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 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 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.

scholarly journals Penerapan Algoritma Backpropagation Pada Game Pengenalan Nahwu Di Mi Darul Falah Jepara

2019 ◽  
Vol 5 (2) ◽  
pp. 103
Author(s):  
R Hadapiningradja Kusumodestoni ◽  
Adi Sucipto ◽  
Sela Nur Ismiati ◽  
M Novailul Abid
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Technological Development ◽  
Technological Advancement ◽  
Artificial Neural ◽  
Academic Year ◽  
Class Iv ◽  
The Way

Nahwu is a science that studies Arabic grammar. Nevertheless, the interest of students in learning nahwu is currently decreasing. It happens because the technological advancement vastly develops but the way of learning it is still conventional and tends to be boring. Based on the school data for the academic year of 2017/2018 at MI Darul Falah Sirahan Cluwak Pati, there were only 20 students able to undestand nahwu well out of 60 students who started learning nahwu in class IV. Technological development has brought many changes to things around us. One of the most developed at the meantime is game. Lately games have become something very fast developing. Using the game to be used as a secondary learning media for students in learning nahwu is considered quite effective. The method used in designing this game was Backpropagation meaning an algorithm based on artificial neural networks which is used to determine and take decision that is used to determine scores and levels in the Nahwu Introduction Game. The tools used in this game-making are construct 2, an HTML5-based game maker specifically for the 2d platform. The results of this study were an Android-based Nahwu Introduction Game.

scholarly journals In search of a warning strategy against exchange-rate attacks: Forecasting tactics using artificial neural networks

2000 ◽  
Vol 5 (2) ◽  
pp. 121-137
Author(s):  
A. S. Andreou ◽  
G. A. Zombanakis ◽  
E. F. Georgopoulos ◽  
S. D. Likothanassis
Keyword(s):  
Neural Networks ◽  
Genetic Algorithms ◽  
Artificial Neural Networks ◽  
Discrete Dynamics ◽  
Domestic Currency ◽  
Heart Attacks ◽  
The Past ◽  
Forecasting Performance ◽  
Successful Prediction ◽  
Artificial Neural

“Heart attacks and devaluations are not predictable and, certainly, are never preannounced”. (The usual remark made by government spokesmen shortly after a domestic currency devaluation has taken place.)The contribution that this paper aspires to make is the prediction of an oncoming attack against the domestic currency, something that is expected to increase the possibilities of successful hedging by the authorities. The analysis has focused on the Greek Drachma, which has suffered a series of attacks during the past few years, thus offering a variety of such “shock” incidents accompanied by frequent interventions by the authorities. The prediction exercised here is performed in a discrete dynamics environment, based on the daily fluctuations of the interbank overnight interest rate, using artificial neural networks enhanced by genetic algorithms. The results obtained on the basis of the forecasting performance have been considered most encouraging, in providing a successful prediction of an oncoming attack against the domestic currency.

scholarly journals Neural Networks for Tested MG Artillery Fuses

2019 ◽  
Vol 49 (4) ◽  
pp. 157-186
Author(s):  
Dariusz Ampuła
Keyword(s):  
Neural Networks ◽  
Computer Simulation ◽  
Artificial Neural Networks ◽  
Test Results ◽  
Evaluation Data ◽  
Technical Parameters ◽  
Artificial Neural ◽  
Learning Test ◽  
The Way

Abstract The article presents the information about the usage of artificial neural networks. The automation process of neural networks of the analysed evaluation data results is highlighted. The kinds of MG type artillery fuses are described and the kinds of cartridges’ calibres, in which they are used, are also specified. The way of preparation of databases of test results to computer simulation is described. Building of neural networks determining the main technical parameters and sizes of learning, test and validation sets is characterized. The summary for chosen active neural networks for individual kinds of the analysed MG type artillery fuses is presented. Graphs of learning, values of sensibility indicators and fragments of prediction sheets for the chosen neural networks were shown.

scholarly journals EVALUATION OF ARTIFICIAL NEURAL NETWORKS EFFECTIVENESS FOR UNFOLDING GAMMA-SPECTRUM OF 137CS

2021 ◽  
Vol 2 ◽  
pp. 44-54
Author(s):  
Aleksander N. Nikitin ◽  
◽  
Egor V. Mischenko ◽  
Olga A. Shurankova ◽  
◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Radiation Source ◽  
Γ Radiation ◽  
Spectrometry Analysis ◽  
Convolution Neural Networks ◽  
Spectrum Unfolding ◽  
The Common ◽  
Artificial Neural ◽  
Fully Connected

Development of machine learning methods for spectrum processing is one of the most promising ways for gamma- spectrometry automation and accuracy improvement. Effectiveness of fully connected and convolution neural networks for quantitative γ-spectrometry analysis using scintillation detector NaI(Tl) and lead shielding is presented in the article. Semi-synthetic spectrums were used for the models training; the semi-synthetic spectrums are in channels additions of random spectrums measured at a short duration. The analysis shows advantages of artificial neural networks compare to the common analytical method of spectrum unfolding. The mean square error of activity evaluation is 2–4 times lower than the common method if measuring time is equal to 100 s. In highly standardized conditions of measuring, the advantages of convolution neural networks appear with increasing radiation source activity. Validation with sources not used in training of neural networks has shown fully connected and convolution neural networks can have advantages over the standard method when activity of γ-radiation source is relatively high.

scholarly journals Forecasting Energy Use in Buildings Using Artificial Neural Networks: A Review

Energies ◽  
2019 ◽  
Vol 12 (17) ◽  
pp. 3254 ◽  
Author(s):  
Jason Runge ◽  
Radu Zmeureanu
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Energy Consumption ◽  
Energy Use ◽  
Building Energy ◽  
Data Driven ◽  
Past Century ◽  
Energy Forecasting ◽  
The Past ◽  
Artificial Neural

During the past century, energy consumption and associated greenhouse gas emissions have increased drastically due to a wide variety of factors including both technological and population-based. Therefore, increasing our energy efficiency is of great importance in order to achieve overall sustainability. Forecasting the building energy consumption is important for a wide variety of applications including planning, management, optimization, and conservation. Data-driven models for energy forecasting have grown significantly within the past few decades due to their increased performance, robustness and ease of deployment. Amongst the many different types of models, artificial neural networks rank among the most popular data-driven approaches applied to date. This paper offers a review of the studies published since the year 2000 which have applied artificial neural networks for forecasting building energy use and demand, with a particular focus on reviewing the applications, data, forecasting models, and performance metrics used in model evaluations. Based on this review, existing research gaps are identified and presented. Finally, future research directions in the area of artificial neural networks for building energy forecasting are highlighted.

scholarly journals Autonomous Evolution of Topographic Regularities in Artificial Neural Networks

2010 ◽  
Vol 22 (7) ◽  
pp. 1860-1898 ◽  
Author(s):  
Jason Gauci ◽  
Kenneth O. Stanley
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Evolutionary Algorithms ◽  
Neural Computation ◽  
Topographic Maps ◽  
The Novel ◽  
The Past ◽  
Connectivity Patterns ◽  
Artificial Neural ◽  
Autonomous Evolution

Looking to nature as inspiration, for at least the past 25 years, researchers in the field of neuroevolution (NE) have developed evolutionary algorithms designed specifically to evolve artificial neural networks (ANNs). Yet the ANNs evolved through NE algorithms lack the distinctive characteristics of biological brains, perhaps explaining why NE is not yet a mainstream subject of neural computation. Motivated by this gap, this letter shows that when geometry is introduced to evolved ANNs through the hypercube-based neuroevolution of augmenting topologies algorithm, they begin to acquire characteristics that indeed are reminiscent of biological brains. That is, if the neurons in evolved ANNs are situated at locations in space (i.e., if they are given coordinates), then, as experiments in evolving checkers-playing ANNs in this letter show, topographic maps with symmetries and regularities can evolve spontaneously. The ability to evolve such maps is shown in this letter to provide an important advantage in generalization. In fact, the evolved maps are sufficiently informative that their analysis yields the novel insight that the geometry of the connectivity patterns of more general players is significantly smoother and more contiguous than less general ones. Thus, the results reveal a correlation between generality and smoothness in connectivity patterns. They also hint at the intriguing possibility that as NE matures as a field, its algorithms can evolve ANNs of increasing relevance to those who study neural computation in general.

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