This paper uses Artificial Neural Networks to develop a model for detecting management fraud. Although similar to the more widely investigated area of bankruptcy prediction, research has been minimal. To increase the body of knowledge on this subject, we offer an in-depth examination of important publicly available predictors of fraudulent financial statements. We test the value of these sugges...

Computational intelligence provides a variety of means that can perform complex image processing in a rather effective way. Among them, self-learning systems, especially selflearning artificial neural networks (self-organizing maps, ART neural networks, ‘BrainState-in-a-Box’ neuromodels, etc.) (Haykin, 1999) and fuzzy clustering systems (fuzzy cmeans, algorithms of Gustafson-Kessel, Yager-Filev...

the main objective of this paper is to introduce a new intelligent optimization technique that uses a predictioncorrectionstrategy supported by a recurrent neural network for finding a near optimal solution of a givenobjective function. recently there have been attempts for using artificial neural networks (anns) in optimizationproblems and some types of anns such as hopfield network and boltzm...

Abstract. A scalable architecture to facilitate emergent (self-organized) task decomposition using neural networks and evolutionary algorithms is presented. Various control system architectures are compared for a collective robotics (3 × 3 tiling pattern formation) task where emergent behaviours and effective task -decomposition techniques are necessary to solve the task. We show that bigger, m...

Abstract. A scalable architecture to facilitate emergent (self-organized) task decomposition using neural networks and evolutionary algorithms is presented. Various control system architectures are compared for a collective robotics (3 × 3 tiling pattern formation) task where emergent behaviours and effective task -decomposition techniques are necessary to solve the task. We show that bigger, m...

The Artificial Neural Networks method is applied on visual working efficiency of cockpit. A Self-Organizing Map (SOM) network is demonstrated selecting material with near properties. Then a Back-Propagation (BP) network automatically learns the relationship between input and output. After a set of training, the BP network is able to estimate material characteristics using knowledge and criteria...

Self-Organising Maps (SOMs) are effective tools in classification problems, and in recent years the even more powerful Dynamic Growing Neural Networks, a variant of SOMs, have been developed. Automatic Classification (also called clustering) is an important and difficult problem in many Astrophysical experiments, for instance, Gamma Ray Burst classification, or gamma-hadron separation. After a ...

modelling and forecasting stock market is a challenging task for economists and engineers since it has a dynamic structure and nonlinear characteristic. this nonlinearity affects the efficiency of the price characteristics. using an artificial neural network (ann) is a proper way to model this nonlinearity and it has been used successfully in one-step-ahead and multi-step-ahead prediction of di...