Obstacle detection is crucial for the navigation of autonomous mobile robots: it is necessary to ensure their presence as accurately as possible and find their position relative to the robot. Autonomous mobile robots for indoor navigation purposes use several special sensors for various tasks. One such study is localizing the robot in space. In most cases, the LiDAR sensor is employed to solve this problem. In addition, the data from this sensor are critical, as the sensor is directly related to the distance of objects and obstacles surrounding the robot, so LiDAR data can be used for detection. This article is devoted to developing an obstacle detection algorithm based on 2D LiDAR sensor data. We propose a parallelization method to speed up this algorithm while processing big data. The result is an algorithm that finds obstacles and objects with high accuracy and speed: it receives a set of points from the sensor and data about the robot’s movements. It outputs a set of line segments, where each group of such line segments describes an object. The two proposed metrics assessed accuracy, and both averages are high: 86% and 91% for the first and second metrics, respectively. The proposed method is flexible enough to optimize it for a specific configuration of the LiDAR sensor. Four hyperparameters are experimentally found for a given sensor configuration to maximize the correspondence between real and found objects. The work of the proposed algorithm has been carefully tested on simulated and actual data. The authors also investigated the relationship between the selected hyperparameters’ values and the algorithm’s efficiency. Potential applications, limitations, and opportunities for future research are discussed.

The relevance of the study is due to the growing number of diseases of the cerebrovascular system, in particular stroke, which is one of the leading causes of disability and mortality in the world. To improve stroke risk prediction models in terms of efficiency and interpretability, we propose to integrate modern machine learning algorithms and data dimensionality reduction methods, in particular XGBoost and optimized principal component analysis (PCA), which provide data structuring and increase processing speed, especially for large datasets. For the first time, explainable artificial intelligence (XAI) is integrated into the PCA process, which increases transparency and interpretation, providing a better understanding of risk factors for medical professionals. The proposed approach was tested on two datasets, with accuracy of 95% and 98%. Cross-validation yielded an average value of 0.99, and high values of Matthew's correlation coefficient (MCC) metrics of 0.96 and Cohen's Kappa (CK) of 0.96 confirmed the generalizability and reliability of the model. The processing speed is increased threefold due to OpenMP parallelization, which makes it possible to apply it in practice. Thus, the proposed method is innovative and can potentially improve forecasting systems in the healthcare industry.

Forecasting financial markets is a complex task that requires addressing various challenges, such as market complexity, data heterogeneity, the need for rapid response and constant changes in conditions, to gain a competitive advantage. To effectively address these challenges, it is necessary to constantly improve existing and develop new methods of intelligent forecasting, which will improve the accuracy of forecasts, reduce risks and increase the productivity of financial decision-making processes. In this article, we study and analyse forecasting methods in financial markets, such as support vector regression (SVR), autoregressive integrated moving average (ARIMA), long short-term memory recurrent neural network (LSTM) and extreme gradient boosting algorithm (XG-Boost). Based on this analysis, we propose an ensemble forecasting procedure that integrates LSTM and ARIMA models. Due to the careful combination of these models, our approach yields better results than individual methods. For example, our model demonstrates a significant 15% improvement in root mean square error (RMSE) and a slight improvement in coefficient of determination compared with LSTM. Furthermore, simulation results obtained on three real-world datasets and evaluated using the RMSE criterion confirm the superiority of our proposed method over alternative methods such as LSTMs, transformer models and optimized deep recurrent neural networks with long short-term memory for financial market forecasting. Furthermore, our approach creates the prerequisites for parallelizing both models, thus providing an opportunity to accelerate forecasting results in future research.

Introduction: Streaming services are highly popular today. Millions of people watch live streams or videos and listen to music.Methods: One of the most popular streaming platforms is Twitch, and data from this type of service can be a good example for applying the parallel DBSCAN algorithm proposed in this paper. Unlike the classical approach to neighbor search, the proposed one avoids redundancy, i.e., the repetition of the same calculations. At the same time, this algorithm is based on the classical DBSCAN method with a full search for all neighbors, parallelization by subtasks, and OpenMP parallel computing technology.Results: In this work, without reducing the accuracy, we managed to speed up the solution based on the DBSCAN algorithm when analyzing medium-sized data. As a result, the acceleration rate tends to the number of cores of a multicore computer system and the efficiency to one.Discussion: Before conducting numerical experiments, theoretical estimates of speed-up and efficiency were obtained, and they aligned with the results obtained, confirming their validity. The quality of the performed clustering was verified using the silhouette value. All experiments were conducted using different percentages of medium-sized datasets. The prospects of applying the proposed algorithm can be obtained in various fields such as advertising, marketing, cybersecurity, and sociology. It is worth mentioning that datasets of this kind are often used for detecting fraud on the Internet, making an algorithm capable of considering all neighbors a useful tool for such research.

Introduction: Accurate segmentation of anatomical structures in chest X-ray images remains challenging, especially for regions with low contrast and overlapping structures. This limitation significantly affects the diagnosis of cardiothoracic diseases. Existing deep learning methods often struggle with preserving structural boundaries, leading to segmentation artifacts.Methods: To address these challenges, I propose a novel segmentation approach that integrates contour detection techniques with the U-net deep learning architecture. Specifically, the method employs Sobel and Scharr edge detection filters to enhance structural boundaries in chest X-ray images before segmentation. The pipeline involves pre-processing using contour detection, followed by segmentation with a U-net model trained to identify lungs, heart, and clavicles.Results: Experimental evaluation demonstrated that using edge-enhancing filters, particularly the Sobel operator, leads to a marked improvement in segmentation accuracy. For lung segmentation, the model achieved an accuracy of 99.26%, a Dice coefficient of 98.88%, and a Jaccard index of 97.54%. Heart segmentation results included 99.47% accuracy and 94.14% Jaccard index, while clavicle segmentation reached 99.79% accuracy and 89.57% Jaccard index. These results consistently outperform the baseline U-net model without edge enhancement.Discussion: The integration of contour detection methods with the U-net model significantly improves the segmentation quality of complex anatomical regions in chest X-rays. Among the tested filters, the Sobel operator proved to be the most effective in enhancing boundary information and reducing segmentation artifacts. This approach offers a promising direction for more accurate and robust computer-aided diagnosis systems in radiology.