This study analyzes the spatial aggregation and activity of the urban population in Almaty using anonymized population density data provided by a telecommunications operator and geographic data from OpenStreetMap. The study focuses on identifying stable zones of high population activity, which facilitates the optimization of transport routes, urban infrastructure planning, and the efficient allocation of city resources. The novelty of this work lies in the integration of aggregated spatiotemporal data with advanced clustering methods, including DBSCAN, KMeans++, and agglomerative clustering. The research methodology involves dividing the city into 500 × 500 m quadrants, calculating normalized population density metrics, and identifying high-activity clusters. Based on a comparative analysis of clustering algorithms, DBSCAN exhibited the highest clustering quality according to the silhouette coefficient and the Davies–Bouldin index, allowing for the identification of key zones of urban activity. The identified clusters were utilized to assess transport load, analyze disparities in the distribution of public transport stops, and develop recommendations to improve public transport accessibility in the most congested areas. The study’s findings are applicable not only to optimizing the transport network but also to addressing a broader range of urban planning challenges, including the strategic placement of infrastructure facilities and the management of population flows. The proposed methodology is scalable and can be adapted to other cities requiring effective tools for analyzing the spatiotemporal activity of urban populations.

This research introduces a novel convolutional neural network-bidirectional long short-term memory (CNN-BiLSTM) hybrid network for the automatic classification of heart arrhythmias using 12-lead electrocardiograms (ECGs). By merging the spatial feature extraction capabilities of CNNs with the temporal precision of BiLSTM networks, our approach sets a new standard in cardiac diagnostics. The proposed model was tested against the comprehensive CPSC2018 dataset, demonstrating superior performance with an accuracy of 90.67%, precision of 93.27%, recall of 96.35%, and an F-score of 94.78%, surpassing existing state-of-the-art methods. These results underscore the effectiveness of integrating spatial and temporal data analysis, offering a robust and reliable tool for medical practitioners. This study represents a significant advancement in automated ECG analysis, paving the way for improved diagnosis and treatment of heart diseases, and contributing to enhanced patient outcomes in cardiac care.

This study presents a comparative analysis of the object detection models YOLOv8n and YOLOv12n in their Nano configurations for real-time construction site monitoring tasks. Key model characteristics were evaluated, including detection accuracy, recall, inference speed, and computational resource consumption across various server configurations. Particular attention was given to the use of the Slicing Aided Hyper Inference (SAHI) method to improve the detection quality of small objects, as well as the impact of scene complexity on system performance. YOLOv12n achieved significantly higher accuracy and recall than YOLOv8n. In addition, the integration of the SAHI method further enhanced small object detection performance. Parallel stream processing showed that servers equipped with Tesla L4 GPUs offered the best trade-off between inference speed and stability under multi-threaded workloads. The inclusion of data augmentation techniques simulating various weather conditions enhanced model robustness, especially for YOLOv8n, in noisy and complex scenes. The findings underscore the clear advantage of YOLOv12n for applications requiring high recall in challenging environments, while YOLOv8n demonstrated strong efficiency on resource-constrained systems. The proposed approaches can be utilized in the development of intelligent monitoring systems for construction sites, industrial facilities, and logistics hubs, including automated access control and real-time employee activity tracking.

Spatiotemporal analytics of population movement and density data plays a crucial role in building a «smart city», providing a basis for optimizing urban planning, improving transport systems, increasing public safety, environmental monitoring, developing digital services and urban analytics. This article presents the results of a study on spatiotemporal patterns of distribution and concentration of the population of Almaty using the method of dynamic heat maps. To build a complete picture of the movement, density and activity of the population, open geographic data from OpenStreetMap (OSM) and aggregated data from a mobile operator were used. Analysis of the load on urban quadrants of 500×500 meters based on OSM made it possible to assess the key patterns of change in population density depending on the time of day. Visualization of spatiotemporal data is implemented using the Python Folium library, which ensured the creation of clear interactive maps. The scientific novelty of the study lies in the study of urban processes in Almaty based on integrated data from different sources reflecting the spatiotemporal features of the dynamics of the urban population. The results obtained demonstrate clear patterns of population concentration that can be used to more accurately forecast and plan the allocation of resources and urban infrastructure.