Neural Networks and Rules-based Systems used to Find Rational and Scientific Correlations between being Here and Now with Afterlife Conditions
Neural Networks and Rules-based Systems used to Find Rational and
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Annotation-Disorders of the functional state of the gastrointestinal tract associated with the influence of various parasites are considered. The symptoms of diseases caused by parasites and their location in the gastrointestinal tract are given. The possibility of using neural network technology in diagnosing diseases as a result of the influence of various parasites is estimated. The structure of the neural network is given, indicating the set of inputs and outputs, as well as the result of training the network. For the created neural network, test results for the corresponding symptoms and disease prediction results for these symptoms were obtained.
N. T. Abdullayev. 2020. \u201cThe use of the Neural Network in Predicting a Number of Diseases of the Gastrointestinal Tract caused by Parasites\u201d. Global Journal of Science Frontier Research - I: Interdisciplinary GJSFR-I Volume 20 (GJSFR Volume 20 Issue I4): .
Crossref Journal DOI 10.17406/GJSFR
Print ISSN 0975-5896
e-ISSN 2249-4626
The methods for personal identification and authentication are no exception.
Total Score: 103
Country: Azerbaijan
Subject: Global Journal of Science Frontier Research - I: Interdisciplinary
Authors: N. T. Abdullayev, U. N. Musevi, K. S. Pashayeva (PhD/Dr. count: 0)
View Count (all-time): 152
Total Views (Real + Logic): 2138
Total Downloads (simulated): 1180
Publish Date: 2020 09, Thu
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Neural Networks and Rules-based Systems used to Find Rational and
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Annotation-Disorders of the functional state of the gastrointestinal tract associated with the influence of various parasites are considered. The symptoms of diseases caused by parasites and their location in the gastrointestinal tract are given. The possibility of using neural network technology in diagnosing diseases as a result of the influence of various parasites is estimated. The structure of the neural network is given, indicating the set of inputs and outputs, as well as the result of training the network. For the created neural network, test results for the corresponding symptoms and disease prediction results for these symptoms were obtained.
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