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2006 research outputs found
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Development of sensor systems for floodwater monitoring and alerting
No full textThis study addresses the systematic prediction of river water levels in Kazakhstan via hy-drological computations, which are essential for forecasting water-related events and formulatingplans for sustainable water resource management. Particular focus is placed on the significance ofprompt and efficient monitoring of river dynamics to alleviate natural disasters such as floods andmudflows, especially in high-risk places like Almaty, situated in geologically unstable mountainouslandscapes. The research focuses the potential of intelligent sensor-based monitoring systems thatcan gather real-time data on water levels, precipitation, soil moisture, and various environmentalconditions. Systems integrated with artificial intelligence and data analysis can substantiallyaugment decision-making processes, facilitate early warning mechanisms, and boost the precisionof forecasts. This method ultimately protects natural ecosystems and local communities from thedetrimental effects of hydrological hazard
The Art of Personalized Student-Supervisor Matchings
No full textThe process of student-supervisor matching is a critical yet complex task in higher education institutions, directly influencing research productivity, student satisfaction, and workload distribution. The ability to assign students to the most suitable supervisors is essential for fostering strong academic relationships, optimizing institutional resources, and improving research outcomes. However, traditional manual assignment methods often lead to inefficiencies, subjective biases, and an imbalance in workload distribution. As a result, automated recommendation systems have emerged as a promising solution to enhance the efficiency and fairness of student-supervisor pairings. This study evaluates three recommendation algorithms—Singular Value Decomposition (SVD)-based collaborative filtering, graph-based matching using the Hungarian Algorithm, and machine learning via Random Forest Regression—to determine their effectiveness in optimizing student-supervisor assignments. A rigorous empirical analysis is conducted across five key performance metrics: accuracy, fairness, stability, scalability, and computational efficiency. The findings reveal that while collaborative filtering performs well with established datasets, it struggles with novel cases due to its dependence on prior interactions. The Hungarian Algorithm guarantees optimal matching but faces scalability challenges, particularly in large academic institutions with thousands of students and supervisors. Meanwhile, Random Forest Regression effectively captures complex compatibility patterns but requires extensive labeled data, limiting its applicability in cases where historical matching data is sparse or unavailable. To overcome these limitations, the study proposes an adaptive hybrid framework that integrates the strengths of all three approaches. The hybrid model leverages collaborative filtering’s ability to recognize patterns in existing data, the Hungarian Algorithm’s precision in optimal pairings, and the predictive power of machine learning. By combining these methodologies, the proposed system enhances match accuracy, ensures fair workload distribution, and remains computationally efficient for large-scale institutional implementation. Additionally, the framework introduces dynamic adaptation mechanisms that allow the system to update recommendations based on real-time changes in student preferences and supervisor availability, making it more practical for real-world applications. The research contribution is a comprehensive, empirically validated hybrid framework that improves student-supervisor matching by balancing accuracy, fairness, and efficiency. This study provides educational institutions with actionable guidelines for scalable and equitable assignment processes, ultimately contributing to more effective mentorship experiences, improved research collaborations, and enhanced academic outcomes
Modeling and Forecasting DigitalCurrency Volatility with GARCH(1,1)
No full textThe burgeoning field of digital currencies presents unique challenges for predictive modeling due totheir inherent volatility and market dynamics distinct from traditional financial assets.We study the use of the GARCH(1,1) model to characterize and forecast the conditional volatility ofdaily Bitcoin returns. Using standard OHLCV data, we estimate a parsimonious GARCH(1,1) specificationand produce one-step-ahead volatility forecasts. We discuss model assumptions, stability conditions, andpractical considerations for risk metrics (e.g., VaR). The aim is to document a transparent, reproduciblepipeline rather than to compare exhaustively against alternative models. Results illustrate how a standardGARCH(1,1) specification can provide interpretable volatility estimates for Bitcoin, serving as a transparentbaseline rather than a novel predictive breakthrough
Девушки и женщины в инновациях и технологическом развитии в Казахстане
No full textИсследование посвящено анализу представленности девушек и женщин в сфере науки, технологий и инноваций в Казахстане. На основе статистики, обзора международных и национальных тенденций, а также опросов студентов и преподавателей ВУЗов Алматы исследованы барьеры и возможности участия женщин в науке, технологиях и инновациях. Оценено представительство женщин в STEM-занятости и потенциал в предпринимательстве. Особое внимание уделено роли университетских инкубаторов и студенческой экосистемы. По итогам исследования сформулированы рекомендации, направленные на развитие инклюзивной инновационной среды и поддержку женщин в научно-технологическом развитии страны
Text Classification for AI Generated Content with Machine Learning and Deep Learning Models
Get PDFThe rapid development of generative AI models, such as GPT-4, LLaMA, and Gemini, is causing an explosion of AI-generated text that may be akin to human writing. This poses a challenge in differentiating between AI generated content and human-authored text across a range of verticals: academic integrity, misinformation detection, and content moderation. This paper presents a comparison of machine learning and deep learning models on the classifier for AI-generated text. We compare the performance of Logistic Regression with TF-IDF features, a Bi-LSTM model, and a fine-tuned DistilBERT model on data from the COLING Workshop on MGT Detection Task 1, involving text samples from five AI models and human authors. Our experiments showed that Bi-LSTM outperforms other models, yielding the best results in accuracy (90.09%) and F1-score (90.02%). We further present the binary classification performance that distinguishes AI-generated text from human-written content, with an accuracy of 95.9%. It is suggested that deep learning methods are competent in detecting AI-generated text, though there are certain limitations, including adversarial attacks and changing styles of AI-generated writing. Future work will be focused on enhancing model robustness through adversarial training and hybrid architectures
DEVELOPMENT AND OPTIMIZATION OF PHYSICS-INFORMED NEURAL NETWORKS FOR SOLVING PARTIAL DIFFERENTIAL EQUATIONS
No full textThis work compares the advantages and limitations of the Finite Difference Method with Physics-Informed Neural Networks, showing where each can best be applied for different problem scenarios. Analysis on the L2 relative error based on one-dimensional and two-dimensional Poisson equations suggests that FDM gives far more accurate results with a relative error of 7.26 × 10-8 and 2.21 × 10-4 , respectively, in comparison with PINNs, with an error of 5.63 × 10-6 and 6.01 × 10-3 accordingly. Besides forward problems, PINN is realized also for forward-inverse problems which reflect its ability to predict source term after its sufficient training. Visualization of the solution underlines different methodologies adopted by FDM and PINNs, yielding useful insights into their performance and applicabilit
Медициналық ұйымда кадрларды даярлау мен дамыту жүйесін қалыптастыру
No full textДиссертациялық жобаның өзектілігі. Қазақстанның денсаулық сақтау жүйесіндегі дәйекті модернизация процесі тек ұлттық құрылымдарға ғана емес, сонымен қатар медициналық ұйымдарға қатысты өзгерістердің қажеттілігі туралы түсініктің пайда болуына түбегейлі әсер етеді. Сонымен қатар, медициналық ұйымдарды жақсарту стратегиясына, олардың жұмыс жағдайына, көрсетілетін қызметтердің сапасына қойылатын талаптар артып келеді. Бұл негізінен кадрларды дұрыс таңдауға байланысты екені жасырын емес. Осылайша, кадр саясаты саласындағы өзгерістер, кадрлық стратегиялық шешімдерді жетілдіру, медициналық ұжымның уәждемесін күшейту, сондайақ ішкі ұйымдық қарым-қатынастарды үйлестіру, ең алдымен, мемлекеттік, институционалдық және қоғамдық мүдделерге сай болуы керек. Жоғарыда аталғандар қазіргі уақытта Қазақстан Республикасындағы медициналық ұйымдардың персоналды басқару жүйесінде орын алып отырған бірқатар қиындықтар мен қарама-қайшылықтарды анықтауға мүмкіндік береді
Продажа контрафактной продукции через маркетплейсы в Республике Казахстан: пробелы в регулировании и международный опыт
No full textВ условиях стремительного роста электронной коммерции маркетплейсы становятся одними из ключевых площадок для реализации товаров, включая контрафактную продукцию. В Республике Казахстан проблема продажи поддельных товаров через онлайн-платформы обостряется из-за недостаточности правовых механизмов и ограниченной ответственности самих маркетплейсов. В настоящей статье проводится анализ текущего состояния регулирования электронной торговли в Республике Казахстан с акцентом на борьбу с контрафактом. Выявлены основные пробелы в законодательстве, в том числе отсутствие четкого определения ответственности маркетплейсов за размещаемый контент и ограниченные полномочия контролирующих органов. В качестве основы для выработки эффективных мер противодействия исследуется международный опыт, в частности правовые подходы Европейского союза, США и Китая. Рассматриваются такие инструменты, как «принцип активной платформы», механизмы предварительной модерации контента, обязательное сотрудничество с правообладателями и применение цифровых технологий для отслеживания происхождения товаров. По результатам анализапредложены рекомендации по совершенствованию национального законодательства и внедрению комплексного подхода к борьбе с контрафактной продукцией в цифровой торговле
BENEFITS AND CHALLENGES OF PEER ASSESSMENT IN HIGHER EDUCATION: A COMPREHENSIVE LITERATURE REVIEW
No full textPeer assessment has been described as a collaborative method of assessment that promotes active and reflective learning. It is particularly useful in the higher educational context since university students are required to be involved in more autonomous and student-centered activities. Therefore, university instructors should consider alternative forms of assessment. However, peer assessment is often neglected by instructors, and students are not prepared to employ peer practices. The aim of the current literature review is to analyze the previous studies on peer assessment in higher education. Particularly, it will describe the reasons for using peer assessment, its forms, and the benefits and challenges of employing peer assessment in higher education. Therefore, the findings will raise the awareness of teachers and students regarding the advantages and limitations of peer assessment techniques. To achieve this goal, the review of 30 articles, which were related to the implementation of peer assessment in higher education is presented. The articles were selected from various databases, namely Google Scholar, ERIC, and JSTOR, between the years 2015 and 2024
