scholarly journals ARTIFICIAL NEURAL NETWORKS IN MEDICAL DIAGNOSIS

2021 ◽  
Vol 2 (133) ◽  
pp. 33-41
Author(s):  
Nataliya Matveeva
Keyword(s):  
Neural Networks ◽  
Type 2 Diabetes ◽  
Artificial Neural Networks ◽  
Multilayer Perceptron ◽  
Medical Diagnosis ◽  
Healthy Person ◽  
Modeling Process ◽  
Artificial Neural ◽  
Multilayer Perceptron Networks

Artificial neural networks are finding many uses in the medical diagnosis application. The article examines cases of renopathy in type 2 diabetes. Data are symptoms of disease. The multilayer perceptron networks (MLP) is used as a classifier to distinguish between a sick and a healthy person. The results of applying artificial neural networks for diagnose renopathy based on selected symptoms show the network's ability to recognize to recognize diseases corresponding to human symptoms. Various parameters, structures and learning algorithms of neural networks were tested in the modeling process.

scholarly journals The Application of Artificial Neural Networks in Predicting Children’s Giftedness

2016 ◽  
Vol 19 (1) ◽  
pp. 49-59 ◽  
Author(s):  
Nina Pavlin-Bernardić ◽  
◽  
Silvija Ravić ◽  
Ivan Pavao Matić ◽  
◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
School Readiness ◽  
Multilayer Perceptron ◽  
Gifted Students ◽  
Test Sample ◽  
Hyperbolic Tangent ◽  
Readiness Assessment ◽  
Artificial Neural

Artificial neural networks have a wide use in the prediction and classification of different variables, but their application in the area of educational psychology is still relatively rare. The aim of this study was to examine the accuracy of artificial neural networks in predicting students’ general giftedness. The participants were 221 fourth grade students from one Croatian elementary school. The input variables for artificial neural networks were teachers’ and peers’ nominations, school grades, earlier school readiness assessment and parents’ education. The output variable was the result on the Standard Progressive Matrices (Raven, 1994), according to which students were classified as gifted or non-gifted. We tested two artificial neural networks’ algorithms: multilayer perceptron and radial basis function. Within each algorithm, a number of different types of activation functions were tested. 80% of the sample was used for training the network and the remaining 20% to test the network. For a criterion according to which students were classified as gifted if their result on the Standard Progressive Matrices was in the 95th centile or above, the best model was obtained by the hyperbolic tangent multilayer perceptron, which had a high accuracy of 100% of correctly classified non-gifted students and 75% correctly classified gifted students in the test sample. When the criterion was the 90th centile or above, the best model was also obtained by the hyperbolic tangent multilayer perceptron, but the accuracy was lower: 94.7% in the classification of non-gifted students and 66.7% in the classification of gifted students. The study has shown artificial neural networks’ potential in this area, which should be further explored. Keywords: gifted students, identification of gifted students, artificial neural networks

scholarly journals ARTIFICIAL NEURAL NETWORKS: AN APPLICATION IN THE STUDY OF AIR QUALITY

2014 ◽  
Vol 13 ◽  
Author(s):  
Amaury De Souza ◽  
Hamilton Germano Pavão ◽  
Ana Paula Garcia Oliveira
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Air Quality ◽  
Multilayer Perceptron ◽  
Artificial Neural

A estimativa da concentração do ozônio de superfície propicia a geração de dados para o planejamento de previsão da qualidade do ar, útil na gestão de saúde publica. O objetivo deste trabalho foi elaborar uma Rede Neural Artificial (RNAs) para estimar a concentração do ozônio de superfície em função de dados diários de clima. A RNA, do tipo FeedForward Multilayer Perceptron, foi treinada tomando-se por referência da concentração diária do ozônio medida. Nas camadas intermediárias e de saída foram utilizadas funções de ativação do tipo tan-sigmóide e lineares, respectivamente. O desempenho da RNA desenvolvida foi muito bom, podendo-se considerá-la como integrante do conjunto de métodos indiretos para estimativa da concentração do ozônio de superfície. O modelo proposto pode ser utilizado pelo governo público como ferramenta para ativar ações de ferramentas durante os períodos de estagnação atmosférica, quando os níveis de ozônio na atmosfera possam representar riscos à saúde publica.

scholarly journals Artificial neural networks in medical diagnosis

2013 ◽  
Vol 11 (2) ◽  
pp. 47-58 ◽  
Author(s):  
Filippo Amato ◽  
Alberto López ◽  
Eladia María Peña-Méndez ◽  
Petr Vaňhara ◽  
Aleš Hampl ◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Medical Diagnosis ◽  
Artificial Neural

Artificial Neural Networks

2010 ◽  
pp. 122-128 ◽  
Author(s):  
Xiaojun Yang
Keyword(s):  
Neural Networks ◽  
Pattern Recognition ◽  
Artificial Neural Networks ◽  
Urban Growth ◽  
Hydrological Modeling ◽  
Basic Structure ◽  
Nonlinear Phenomena ◽  
Model Complex ◽  
Artificial Neural ◽  
Multilayer Perceptron Networks

Artificial neural networks are increasingly being used to model complex, nonlinear phenomena. The purpose of this chapter is to review the fundamentals of artificial neural networks and their major applications in geoinformatics. It begins with a discussion on the basic structure of artificial neural networks with the focus on the multilayer perceptron networks given their robustness and popularity. This is followed by a review on the major applications of artificial neural networks in geoinformatics, including pattern recognition and image classification, hydrological modeling, and urban growth prediction. Finally, several areas are identified for further research in order to improve the success of artificial neural networks for problem solving in geoinformatics.

Sensitivity for Multivariate Calibration Based on Multilayer Perceptron Artificial Neural Networks

2020 ◽  
Vol 92 (18) ◽  
pp. 12265-12272
Author(s):  
Fabricio A. Chiappini ◽  
Franco Allegrini ◽  
Héctor C. Goicoechea ◽  
Alejandro C. Olivieri
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Multilayer Perceptron ◽  
Multivariate Calibration ◽  
Artificial Neural

scholarly journals Application of Artificial Neural Networks to Assess the Mycological State of Bulk Stored Rapeseeds

Agriculture ◽  
2020 ◽  
Vol 10 (11) ◽  
pp. 567
Author(s):  
Jolanta Wawrzyniak
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Control Systems ◽  
Multilayer Perceptron ◽  
Activation Function ◽  
Fungal Population ◽  
Support Tool ◽  
Artificial Neural ◽  
Hidden Layer ◽  
And Storage

Artificial neural networks (ANNs) constitute a promising modeling approach that may be used in control systems for postharvest preservation and storage processes. The study investigated the ability of multilayer perceptron and radial-basis function ANNs to predict fungal population levels in bulk stored rapeseeds with various temperatures (T = 12–30 °C) and water activity in seeds (aw = 0.75–0.90). The neural network model input included aw, temperature, and time, whilst the fungal population level was the model output. During the model construction, networks with a different number of hidden layer neurons and different configurations of activation functions in neurons of the hidden and output layers were examined. The best architecture was the multilayer perceptron ANN, in which the hyperbolic tangent function acted as an activation function in the hidden layer neurons, while the linear function was the activation function in the output layer neuron. The developed structure exhibits high prediction accuracy and high generalization capability. The model provided in the research may be readily incorporated into control systems for postharvest rapeseed preservation and storage as a support tool, which based on easily measurable on-line parameters can estimate the risk of fungal development and thus mycotoxin accumulation.

BRNeural ‑ Artificial Neural Networks Simulator with Topology Multilayer Perceptron Using the Encog Framework

2016 ◽  
Vol 14 (1) ◽  
pp. 309-313 ◽  
Author(s):  
Ivo Mario Mathias ◽  
Luiz Antonio Zanlorensi Junior ◽  
Luciano Bueno Matyak ◽  
Ariangelo Hauer Dias ◽  
Robson Fernando Duda ◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Multilayer Perceptron ◽  
Artificial Neural
Sign in / Sign up

Export Citation Format

Share Document