Planning tourism development means preparing the destination for coping with uncertainties as is sensitive to many changes. This study tested two types of artificial neural networks in modeling international tourist arrivals recorded Ohrid (North Macedonia) during 2010–2019. It argues that MultiLayer Perceptron (MLP) network more accurate than Nonlinear AutoRegressive eXogenous (NARX) model whe...

Real-world graphs, such as social networks, financial transactions, and recommendation systems, often demonstrate dynamic behavior. This phenomenon, known graph stream, involves the changes of nodes emergence disappearance edges. To effectively capture both structural temporal aspects these neural networks have been developed. However, existing methods are usually tailored to process either con...

Dynamic neural networks could adapt their structures or parameters based on different inputs. By reducing the computation redundancy for certain samples, it can greatly improve computational efficiency without compromising accuracy. In this paper, we investigate robustness of dynamic against energy-oriented attacks. We present a novel algorithm, named GradAuto, to attack both depth and width mo...

mobile robot navigation is one of the basic problems in robotics. in this paper, a new approachis proposed for autonomous mobile robot navigation in an unknown environment. the proposedapproach is based on learning virtual parallel paths that propel the mobile robot toward the trackusing a multi-layer, feed-forward neural network. for training, a human operator navigates themobile robot in some...

In this study, a low-cost, rapid and qualitative evaluation procedure is presented using dynamic pattern recognition analysis to assess liquefaction potential which is useful in the planning, zoning, general hazard assessment, and delineation of areas, Dynamic pattern recognition using neural networks is generally considered to be an effective tool for assessing of hazard potential on the b...

in the present study iran’s rice imports trend is forecasted, using artificial neural networks and econometric methods, during 2009 to 2013, and their results are compared. the results showed that feet forward neural network leading with less forecast error and had better performance in comparison to econometric techniques and also, other methods of neural networks, such as recurrent networks a...

A potentially powerful application of evolutionary computation (EC) is to evolve neural networks for automated control tasks. However, in such tasks environments can be unpredictable and fixed control policies may fail when conditions suddenly change. Thus, there is a need to evolve neural networks that can adapt, i.e. change their control policy dynamically as conditions change. In this paper,...