The aim of this research is to automate an analysis of the EGFR gene as a whole, and especially an analysis of those exons with clinically identified microdeletion mutations which are recorded with non-mutated nucleotides in a long chains of a, c, t, g nucleotides, and “-“ (microdeletion) in the NCBI database or other sites. In addition, the developed system can analyze data resulting from EGFR gene DNA sequencing or DNA extraction for a new patient and identify regions potential microdeletion mutations that clinicians need to develop new

The dams are very important objects for production of electric energy, irrigation, flood management and tourism. However, besides all benefits the dams provide, they also represent great danger for areas downstream because there is always risk of dam failure. To prevent dam failure it is important to perform regular dam monitoring and for that purpose geodetic and physical methods are used. Geodetic methods use special network of points for object monitoring where reference points are used for monitoring of object points which are strategically distributed on the object. By quality prediction of object behaviour it would be possible to prevent further damage on the object and additionally to save human lives in cases of great danger. In this paper artifical neural networks (ANNs) are used for dam movement prediction. ANNs are very popular tool for prediction since they are known for their quick learning ability and good generalization ability which gives them advantage compared to traditional statistical methods.

This paper is related to a research of modelling fuzzy-neural systems using the coevolutionary algorithm, and has the focus on advantages of using the coevolutionary algorithm for system structure optimization. In the context of this work, the term fuzzy-neural system defines the system that can be used as the fuzzy system with all its functionalities or as the neural network with all its functionalities. The hybridization of fuzzy logic, neural networks and coevolutionary algorithm and its architecture are presented in general, and the role of the coevolutionary algorithm in structure optimization is described in details. Results of testing with Iris Database, from UCI Machine Learning Repository are also presented. Tests performed during the research supports the conclusion that usage of the coevolutionary algorithm for the fuzzy-neural system’s structure optimization is very efficient.

Nowadays, we are witnessing the rapid development of medicine and various methods that are used for early detection of diseases. In order to make quality decisions in diagnosis and prevention of disease, various decision support systems based on machine learning methods have been introduced in the medical domain. Such systems play an increasingly important role in medical practice. This paper presents a new web framework concept for disease prediction. The proposed framework is object-oriented and enables online prediction of various diseases. The framework enables online creation of different autonomous prediction models depending on the characteristics of diseases. Prediction process in the framework is based on a hybrid Case Based Reasoning classifier. The framework was evaluated on disease datasets from public repositories. Experimental evaluation shows that the proposed framework achieved high diagnosis accuracy.