scholarly journals Applying artificial neural networks in slope stability related phenomena

2016 ◽  
Vol 47 (4) ◽  
pp. 1901
Author(s):  
P. Tsangaratos ◽  
A. Benardos
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Slope Stability ◽  
Landslide Susceptibility ◽  
Learning Algorithm ◽  
The Past ◽  
Artificial Neural ◽  
Gis Environment ◽  
Specific Learning ◽  
The Way

Over the past years, Artificial Neural Networks (ANN) have been successfully used for the modelling in a great number of geoscience applications. In this paper we discuss the architecture and the way ANN work, presenting a specific learning algorithm which has been applied in the estimation of landslide susceptibility within a GIS environment.

scholarly journals Augmented reality using artificial neural networks –a review

2019 ◽  
Vol 8 (4) ◽  
pp. 603
Author(s):  
Sreekumar Narayanan ◽  
Srinath Doss
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Augmented Reality ◽  
Effective Utilization ◽  
The Past ◽  
Diverse Application ◽  
The Common ◽  
Artificial Neural ◽  
The Military ◽  
The Way

The present paper reviews the areas where Augmented Reality (AR) has been used in Artificial Neural Networks (ANN) (Artificial Neural Networks). The focus on systems based on AR is largely on enhancing technologies in diverse application areas such as; defense, robotics, medical, manufacturing, education, entertainment, assisted driving, maintenance and mobile assistance. However, AR is now finding much usage in ANN. The research considered a review based methodology wherein most studies conducted in the past on AR and ANN were reviewed. AR with ANN has profound applications in various sectors and has been developed in an extended way but still has some distance to go afore industries, the military and the common public will receive it as a accustomed user interface. AR would modernize the way people animate and the way industries endeavor by effective utilization. There is an incredible potential in fields such as construction, art, architecture, repair and manufacturing with mediated reality and well-organized visualization through AR.  

scholarly journals INDUSTRIAL APPLICATIONS OF ARTIFICIAL NEURAL NETWORKS

2014 ◽  
pp. 8-20
Author(s):  
Kurosh Madani
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Real World ◽  
Industrial Applications ◽  
Large Field ◽  
Nonlinear Functions ◽  
The Past ◽  
Real World Applications ◽  
Ann Models ◽  
Artificial Neural

In a large number of real world dilemmas and related applications the modeling of complex behavior is the central point. Over the past decades, new approaches based on Artificial Neural Networks (ANN) have been proposed to solve problems related to optimization, modeling, decision making, classification, data mining or nonlinear functions (behavior) approximation. Inspired from biological nervous systems and brain structure, Artificial Neural Networks could be seen as information processing systems, which allow elaboration of many original techniques covering a large field of applications. Among their most appealing properties, one can quote their learning and generalization capabilities. The main goal of this paper is to present, through some of main ANN models and based techniques, their real application capability in real world industrial dilemmas. Several examples through industrial and real world applications have been presented and discussed.

Methods of Evaluation for Region’s Landslide Susceptibility. Short Overview

2017 ◽  
Vol 6 (3) ◽  
pp. 57-60
Author(s):  
Денис Кривогуз ◽  
Denis Krivoguz
Keyword(s):  
Neural Networks ◽  
Logistic Regression ◽  
Artificial Neural Networks ◽  
Discriminant Analysis ◽  
Linear Discriminant Analysis ◽  
Landslide Susceptibility ◽  
Susceptibility Assessment ◽  
Linear Discriminant ◽  
Landslide Susceptibility Assessment ◽  
Artificial Neural

Modern approaches to the region’s landslide susceptibility assessment are considered in this paper. Have been presented descriptions of the most used techniques for landslide susceptibility assessment: logistic regression, indicator validity, linear discriminant analysis and application of artificial neural networks. These techniques’ advantages and disadvantages are discussed in the paper. The most suitable techniques for various conditions of analysis have been marked. It has been concluded that the most acceptable techniques of analysis for a large number of input data related to the studied region are the method of logistic regression and indicator validity method. With these methods the most accurate results are achieved. When there is a lack of information, it is more expedient to use linear discriminant analysis and artificial neural networks that will minimize potential analysis inaccuracies.

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