Tomasz Szmuc

Dimensionality reduction is a key stage in data processing. Eliminating irrelevant attributes from the input dataset enables more efficient processing and supports the development of robust Machine Learning models. In particular, when dealing with large datasets, attribute reduction algorithms can significantly reduce computational complexity while minimizing the loss of valuable information contained in the data. This paper explores the potential application of a Feature selection method based on Rough Set Theory for multivariate data. The experiments were conducted on the publicly available Taiwan Credit dataset. The results show that limiting the input feature space to a minimal set of attributes generated by a reduct selection algorithm not only reduces training and inference time for Machine Learning or Deep Learning models but may also improve their classification performance. The models achieved higher Accuracy and better adaptability to the input data, as confirmed by increased or comparable Precision and Recall scores. Additionally, the findings indicate that applying Feature selection using a reduct set can lead to more efficient data processing. These results suggest that integrating Rough Set Theory with Deep Learning models still represents a promising approach to analyzing attribute-value data.

This study explores the application of reinforcement learning (RL) algorithms to optimize lift control strategies. By developing a versatile lift simulator enriched with real-world traffic data from an intelligent building system, we systematically compare RL-based strategies against well-established heuristic solutions. The research evaluates their performance using predefined metrics to improve our understanding of RL’s effectiveness in solving complex decision problems, such as the lift control algorithm. The results of the experiments show that all trained agents developed strategies that outperform the heuristic algorithms in every metric. Furthermore, the study conducts a comprehensive exploration of three Experience Replay mechanisms, aiming to enhance the performance of the chosen RL algorithm, Deep Q-Learning.

Scanning real 3D objects face many technical challenges. Stationary solutions allow for accurate scanning. However, they usually require special and expensive equipment. Competitive mobile solutions (handheld scanners, LiDARs on vehicles, etc.) do not allow for an accurate and fast mapping of the surface of the scanned object. The article proposes an end-to-end automated solution that enables the use of widely available mobile and stationary scanners. The related system generates a full 3D model of the object based on multiple depth sensors. For this purpose, the scanned object is marked with markers. Markers type and positions are automatically detected and mapped to a template mesh. The reference template is automatically selected for the scanned object, which is then transformed according to the data from the scanners with non-rigid transformation. The solution allows for the fast scanning of complex and varied size objects, constituting a set of training data for segmentation and classification systems of 3D scenes. The main advantage of the proposed solution is its efficiency, which enables real-time scanning and the ability to generate a mesh with a regular structure. It is critical for training data for machine learning algorithms. The source code is available at https://github.com/SATOffice/improved_scanner3D.

Inconsistency, lacking values of attributes or parameters, as well as discrepancies between records caused by insufficient precision cannot always be managed in the initial phases of knowledge discovery, i.e., data preparation and refinement. The theory of rough sets aims to overcome problems that are caused by uncertainty and lack of precision within the gathered data sets. This approach is a useful tool that operates on a formal model using relational algebra, elementary operations on finite sets and first-order logic. In this paper, we present an analysis of existing rough set tools, namely: Rough Set Exploration System, Rough Sets Data Explorer, Rough Set Data Analysis Framework, Waikato Environment for Knowledge Analysis and Rough Set Toolkit for Analysis of Data. Our comparison is performed only theoretically and covers the available algorithms, preparation of input data, licensing, as well as installation requirements.