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«ПРЕПОДАВАНИЕ ГЕНЕТИКИ С ИСПОЛЬЗОВАНИЕМ СИСТЕМ ИСКУССТВЕННОГО ИНТЕЛЛЕКТА: ПРЕИМУЩЕСТВА И НЕДОСТАТКИ»
No full textИскусственный интеллект (ИИ) способен революционизировать образование, улучшая качество обучения учащихся и помогая преподавателям в их практической деятельности. Используя методы ИИ, такие как интеллектуальные системы обучения, виртуальные ассистенты и аналитика данных, классы могут стать более персонализированными, привлекательными и эффективными. Учащиеся получают выгоду от персонализированных 118 методов обучения, адаптивной обратной связи и возможностей для совместной работы, что приводит к улучшению понимания, развитию навыков критического мышления и самостоятельному обучению. Преподаватели, с другой стороны, могут автоматизировать административные задачи, получать информацию об успеваемости учащихся на основе данных и внедрять индивидуальные стратегии обучения. Интеграция искусственного интеллекта в образование обещает улучшить результаты обучения, повысить эффективность и преобразовать образовательный опыт для всех заинтересованных сторон
Анализ современных подходов к управлению человеческими ресурсами на уровне медицинской организации
No full textВ условиях глобализации и постоянных изменений в сфере здравоохранения, управление человеческими ресурсами (УЧР) выходит на передний план как ключевой компонент, определяющий качество и доступность медицинских услуг на мировом уровне. Цель исследования: изучить современные подходы к управлению человеческими ресурсами в медицинской организации для повышения эффективности и качества медицинских услуг. Процесс исследования: исследованы последние зарубежные и отечественные тенденции в управлении человеческими ресурсами в здравоохранении. Методы: количественный анализ, анкетирование, аналитический, теоретическое моделирование. Результаты: проанализирована результативность применяемых подходов к управлению человеческими ресурсами в медицинской организации и оценено их влияние на качество оказываемых медицинских услуг. Сформирован и обоснован комплекс современных подходов к УЧР в медицинской организации. Разработана концепция мобильного центра тактического менеджмента, которая представляет собой комплекс современных подходов к управлению человеческими ресурсами
Exploring the Role of HRM Practices on Employee Well-Being
Get PDFThis qualitative study tries to understand the role human resource management practices play on employee wellbeing in the context of higher education institutions in Kazakhstan. It mainly focused on two institutions namely SDU university in Almaty, Kazakhstan and Maqsut Narikbayev University in Astana, Kazakhstan. The study attempts to explore the interplay of dynamics between HRM practices and employee wellbeing, which has not been investigated in this context in the literature. This was done through combining grounded theory (Gioia, 2013) and the Eisenhardt Method (Eisenhardt, 2021) approaches. Significant findings of the research disclose that firstly, emphasis of SDU university is on inclusive and friendly community. The university establishes a welcoming atmosphere by hosting regular social activities and having a culture of acknowledgement. In comparison, the main priority of MNU seems to be employee’s autonomy and leadership. The intern empowers staff with independence and trust in managerial and administrative decisions. General challenge that both universities are facing is mainly healthcare and clear communication that is negatively influencing morale and wellbeing of employees. This study sides with the previous studies that highlight the importance of effective HRM practices for increasing the well-being of employees. While promoting the need for better healthcare, communication and establishment of better work-life balance in the institutions. By taking action regarding these problems, the institutions can improve and increase their already successful organizations. It will result in enhanced culture, employee engagement and positive workplace environment of the organization conducive to sustainable growth and development
Empowering Kazakh Text Generation: Developing a Meaningful Natural Language Processing Model for Kazakh Language
Get PDFNowadays, NLP is getting more and more popular due to the vast development of the computing units such as GPUs and CPUs. Especially, recent GPU models allow researchers to process analyse and process terabytes of text and audio data in a short period of time. The problem is that the data is not unlimited and has its own threshold value, therefore data augmentation is one of the techniques of data synthesis, the development of which can produce good effect on the Science of Kazakhstan, to be more precise on the kazakh language processing research area. This research aimed to develop and compare state-of-the-art uni-directional and bi-directional LSTM architectures in order to determine which one is more effective in a task of generating synthetic, kazakh language based text data. Generated synthetic data can be used in a variuos NLP tasks such as training NLP models, content generation, text classification and domain adaptation. By generating synthetic data scientist can overcome data limitations, model overfitting and open new abilities in adjusting and increasing model performance. Architectures used in this research are Uni-directional and Bi-directional LSTMs. After many training session and model parameters tuning the study shows that Bi-directional LSTM has its own pros and cons over the traditional Uni-directional LSTM. Bi-directional neural network learns well and understands the relationship between words better than Uni-directional one, but as expected the training process takes much more time as it extracts the information from the input text in both directions. The dataset used in this study consist of 10 000 unique rows of text taken from the kazakh language based Wikipedia area where each row has median as 80 word sequence. It worth to mention that in order to understand the model relationship between the words study proposes the usage of FastText pre-trained kazakh word vectors as an embedding layer for the both models. This layer turns the input words into vectors where similiar words have close vectors. Finally, the research takes a step towards generating synthetic data with the help of which future works can avoid the problems of data limitations, on the other hand researchers can save the time by ignoring ineffective model selection problem and considering this study’s models comparison
Advisory system for adapting a single machine problem to a distributed solution
Get PDFGeneral characteristics of the work. The work encompasses developing an advisory system to recommend solutions for single-machine problems adaptable to distributed systems, mainly focusing on implementation within the MapReduce platform. Methodologically, an experiment evaluated learning effectiveness, while extensive data collection informed model development. Predictive models, including Naive Bayes and Logistic Regression, were optimized and integrated into a recommendation system validated through rigorous evaluation. The aim of the research is to develop an advisory system that recommends single-machine problem solutions that adapt to distributed systems and are suitable for implementation on the MapReduce platform
Sanction Busting Within and Outside Regional Integration
Get PDFThe trade balance of Russia with its top 20 trade partners has increased over the last few years till 2022 with few exceptions such as the United States and the United Kingdom. Even the Russian trade deficit with Germany till 2021 has turned to trade surplus in 2022. However, as a result of the strict sanctions imposed on certain trade products by European Union (EU) nations and the US, the total import values of Russia declined from 199 billion in 2022. To explain if third countries, including Eurasian Economic Union (EAEU) nations, aid Russia by means of sanctions-busting, we offer several empirical tests. In this paper, we use annual cross-sectional data for 22,648 products exported to Russia along with tariff rates imposed on them in 2022. We first notice that even though the number of sanctioned exports is three times lower than that of non-sanctioned exports, the total import value from sanctioned exports is predicted to be larger than that from non-sanctioned exports on average by 0.36 percentage point. Second, the Russian import values from both EU and Rest of the World (ROW) exports are significantly greater than those from EAEU nations. Last but not least, EAEU nations, which are Russia's partners within the union, and EU nations, are more susceptible to activities such as sanction-busting compared to other third countries
Повышение эффективности работы медицинского персонала с помощью IT-технологии
Get PDFВ государственной программе “Цифровой Казахстан” мировые тренды ясно перечислены для достижения качественной и доступной национальной медицинской услуги. Однако в сфере здравоохранения существуют значительные области, недостаточно покрытые IT- технологиями. Цель этого исследования изучить мировой и национальный опыт применения IT- технологии в работе медицинского персонала, анализировать эффективность работы медицинского персонала с помощью IT- технологии и разработка практических рекомендаций по усовершенствованию МИС «Инфомед». Подводя итоги, изучен опыт применения IT- технологии в работе медицинского персонала, анализирован эффективность их работы с помощью IT- технологии, разработан практические рекомендаций по IT усовершенствованию МИС «Инфомед»
Master Thesis: “Intrusion detection system with deep packet inspection”
No full textThe rapid expansion of the internet has revolutionized modern life, yet it also poses significant risks due to vulnerabilities in increasingly complex software and networks. Addressing these risks requires robust methods for preventing attacks. Firewalls serve as critical security tools in network protection, distinguishing between secure and less secure networks and regulating information flow. However, they alone cannot fully safeguard against malicious activities. Intrusion Detection Systems (IDS), particularly those incorporating Deep Packet Inspection (DPI), have emerged as effective means of identifying and mitigating network threats. The research aims to evaluate the effectiveness of DPI algorithms in detecting and responding to network intrusions. By reviewing existing literature and conducting experiments, the study seeks to identify the most efficient algorithms based on speed and accuracy metrics. The research objectives include proposing algorithm designs to enhance IDS performance and developing an intrusion detection system algorithm. The methodology involves studying various DPI algorithms, normalizing datasets, and comparing algorithm performance through experimentation. Preliminary results from algorithms like K-Nearest demonstrate ongoing progress in algorithm development. While the project faces challenges such as research environment limitations, the commitment to understanding the subject thoroughly remains unwavering. By leveraging Linux operating systems and diligent research, the study aims to contribute valuable insights into intrusion detection system effectiveness, potentially leading to the development of more efficient algorithm
Real-time Sound Anomaly Detection in Industrial Environments with Deep Learning
No full textThis research uses deep learning to explore the field of sound anomaly detection in industrial settings in response to the growing need for improved industrial efficiency and safety. Centered on taking care of the constraints of conventional techniques, the study examines the effectiveness of several deep learning architectures, such as hybrid models, recurrent neural networks (RNNs), and convolutional neural networks (CNNs), in identifying abnormal noises. With a focus on rigorous evaluation of datasets, preprocessing methods, and benchmarks, the survey offers a thorough picture of the most recent models and their uses in a variety of industrial areas. This research compares deep learning with traditional methods for sound anomaly identification and looks at performance evaluation criteria. Case studies and realworld implementations demonstrate the usefulness of the enhancements. While highlighting the need for innovative approaches to enhance the practical usefulness and robustness of deep learning-based sound anomaly detection in industrial settings, the research also points out its shortcomings and makes recommendations for future directions. This research not only contributes valuable insights into the intersection of deep learning and industrial sound analysis but also serves as a pivotal guide for researchers and practitioners seeking to navigate the complexities of deploying effective sound anomaly detection systems
THE IMPORTANCE OF DEVELOPING SKILLS IN USING THE STEM EDUCATION SYSTEM IN CHEMISTRY LESSONS
No full textSTEM education, encompassing Science, Technology, Engineering, and Mathematics, is increasingly recognized for its vital role in fostering critical thinking, problem-solving, and innovation skills in students. Chemistry, a fundamental STEM subject, offers a unique platform for integrating these disciplines. By developing skills in using the STEM education system in chemistry lessons, students can:enhance their understanding of chemical concepts: STEM-based teaching emphasizes hands-on activities, simulations, and real-world problem-solving, fostering deeper engagement and conceptual comprehension. Develop problem-solving and analytical skills: Chemistry experiments and projects 22 encourage students to identify, analyze, and solve problems, honing their analytical thinking and critical inquiry abilities