Lazaros S. Iliadis

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This is a preliminary attempt towards a wider use of Artificial Neural Networks in the management of mountainous water supplies. It proposes a model to be used effectively in the estimation of the average annual water supply, in each mountainous watershed of Cyprus. This is really a crucial task, especially during the long dry summer months of the island.(More)
The objective of the present study is to design and develop an Artificial Neural Network (ANN) model for estimations of the ambient ozone concentrations based on meteorological and pollutant parameters. The study focuses on an urban site in the metropolitan area of Athens. The research proves that the optimal ANN is a Modular one that uses the Back(More)
The effective protection from natural disasters requires the development of a rational and sensible protection and prevention policy. This project deals with the development and testing of a decision support system that acts on two levels. On the first level, it estimates the annual forest fire risk for each area of Greece using a fuzzy Trapezoidal(More)
This manuscript is the result of research conducted towards the production of meta-data to be used as inputs to neural networks. It is essentially a preliminary attempt towards the use of an evolutionary approach to interpret the significance which time series data pose on the behavior of mountainous water supplies, proposing a model which could be(More)
A novel approach to color image segmentation is proposed and formulated in this paper. Conventional color segmentation methods apply SOFMs – among other techniques – as a first stage clustering in hierarchical or hybrid schemes in order to achieve color reduction and enhance robustness against noise. 2-D SOFMs defined upon 3-D color space are usually(More)
This study presents an original mathematical model and a prototype computer decision support system for the management of natural disasters risk. The system not only estimates the degree of risk for each area under study but it evaluates itself by calculating the entropy of its output as well. The degree of risk can be very useful for the design of(More)
This study proposes an Artificial Neural Network (ANN) and Genetic Algorithm model for diagnostic risk factors selection in medicine. A medical disease prediction may be viewed as a pattern classification problem based on a set of clinical and laboratory parameters. Probabilistic Neural Networks (PNNs) were used to face a medical disease prediction. Genetic(More)