Жарияланымдар / Публикации

Постоянный URI для этого сообществаhttps://cris.qyzpu.edu.kz/handle/123456789/2

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2021 - 20263

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Автор: search.filters.author.Saya SapakovaЯзык: search.filters.language.Английский

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    Internet of Things in Healthcare: A Review
    (Springer Science+Business Media, 2021-01-01)
    Batyrkhan Sultanovich Omarov
    ;
    Altynzer Baiganova
    ;
    Saya Sapakova
    ;
    Zhanna Yessengaliyeva
    ;
    S.A. Issayev
    Цитирования (OpenAlex) 10Цитирования (OpenCitations) 2
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    ANALYZING RUSSIAN BORROWINGS IN KAZAKH SOCIAL MEDIA USING MACHINE LEARNING
    (2024-12-24)
    Saya Sapakova
    ;
    Б. Ғ. Қамбарова
    This study explores the influence of Russian words on the development of the Kazakh language in social networks. The rapid advancement of information technology significantly impacts the language used in online communications. While the chaotic nature of online interactions can complicate language use and create confusion, it also accelerates the spread of information in Kazakh. This research examines how foreign words affect modern Kazakh internet discourse, including direct borrowings that enter the language without modification, mixedphrases that retain the lexical and semantic properties of foreign words, the emergence of new abbreviations, and the influence of barbarisms. The study utilizes machine learning methods to analyse social media content from Instagram and Facebook. This approach enabled the processing of over 100,000 posts, revealing key linguistic shifts associated with the integration of Russian borrowings into Kazakh. The use of machine learning algorithms, such as the Naive Bayes classifier, automated the data analysis process and uncovered hidden patterns, providing a deeper understanding of how these borrowings affect the Kazakh language in the digital environment.
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    A Web-Based Digital Twin for Traffic and Air Quality Monitoring: A Prototype Study in Almaty, Kazakhstan
    (Multidisciplinary Digital Publishing Institute, 2026-08-18)
    Saya Sapakova
    ;
    Аскар Сапаков
    ;
    Omirlan Auelbekov
    ;
    Lyailya Tukenova
    ;
    Sakhybay Tynymbayev
    Urban air pollution driven by road traffic poses a significant public health challenge in cities with high vehicle density and frequent congestion, particularly in topographically constrained environments such as Almaty, Kazakhstan. This study presents a web-based digital twin prototype for the integrated monitoring and analysis of traffic flow and air quality in Almaty, Kazakhstan. The system autonomously collects data from the TomTom Traffic, OpenWeather Air Pollution, and WAQI APIs and official population statistics for five fixed monitoring stations, computing traffic density, vehicles per hour, road congestion, estimated CO2 emissions, an air pollution index, and a population exposure index, and providing real-time dashboard visualization alongside longitudinal data accumulation. Over a 50-day deployment (26 May–16 July 2026), 4961 real co-located observations across 18 active days were analyzed; records generated by the prototype’s fallback mechanism during API outages were excluded from the scientific analysis. During this summer period, PM2.5 was low (mean ≈ 6 µg/m3) and spatially uniform, and showed no statistically significant association with traffic intensity (r ≈ −0.03). Traffic indicators were instead weakly but significantly correlated with the vehicle-emitted gases NO2 (r ≈ 0.16) and CO (r ≈ 0.10), which they preceded by up to about one hour. A short-horizon PM2.5 nowcasting task, evaluated across temporal resolutions with time-series cross-validation, was dominated by temporal persistence, with traffic-derived features contributing negligibly. The absence of a summer traffic–PM2.5 association does not preclude such a relationship during the heating season, when particulate levels are higher. The results indicate that the traffic–air-quality relationship in Almaty is season- and pollutant-dependent, and demonstrate a lightweight, reproducible platform suitable for longitudinal monitoring and future heating-season assessment.