Domain ontologies facilitate the organization, sharing, and reuse of subject areas. Building a software ontology is labor-intensive and time-consuming. In the process of obtaining a software ontology, it is required to analyze a huge number of scientific publications relevant to the software being modeled. The process of ontology replenishing with information from a huge number of scientific publications can be facilitated and accelerated through the use of lexical-syntactic patterns of ontological design. In this paper, we consider the possibility of automated construction of scientific subject area ontologies based on a heterogeneous patterns system of ontological design. This system includes ontological design patterns and is intended for ontology developers. System also includes automatically built lexical and syntactic patterns, which help to automatic replenishment of the ontology with information extracted from natural language texts.

The development of a wearable sensor device integrated into the internet of things (IoT) infrastructure is presented, with functionality aimed at continuous measurement of the user's physiological parameters and their intelligent processing for real-time stress level assessment. The system enables continuous monitoring of physiological parameters, allowing early detection of stress signals and supporting adaptive behavioral responses. The hardware platform is designed to consolidate various biomedical sensors, enabling continuous acquisition and intelligent processing of physiological data in real time. During testing, heart rate (HR) ranged from 68 to 89 beats per minute (bpm), respiratory rate varied from 11 to 15 breaths per minute, and skin conductivity ranged from 63 to 77 µS. Acquired physiological data were uploaded to a cloud-based infrastructure to enable advanced processing and analysis. The system achieved an overall stress detection accuracy of 87%, and signal stability remained high even under changing conditions. The proposed wearable solution demonstrates strong potential for use in healthcare, education, and occupational environments. It also offers scalability through the integration of intelligent algorithms and additional sensor modules.

Medical waste management is a growing concern in Kazakhstan. Despite the presence of a regulatory framework, the current medical waste disposal system suffers from fragmentation, lack of transparency, and inefficient communication between stakeholders. These limitations result in illegal dumping, environmental pollution, and increased health risks. This paper presents the development and validation of an integrated Internet of Things (IoT)-based system designed to optimize and automate the monitoring, collection, and disposal of medical waste. The proposed architecture includes Global Positioning System (GPS) tracking, real-time sensor monitoring, cloud data analytics, and predictive routing algorithms, enabling efficient logistics and regulatory compliance. Utilizing a microcontroller and sensors, the system continuously transmits data to a centralized server for monitoring. Experimental deployments across urban and suburban routes in the Zhambyl region demonstrate that the system achieves a Circular Error Probable (CEP₅₀) of 11 m and a 95% positioning accuracy within 23 m, which aligns acceptably with the requirements for city-level route optimization. Statistical analysis confirms that the observed positioning accuracy is consistent with an urban propagation model and adequate for municipal dispatching, though it remains below automotive-grade precision. The system is further supported by a robust power supply solution, allowing up to 49 h of autonomous operation.

Internet-of-Things (IoT) sensing can improve traceability, safety, and efficiency of medical waste handling, yet many deployments remain fragmented, lack an end-to-end system architecture, and do not provide the structured data pipelines needed for artificial intelligence (AI) analytics. This paper presents a layered IoT-based system design for medical waste management that integrates: (i) Espressif Systems 32 (ESP32)-based edge devices for fill-level and Global Positioning System (GPS) telemetry; (ii) secure network communication; (iii) a cloud backend for data ingestion, storage, and analytics; and (iv) operator dashboards with event-driven alerting. The architecture extends our prior GPS-enabled tracking and route optimization by adding sensor-driven state monitoring, threshold-based decision support, and a time-series data pipeline designed for future AI-driven predictive analytics. In a 30-day pilot with five containers, the system collected one reading every 15 min (14,400 total readings). The backend demonstrated efficient processing with an average Application Programming Interface (API) response time of 45 ms, sub-50 ms database write latency, and high uptime; alerts were delivered promptly upon threshold violations. Compared with a fixed-schedule baseline, the system enabled condition-based collection scheduling with zero data loss. The proposed design emphasizes modularity, fault tolerance, and integration readiness for hospital information systems, providing a practical blueprint for scalable smart-healthcare waste logistics and a foundation for machine learning-based predictive waste management.

The article deals with the problem of medical waste management and the need to create a scientific and methodological base for the development of a data collection mechanism related to their formation, collection, transportation and disposal. The introduction of digital technologies and the integration of data on all stages of medical waste management will create an effective management system. The main focus is on system, process and risk-based approaches, as well as data collection methods at various stages. The article focuses on the problems and prospects of implementing digital solutions and automating medical waste management processes. The article examines the database structure, key parameters and relationships, as well as data modeling with an emphasis on logical and physical structure.

The article considers the relevance and applied aspects of the use of chatbots. The structural and functional system of the chatbot is analyzed. A comparative description of the platforms for creating chatbots has been made. The functional blocks are based on the example of the Telegram social network, a prototype of the system. As part of the chatbot development, the necessary and sufficient condition for working with text is described - the use of various text analysis methods as an input element for interacting with the bot and its training. The article discusses a number of solutions used for text analysis and creation of text data analysis models: lemmatization methods, text vectorization, various machine learning models. The article focuses on the consideration of the methodology of text processing in different formats and using different technologies, which ensures the scalability and versatility of the proposed technology and the overall effectiveness of the future work of the chatbot.The article will be of interest to programmers, text analysts and anyone interested in working with text and creating systems for working with text information. A chatbot has been created in the Telegram messenger in the Python programming language that automates the work of a customer service specialist.