Artificial neural networks as an alternative approach to groundwater numerical modelling and environmental design

2008 ◽  
Vol 22 (17) ◽  
pp. 3337-3348 ◽  
Author(s):  
Ioannis K. Nikolos ◽  
Maria Stergiadi ◽  
Maria P. Papadopoulou ◽  
George P. Karatzas
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Numerical Modelling ◽  
Environmental Design ◽  
Alternative Approach ◽  
Artificial Neural

scholarly journals The Precipitation and Dissolution of Alloy Iron Carbides in Vacuum Carburization Processes for Automotive and Aircraft Applications - Part II

2012 ◽  
Vol 486 ◽  
pp. 303-308 ◽  
Author(s):  
Piotr Kula ◽  
Emilia Wołowiec ◽  
Robert Pietrasik ◽  
Konrad Dybowski ◽  
Leszek Klimek
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Numerical Modelling ◽  
Metallographic Analysis ◽  
Iron Carbides ◽  
Vacuum Carburization ◽  
Artificial Neural ◽  
Precipitation Phenomena ◽  
Alloy Iron

The article is dedicated to the experiments and tests on the phenomena of precipitation and dissolution of alloy iron carbides in vacuum carburization processes. Special attention has been paid to the possibility of using artificial neural networks to predict the speed of the processes examined. In the section below, we are presenting the precipitation phenomena taking place in vacuum carburization processes and the experiments that were conducted. Moreover, a qualitative and metallographic analysis of carbide phenomena was described together with the method of numerical modelling and predicting the processes with the use of artificial neural networks.

scholarly journals Artificial Neural Networks in Cardiology: Analysis of Numerical and Text Data

2020 ◽  
Vol 15 (1) ◽  
pp. 40-56
Author(s):  
P.S. Onishchenko ◽  
K.Y. Klyshnikov ◽  
E.A. Ovcharenko
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Text Data ◽  
Data Application ◽  
Alternative Approach ◽  
Textual Data ◽  
Artificial Neural ◽  
Prediction Systems ◽  
Network Technologies ◽  
Attending Physician

This review discusses works on the use of artificial neural networks for processing numerical and textual data. Application of a number of widely used approaches is considered, such as decision support systems; prediction systems, providing forecasts of outcomes of various methods of treatment of cardiovascular diseases, and risk assessment systems. The possibility of using artificial neural networks as an alternative approach to standard methods for processing patient clinical data has been shown. The use of neural network technologies in the creation of automated assistants to the attending physician will make it possible to provide medical services better and more efficiently.

Artificial Neural Networks for Predicting Noise from Construction Sites

1997 ◽  
Vol 4 (3) ◽  
pp. 211-221 ◽  
Author(s):  
A.H. Boussabaine
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Work Force ◽  
Prediction Methods ◽  
Noise Prediction ◽  
Construction Sites ◽  
Standard Methods ◽  
Alternative Approach ◽  
Noise Assessment ◽  
Artificial Neural

Exposure of populations to noise at their workplace and at home is a major concern for academia and industry. This concern has resulted in a number of standards for predicting the level of noise and studying the adverse noise-induced effect on the health of the work force. Among these standards is the assessment of noise for open construction sites. Current British Standard methods for noise assessment are limited in their application. The paper explains the need for noise prediction and demonstrates the limitation of the existing methods. A background introduction to Artificial Neural Networks (ANN) methods with a methodology for developing ANN applications is provided. ANN is proposed as an alternative approach to construction noise prediction methods. A model for construction noise prediction is presented and its structure is described.

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