Ing. Miroslav Čepek, Ph.D.

Proceedings paper

Department of Applied Mathematics

This study introduces a novel machine learning-based methodology for automated detection and tracking of sperm cells within microscopic video recordings, aiming to elucidate the dynamics and motion patterns of individual sperm cells as well as sperm cell bundles. At first, the method identifies sperm cells across successive frames within a video sequence, facilitating the reconstruction of each cell's trajectory over time. Subsequently, we introduce a classification algorithm that distinguishes between solitary sperm cells, clusters of adjacent cells, and cohesive sperm cell bundles, addressing a gap in existing methodologies. Finally, we employ three conventional metrics for velocity assessment: Straight Line Velocity (VSL) and Average Path Velocity (VAP) and Curvilinear velocity (VCL), to quantify the movement speed of both individual sperm cells and bundles. The approach represents a significant advancement in the automated analysis of sperm motility and aggregation phenomena, providing a robust tool for researchers to study sperm behavior with enhanced accuracy and efficiency. The integration of machine learning techniques in sperm cell detection and tracking offers promising insights into reproductive biology and fertility studies.

Proceedings paper

10.1109/CSIT56902.2022.10000471

We introduce dynamic auto-sizing, a novel approach to training artificial neural networks which allows the models to automatically adapt their size to the problem domain. The size of the models can be further controlled during the learning process by modifying the applied strength of regularization. The ability of dynamic auto-sizing models to expand or shrink their hidden layers is achieved by periodically growing and pruning entire units such as neurons or filters. For this purpose, we introduce weighted L1 regularization, a novel regularization method for inducing structured sparsity. Besides analyzing the behavior of dynamic auto-sizing, we evaluate predictive performance of models trained using the method and show that such models can provide a predictive advantage over traditional approaches.

Article

10.1016/j.neunet.2010.02.003

Department of Theoretical Computer Science

Optimization of neural network topology, weights and neuron transfer functions for given data set and problem is not an easy task. In this article, we focus primarily on building optimal feed-forward neural network classifier for i.i.d. data sets. We apply metalearning principles to the neural network structure and function optimization. We show that diversity promotion, ensembling, self-organization and induction are beneficial for the problem. We combine several different neuron types trained by various optimization algorithms to build a supervised feedforward neural network called Group of Adaptive Models Evolution (GAME). The approach was tested on wide number of benchmark data sets. The experiments show that the combination of different optimization algorithms in the network is the best choice when the performance is averaged over several real-world problems.

Proceedings paper

Department of Theoretical Computer Science

The data preprocessing is very important part of the knowledge discovery process. Data mining systems con- tains tens of preprocessing methods (for example methods for missing data imputation, data reduction, discretization, data enrichment, etc...) and usually it is not clear which methods to use. The selection of preprocessing methods appropriate for particular dataset needs strong experience and a lot of experimenting. In this paper we will test influence of modelling method which is the corner stone of Inductive Preprocessing Algorithm. Modelling method is used to evaluate evolved sequence of the preprocessing methods. In this paper we compare four modelling methods in respect to final achieved accuracy. The tested modelling methods are Polynomial model, Decision Tree, SVM and Logistic Function Classifier. To test our automatic preprocessing utilize several real-world datasets available from UCI Machine learning repository.

Proceedings paper

Department of Theoretical Computer Science

The data preprocessing is very important part of the knowledge discovery process. Data mining systems contains tens of preprocessing methods (for example methods for missing data imputation, data reduction, discretization, data enrichment, etc...) and usually it is not clear which methods to use. The selection of preprocessing methods appropriate for particular dataset needs strong experience and a lot of experimenting. In this paper we will test our extension of inductive approach to data preprocessing. We developed inductive preprocessing method which utilizes genetic algorithm to compose from scratch a sequence of preprocessing methods which fits to the data and allows successful model to be created. To test our automatic preprocessing utilize several real-world datasets available from UCI Machine learning repository.