In order to develop effective evolutionary artificial neural networks (EANNs) we have to address the questions on how to evolve EANNs more efficiently and how to achieve the best performance from the ANNs evolved. Most of the previous works, however, do not utilize all the information obtained with several ANNs but choose the one best network in the last generation. Some recent works indicate t...

in recent years, various time series models have been proposed for financial markets forecasting. in each case, the accuracy of time series forecasting models are fundamental to make decision and hence the research for improving the effectiveness of forecasting models have been curried on. many researchers have compared different time series models together in order to determine more efficient ...

time series forecasting is an active research area that has drawn considerable attention for applications in a variety of areas. forecasting accuracy is one of the most important features of forecasting models. nowadays, despite the numerous time series forecasting models which have been proposed in several past decades, it is widely recognized that financial markets are extremely difficult to ...

This paper presents an overview on applications of artificial neural network in electric power industry (EPI) which is currently undergoing an extraordinary development. One of the most thrilling and potentially cost-effective recent developments in this field is increasing usage of artificial intelligence techniques viz. artificial neural networks (ANNs), genetic algorithm, fuzzy logic, and ex...

Rapid evaluation of demand parameters of different types of  buildings is crucial for social restoration after damaging earthquakes. Previous studies proposed numerous methodologies to measure the performance of buildings for assessing the potential risk under the seismic hazard. However, time-consuming Nonlinear Response History Analysis (NRHA) barricaded implementing a prompt loss estimation ...

The study of drought and its forecasting plays an important role in planning managing water resource systems, especially extreme climatic periods. This aims to analyze forecast characteristics, through the use Reconnaissance Drought Index (RDI) order temporal spatiotemporal nine climate stations Kurdistan Region Iraq for period (1973-2020) detect beginning end period, as well future droughts us...

Feed-forward Artificial Neural Networks (ANNs) have become popular among researchers and practitioners for modelling complex real–world problems. One of the latest research areas in this field is evolving ANNs. In this paper, we investigate the simultaneous evolution of architectures and connection weights in ANNs. In simultaneous evolution, we use the concept of multiobjective optimization and...

improving time series forecastingaccuracy is an important yet often difficult task.both theoretical and empirical findings haveindicated that integration of several models is an effectiveway to improve predictive performance, especiallywhen the models in combination are quite different. in this paper,a model of the hybrid artificial neural networks andfuzzy model is proposed for time series for...

Efficient exploitation of hyperspectral imagery is of great importance in remote sensing. Artificial Intelligence approaches have been receiving favorable reviews for classification of hypersepctral data because the complexity of such data challenges the limitations of many conventional methods. Artificial Neural Networks (ANNs) were shown to outperform traditional classifiers in many situation...

Cryptography is the ability of changing information into obvious unintelligibility in a way allowing a secret method of un-mangling. The vital idea of cryptography is the capability to send information between participants in a way that prevents others from reading it. Much cryptography methods are available which are based on number theory but it has the disadvantage of requirement a large com...