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This paper presents an application of a Convolutional Neural Network as a solution for a task associated with ESENSEI Challenge: Marking Hair Follicles on Microscopic Images. As we show in this paper quality of classification results could be improved not only by changing architecture but also by ensemble networks. In this paper, we present two solutions for the task, the first one based on benchmark convolutional neural network, and the second one, an ensemble of VGG-16 networks. Presented models took first and third places in the final competition leaderboard.

Abstract-This paper presents an application of a Gaussian Mixture Model-based voting mechanism for an ensemble of Support Vector Machines (SVMs) to the problem of predicting dangerous seismic events in active coal mines. The author proposes a method of preparing an ensemble of SVMs with different parameters and using the "wisdom of the crowd" for a classification problem. Experiments performed during the research showed an improvement in the quality of the classification after the mixture of Gaussian distributions was applied as votes distribution. The author also proposes a method of data selection for long sequences of measurement arranged chronologically with highly unbalanced occurrence of the positive class in the twoclass classification problem. Finally, using the proposed model to solve the problem defined by the organizers of AAIA'16 DM showed an increase in the stability of the ensemble classifier and an improvement in the quality of the classification problem solution.

This article presents an application of an XGBoost and deep neural network ensemble as a solution for a task assigned at the FedCSIS 2022 Challenge: Predicting the Costs of Forwarding Contracts. We demonstrate that prediction quality can be improved by combining the two approaches. We present a neural network architecture based on three independent flows. We then discuss the influence of long short-term memory units on the risk of overfitting. Finally, we show that the static XGBoost model can complement a neural network that processes dynamic data.