Technical University of Ostrava

About: Technical University of Ostrava is a education organization based out in Ostrava, Czechia. It is known for research contribution in the topics: Artificial neural network & Evolutionary algorithm. The organization has 4186 authors who have published 8936 publications receiving 65393 citations. The organization is also known as: Vysoká škola báňská – Technická univerzita Ostrava & VŠB – Technical University of Ostrava.

Conditions for energy generation as an alternative approach to compost utilization

Abstract: Very strict limits constrain the current possibilities for compost utilization in agriculture and for land reclamation, thus creating a need for other compost utilization practices. A favourable al...

The influence of air flow on maximum explosion characteristics of dust–air mixtures

Abstract: A standard spherical apparatus for measuring explosion characteristics was modified to give increased and controlled turbulence within a dust–air mixture. This was intended to mimic the local effects which may occur during industrial dust explosions, particularly secondary ones which may develop in ducts or mine galleries where the initial explosion causes an increased air velocity and suspension of further quantities of dust. The results show that there may be a doubling of the maximum explosion pressure and of the rate of pressure rise during the explosion under more turbulent conditions. This is significant for modelling of dust explosions and suggests that explosion relief may be inadequate if this factor is not taken into consideration. The modified apparatus therefore gives a laboratory method for assessing the effect of turbulence in dust explosions.

Ensemble of heterogeneous flexible neural trees using multiobjective genetic programming

Abstract: Graphical abstractDisplay Omitted HighlightsA heterogeneous flexible neural tree (FNT) for function approximation was proposed.FNT was studied under Pareto-based multiobjective genetic programming framework.A diversity-index was introduced to maintain diversity in genetic population.FNT was found competitive with other algorithm when cross validated over datasets.Evolutionary weighted ensemble of HFNTs further improved FNT performance. Machine learning algorithms are inherently multiobjective in nature, where approximation error minimization and model's complexity simplification are two conflicting objectives. We proposed a multiobjective genetic programming (MOGP) for creating a heterogeneous flexible neural tree (HFNT), tree-like flexible feedforward neural network model. The functional heterogeneity in neural tree nodes was introduced to capture a better insight of data during learning because each input in a dataset possess different features. MOGP guided an initial HFNT population towards Pareto-optimal solutions, where the final population was used for making an ensemble system. A diversity index measure along with approximation error and complexity was introduced to maintain diversity among the candidates in the population. Hence, the ensemble was created by using accurate, structurally simple, and diverse candidates from MOGP final population. Differential evolution algorithm was applied to fine-tune the underlying parameters of the selected candidates. A comprehensive test over classification, regression, and time-series datasets proved the efficiency of the proposed algorithm over other available prediction methods. Moreover, the heterogeneous creation of HFNT proved to be efficient in making ensemble system from the final population.

The Influence of Attitudes toward Immigrants on International Migration

Abstract: We investigate whether anti-immigrant attitudes affect migrant inflows in Organisation for Economic Co-operation and Development (OECD) countries. Using longitudinal exhaustive data, we find that n...