People usually spend time inside buildings like offices, shops, hospitals, airports, train station and other indoor spaces. Nowadays, the task of positioning and navigation in this kind of environment is becoming more relevant and useful. Commonly used GPS (Global Positioning System), which is typically used in obtaining location information, has no use in closed spaces. Thereby, we need new technology of real-time location system. In this research work we investigated usage of Bluetooth Low Energy RSSI to estimate location. We experimented with the following mathematical filtering functions to smooth rssi and improve accuracy: median, mode, single direction outlier removal, shifting and feedback filtering. For position calculation, we used trilateration algorithm. Our research based on assumptions and experiments of works of other researches. The goal was to achieve accuracy of 1-2 meters.

The data descriptor introduces an open, high-resolution dataset of real-world bus operations in Astana, Kazakhstan, captured from GPS trajectories between July and September 2024. The data covers three high-frequency routes and have been processed into a GTFS format, enabling direct use with existing transit modeling tools. Unlike typical static GTFS feeds, this dataset provides empirically observed dwell times, run times, and travel times, offering a detailed snapshot of operational variability in urban bus systems. The dataset supports applications in machine learning–based travel time prediction, timetable optimization, and transit reliability analysis, especially in settings where live feeds are unavailable. By releasing this dataset publicly, we aim to promote transparent, data-driven transport research in emerging urban contexts.

This study introduces a project aimed at the design and deployment of an intelligent passenger counting system for public transport. The objective is to enhance fare evasion control and reduce financial losses for transport operators through automated tracking of passenger entries and exits. The system employs the YOLO and DeepSORT algorithms, known for their high accuracy in identifying and monitoring passengers within complex environments. Experimental investigations reveal the critical role of camera type and positioning on system efficacy; notably, utilizing USB cameras over IP cameras enhances frame processing speed and overall system performance. However, testing has identified areas for improvement, particularly in managing group movements, minimizing frame loss, and increasing real-time accuracy. Future development efforts will focus on integrating depth sensors and crafting sophisticated data analysis algorithms to refine passenger counting precision during peak traffic periods. Anticipated outcomes of this project include optimized transport routes and schedules, improved management of passenger flows, heightened passenger satisfaction, and effective fare evasion prevention.

This paper presents the MLOps strategy, which adapts the automation principles of DevOps to the deployment and lifecycle management of artificial intelligence (AI) models. By leveraging high-performance automation, MLOps ensures seamless AI development and operations integration, enabling efficient and reliable model deployment. The study demonstrates this approach by implementing the Astana Opinion Mining macro-service customized for sentiment analysis. This macro-service evaluates public opinions based on a criteria taxonomy for assessing the urban environment's sustainable development. As a smart city application, the system facilitates the collection and analysis of citizen feedback to assess the performance of city services and inform urban planning decisions. Technologically, the MLOps strategy employs containers and microservices to construct robust data and process pipelines. Four core pipelines were developed in this research: data collection, feature engineering, experimentation, deployment, and maintenance. The data collection pipeline is achieved through automated crawling from diverse sources such as social media and other internet platforms. The feature engineering pipeline ensures data preprocessing by removing noise, identifying message languages, categorizing topics, and preparing data for further analysis. The experimentation pipeline incorporates services for data labeling, model training, and performance evaluation customized to sentiment analysis tasks. Finally, the deployment pipeline and maintenance pipeline deliver trained models to end-users, ensuring their continual improvement and adaptation. Using this MLOps framework, four models of sentiment analysis were tested in Russian: "Blanchefort," "Sismetanin," "MonoHime," and "Dostoevsky." The "Blanchefort" showed an accuracy of 71,43%. The resulting MLOps framework is fault-tolerant, scalable, and enables real-time urban environment assessments. By automating workflows, the architecture enhances operational efficiency, offering practical applications for smart city initiatives and sustainable urban development, contributing to better decision-making.

The growth of data in social networks facilitate demand for data analysis. The field of event detection is of increasing interest to researchers. Events from real life are actively discussed in the virtual space. Event detection results can be used in a variety of applications, from digital marketing to collecting data about natural disasters. Thereby, researchers face the emergence of new algorithms along with the improvement of existing solutions in the event detection field. This paper proposes improvements to the SEDTWik (Segment-based Event Detection from Tweets using Wikipedia) algorithm. The SEDTWik algorithm is designed to detect events without contextual guidance. The overall SEDTWik detection process excludes the perspective of a topic, or multi-topic, guided (or semi-supervised) event detection approach. As a result, some interesting narrowly focused events are not detected as they are weakly relevant in a broader context (e.g., Wikipedia) although acquiring relevance within a conditioned context. Therefore, there is a need for an adaptive perspective where data is to be analysed against a set of narrower topics of interest. This paper shows that SEDTWik gains expressive power after being extended with multi-topic semi-supervision. The evaluation of the current proposal uses the well-known corpora with labeled events, Events2012. In the Events2012 dataset used notation category for events, meaning that events are combined by a certain topic. SEDTWik with topic dictionaries was checked across all categories. In the main part of the article, it is also explained the process of topic dictionary construction from Events2012 labeled tweets. At this stage of the research, in all tasks unigrams were used. SEDTWik with dictionaries showed improved accuracy, and more events were found within a certain category.