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Research and analysis document management system at the university
No full textIn today’s IT landscape, the selection and implementation of Document Management Systems (DMS) are vital for improving decision-making and automating various workflows. DMS enables efficient document storage and access through digitization and automation, significantly reducing the time and costs associated with manual document management. For DMS to function optimally and meet user needs, intuitive user interfaces(UI) are essential. Despite the critical role of DMS in IT infrastructure, comprehensive analyses of this software are rare. This thesis addresses this gap by providing a detailed, real-world analysis of a university’s DMS and its impact on business processes. Through advanced database analysis, the study identifies key issues such as user accessibility, and retrieval inefficiencies. The research offers practical solutions, including structured workflows, standardized processes. The findings emphasize the importance of user-centric design, continuous training, and support structures to enhance user interaction and system efficiency. This work not only optimizes the university’s document management but also offers a framework adaptable to various organizational contexts, ensuring compliance with regulatory standards and improving overall information management practices
Управление брендом медицинской организации
Get PDFВведение: Управление брендом медицинских организаций не теряет своей актуальности в контексте современной здравоохранительной индустрии. Так, для реализации утвержденной Главой государства стратегии развития и входа в число 50-ти развитых стран мира необходимо провести ряд мероприятий по формированию и диверсификации инновационной экономики, что позволит повысить внутреннюю конкурентоспособность страны на данном рынке. В условиях конкуренции и стремительного развития технологий эффективное управление брендом становится ключевым фактором успеха для медицинских организаций. Цель: Развитие бренда медицинской организации на примере ТОО «Almaty Vision». Методы: Для достижения целей исследования применяются комбинированные методы, включая анализ литературных источников, анкетирование потребителей медицинских услуг, проведение SWOT анализа в отношении положительных и негативных сторон использования бренда медицинской организации, анализ использования различных рекламных каналов среди конкурентов организации ТОО«Almaty Vision» . Результаты: Исследование позволило выявить текущее восприятие бренда медицинской организации как пациентами, так и другими заинтересованными сторонами (партнеры, учредители, конкуренты). Так, были выделены основные аспекты, оказывающие влияние на формирование и развитие бренда медицинской организации, такие как качество медицинских услуг, взаимодействие с пациентами, маркетинговые стратегии и т.д. На основе полученных результатов сформулированы практические рекомендации для медицинских организаций по улучшению управления своим брендом и повышению его конкурентоспособности на рынке здравоохранения. Заключение: Управление брендом на рынке медицинских услуг требует комплексного и продуманного подхода, а также постоянного внимания к изменениям в отрасли и потребностям клиентов. В случае успешной реализации всех мероприятий по развитию бренда, компания сможет достичь высоких результатов и укрепить свою позицию на рынке медицинских услуг
A Career Path Recommendation System For Computer Science Students
Get PDFThis thesis presents the design, implementation, and evaluation of the Hybrid Career Path Recommendation System (HCPR), a sophisticated tool tailored specifically for guiding computer science students in their career decisions. The HCPR system innovatively combines Content-Based Filtering (CBF) and Collaborative Filtering (CF) methods into a hybrid model to enhance the accuracy and personalization of job recommendations. This integration addresses the inherent limitations of using either approach in isolation and leverages their combined strengths to improve recommendation quality. The system utilizes a comprehensive dataset that includes detailed user profiles from Stack Overflow and job postings from LinkedIn. The CBF component analyzes user profiles to match students with jobs that align with their skills and educational backgrounds, while the CF component predicts user preferences based on historical interaction patterns, enhancing the system’s ability to recommend jobs that users are likely to find appealing. The HCPR system’s performance is rigorously evaluated using precision, recall, F1-score, and ranking metrics such as Mean Reciprocal Rank (MRR) and Normalized Discounted Cumulative Gain (NDCG). The results demonstrate a significant improvement in recommendation accuracy and user satisfaction compared to standalone filtering approaches. The theoretical contributions of this thesis include advancements in hybrid recommendation system methodologies and a novel application of these systems to career guidance for computer science students. Practically, the HCPR system provides actionable insights that help students navigate the complex job market, potentially improving educational and career outcomes. This thesis concludes with suggestions for future research, emphasizing the potential for further refinement of the system and its adaptation to other fields beyond computer science. This work contributes to the fields of educational technology and recommender systems by demonstrating how integrated data-driven approaches can be effectively applied to personal and professional development tools
Semi-Regular Continued Fractions with Fast-Growing Partial Quotients
Get PDFIn number theory, continued fractions are essential tools because they provide distinct representations of real numbers and provide information about their characteristics. Regular continued fractions have been examined in great detail, but less research has been carried out on their semi-regular counterparts, which are produced from the sequences of alternating plus and minus ones. In this study, we investigate the structure and features of semi-regular continuous fractions through the lens of dimension theory. We prove a primary result about the Hausdorff dimension of number sets whose partial quotients increase more quickly than a given pace. Furthermore, we conduct numerical analyses to illustrate the differences between regular and semi-regular continued fractions, shedding light on potential future directions in this field
КРИТЕРИИ ОЦЕНКИ ЭФФЕКТИВНОСТИ РАБОТЫ СОТРУДНИКОВ МЕДИЦИНСКОЙ ЛАБОРАТОРИИ
Get PDFВ статье исследуются критерии оценки эффективности работы среднего медицинского персонала в медицинских лабораториях Республики Казахстан. В условиях модернизации здравоохранения и повышения требований к качеству лабораторных услуг, особое внимание уделяется профессионализму, компетентности и мотивации лаборантов. Рассматриваются как объективные, так и субъективные критерии оценки, включая профессиональные навыки
Denoising face recognition system
Get PDFThis thesis investigates the influence of sophisticated denoising techniques on the efficacy of face recognition systems, particularly in environments characterized by substantial image noise. Considering the dependency of face recognition algorithms on the quality of input images, this research conducts a comprehensive evaluation of various denoising strategies, ranging from conventional filters like Median, Bilate-ral, and Gaussian, to advanced deep learning approaches, exemplified by the Deep Convolutional Neural Network (DnCNN). The Extended Yale B dataset, augmented with synthetically introduced noise, provides the basis for this empirical study. Employing quantitative metrics such as Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM), coupled with qualitative evaluations, this dissertation quantifies the enhancements in image quality and recognition precision afforded by each denoising method. The findings affirm that the integration of advanced denoising algorithms markedly improves the accuracy of face recognition systems, highlighting the efficacy of adaptive deep learning solutions in addressing the complexities introduced by noisy visual environments
Анализ кадровой обеспеченности отделении реанимации, интенсивной терапии и анестезиологии детских учреждений на примере АО «НЦПиДХ»
No full textВ Республике Казахстан наблюдается долгосрочный дефицит кадровых ресурсов в сфере здравоохранения, включая врачей анестезиологов реаниматологов, что снижает эффективность функционирования этой отрасли. Кадровые ресурсы играют ключевую роль в обеспечении эффективности здравоохранения, в том числе в повышении качества и доступности медицинской помощи населению. Был проведен анализ кадровой обеспеченности отделении реанимации, интенсивной терапии и анестезиологии в АО «НЦПиДХ». Результаты исследования позволили получить представление о текущих вызовах и потребностях в управлении кадрами врачей анестезиологов-реаниматологов в педиатрической службе. В рамках проведения диссертации были получены выводы и предложены практические рекомендации, направленные на повышение кадрового потенциала сотрудников ОАРИТ
Item matching based on image and text analysis
Get PDFIn recent years, the e-commerce sector has experienced exponential growth as more retailers and brands have opened online sites to reach a worldwide consumer base. The rise in online marketplaces and comparison sites, where consumers may evaluate and buy goods from different vendors or companies, is another effect of the digital transformation. But this has also resulted in a proliferation of sellers and products, making it challenging for buyers to identify the ideal items and for vendors to connect with their intended clientele. Product matching has emerged as a critical problem for efficient decision-making in the retail and supply chain management sectors in response to these challenges. Purpose of the product matching is to match identical or nearly same products across various sources across, such as different e-commerce websites, based on features and their attributes. It can help retailers to find in demand products and boost sales. On the other hand, customers can take benefit from technologies by finding products they needed, compare across different aggregators, and make decisions to purchase. However, product matching is a challenging task. In most cases same products have different names, descriptions and image cards. With its recent breakthroughs in object detection and image categorization, deep learning has come a long way. A convolutional neural network may identify identical products based on the input picture and text, which might be a label or tag. In this work, three pretrained deep convolutional models: MobileNet-V2, VGG-19, and ResNet-50, are implemented to find the most identical products based on text and image data. The best model for similarity detection based on text and image has been found using a variety of performance assessment techniques, including cosine similarity, Levenshtein distance, and custom metric score. The study concludes that the Mobilenet model is the best suitable model for handling these laborious tasks and supporting the development of a digital strategy plan
ISSUES OF THE INTEGRATIVE STEM LESSON FOR SCHOOL
Get PDFThis study was designed to determine the level of STEM integration, particularly in chemistry lessons, and what difficulties/benefits this action may have. The study included 18 articles by different authors on different topics, but with one goal - STEM integration. The result showed that out of 18 articles, 16 partially contain the idea of integrating STEM methodology. In particular, 7 articles were designed to integrate STEM in high school. the rest were intended for teachers in general. None of the articles were intended for elementary school
A thorough survey into the recognition of face emotion expression:experimental study, practical uses, and recommendations for the future
Get PDFThe growth of the volume of information, as well as the expansion of the range of technically complex decision-making tasks require the systematization of existing methods and the development of new techniques and algorithms for their solution. The master’s thesis examines the possibility of using a neural network to solve the problem of recognizing human emotions. Artificial neural networks offer promising prospects for development, and software has a great advantage in using them. Moreover, each task performed has an unlimited and non-standard set of solution methods. The article considers the possibility of using a neural network to solve the problem of recognizing human emotions. The increasing volume of data, along with the breadth of technologically sophisticated issues with solving, necessitates the systematization of existing approaches and the creation of new techniques and algorithms for their resolution. The master’s thesis investigates the feasibility of utilizing a neural network to tackle the challenge of identifying human emotions. Artificial neural networks provide tremendous growth opportunities, and software can benefit greatly from their use. Furthermore, each challenge contains an infinite and non-standardized collection of solution techniques. The article discusses the feasibility of utilizing a neural network to tackle the difficulty of identifying human emotions