying the feasibilit y of implementing screening diagnostics as a solution to the problem of nonlinear optimization in a multidimensional space of high-dimensional factors and states. Artificial neural networks with compulsory training on a representative sample were chosen as a tool for implementing the project. The proposed technology brings diagnostics of coronavirus infection closer to full automation, robotization and intellectualization of complex monitoring (diagnostic) systems as the most promising technology for pattern recognition in systems with a high degree of entropy and allows you to solve the problem at the lowest cost and required performance indicators.

Methods and tools of ensuring the operational reliability of complex industrial facilities.

2020 ◽  
Vol 14 (1) ◽  
pp. 34-42
Author(s):  
A. VAZHYNSKYI ◽  
◽  
S. ZHUKOV ◽  
Keyword(s):  
Neural Networks ◽  
Pattern Recognition ◽  
Artificial Neural Networks ◽  
Expert Systems ◽  
Residual Life ◽  
Full Potential ◽  
Scientific Publications ◽  
Specific Element ◽  
Industrial Facilities ◽  
Artificial Neural

Approaches and algorithms for processing experimental data and data obtained as a result of using modern means of measuring equipment, selecting diagnostic parameters, pattern recognition, which constitute the methodological basis for developing methods and designing tools for creating a service system for complex industrial facilities based on predicting their performance and residual life are described in submitted article. Along with classical methods, methods based on using the full potential of the modern elemental base of microprocessor technology and the use of artificial neural networks, machine learning, and "big data" are discovered. The given examples can serve as the basis for constructing a methodology for the application of the considered approaches for organizing predictive maintenance of complex industrial equipment. An analytical review of a number of scientific publications showed that the creation of new automated diagnostic systems that can increase fault tolerance and extend the life of sophisticated modern power equipment is extremely relevant. For this, various approaches are applied, based on mathematical models, expert systems, artificial neural networks and other algorithms. Summarizing the results of scientific publications, it can be argued that the implementation of a systematic approach to the organization of repair service at the enterprise requires a comprehensive solution to the following urgent problems: • monitoring is formulated as the task of interrogating sensors and collecting information necessary for further analysis; • diagnostics, it is solved as tasks of identifying informative signs with further detection and classification of failures and anomalies in data sets; • improving the accuracy of algorithms aimed at pattern recognition; • condition forecasting is the task of assessing the current and accumulated readings of monitoring systems for making decisions regarding either a specific element of the complex or the facilities. Thus, modern technology make it possible to arrange arbitrarily complex algorithms. However, to use the full potential that artificial neural networks, expert systems, and classical methods for identifying and diagnosing equipment it is necessary to have a conceptual development of the foundations of building systems for organizing maintenance and repair of complex energy equipment

scholarly journals Prediction of Solar Irradiance Based on Artificial Neural Networks

Inventions ◽  
2019 ◽  
Vol 4 (3) ◽  
pp. 45 ◽  
Author(s):  
Waleed I. Hameed ◽  
Baha A. Sawadi ◽  
Safa J. Al-Kamil ◽  
Mohammed S. Al-Radhi ◽  
Yasir I. A. Al-Yasir ◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Solar Irradiance ◽  
Open Circuit Voltage ◽  
Low Cost ◽  
Short Circuit ◽  
Short Circuit Current ◽  
Open Circuit ◽  
Pv Module ◽  
Artificial Neural

Prediction of solar irradiance plays an essential role in many energy systems. The objective of this paper is to present a low-cost solar irradiance meter based on artificial neural networks (ANN). A photovoltaic (PV) mathematical model of 50 watts and 36 cells was used to extract the short-circuit current and the open-circuit voltage of the PV module. The obtained data was used to train the ANN to predict solar irradiance for horizontal surfaces. The strategy was to measure the open-circuit voltage and the short-circuit current of the PV module and then feed it to the ANN as inputs to get the irradiance. The experimental and simulation results showed that the proposed method could be utilized to achieve the value of solar irradiance with acceptable approximation. As a result, this method presents a low-cost instrument that can be used instead of an expensive pyranometer.

scholarly journals A State-Based Sensitivity Analysis for Distinguishing the Global Importance of Predictor Variables in Artificial Neural Networks

2016 ◽  
Vol 2016 ◽  
pp. 1-11 ◽  
Author(s):  
Ehsan Ardjmand ◽  
David F. Millie ◽  
Iman Ghalehkhondabi ◽  
William A. Young II ◽  
Gary R. Weckman
Keyword(s):  
Neural Networks ◽  
Sensitivity Analysis ◽  
Artificial Neural Networks ◽  
Network Models ◽  
Predictor Variables ◽  
Apparent Lack ◽  
Artificial Neural ◽  
High Degree ◽  
Universal Approximators ◽  
Empirical Approaches

Artificial neural networks (ANNs) are powerful empirical approaches used to model databases with a high degree of accuracy. Despite their recognition as universal approximators, many practitioners are skeptical about adopting their routine usage due to lack of model transparency. To improve the clarity of model prediction and correct the apparent lack of comprehension, researchers have utilized a variety of methodologies to extract the underlying variable relationships within ANNs, such as sensitivity analysis (SA). The theoretical basis of local SA (that predictors are independent and inputs other than variable of interest remain “fixed” at predefined values) is challenged in global SA, where, in addition to altering the attribute of interest, the remaining predictors are varied concurrently across their respective ranges. Here, a regression-based global methodology, state-based sensitivity analysis (SBSA), is proposed for measuring the importance of predictor variables upon a modeled response within ANNs. SBSA was applied to network models of a synthetic database having a defined structure and exhibiting multicollinearity. SBSA achieved the most accurate portrayal of predictor-response relationships (compared to local SA and Connected Weights Analysis), closely approximating the actual variability of the modeled system. From this, it is anticipated that skepticisms concerning the delineation of predictor influences and their uncertainty domains upon a modeled output within ANNs will be curtailed.

Artificial Neural Networks and Their Applications in Business

2018 ◽  
pp. 6642-6657
Author(s):  
Trevor J. Bihl ◽  
William A. Young II ◽  
Gary R. Weckman
Keyword(s):  
Neural Networks ◽  
Pattern Recognition ◽  
Big Data ◽  
Artificial Neural Networks ◽  
Model Complexity ◽  
Ann Model ◽  
Processing Methods ◽  
Starting Point ◽  
Clustering And Classification ◽  
Artificial Neural

Despite the natural advantage humans have for recognizing and interpreting patterns, large and complex datasets, as in Big Data, preclude efficient human analysis. Artificial neural networks (ANNs) provide a family of pattern recognition approaches for prediction, clustering and classification applicable to KDD with ANN model complexity ranging from simple (for small problems) highly complex (for large issues). To provide a starting point for readers, this chapter first describes foundational concepts that relate to ANNs. A listing of commonly used ANN methods, heuristics, and criteria for initializing ANNs is then discussed. Common pre- and post- data processing methods for dimensionality reduction and data quality issues are then described. The authors then provide a tutorial example of ANN analysis. Finally, the authors list and describe applications of ANNs to specific business related endeavors for further reading.

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