Browsing by Author "Sultanova N."

Now showing 1 - 4 of 4
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    IDENTIFYING SPAM MESSAGES FOR KAZAKH LANGUAGE USING HYBRID MACHINE LEARNING MODEL
    (СДУ хабаршысы - 2023, 2023) Bilakhanova A.; Ydvrys A. ; Sultanova N.
    Abstract. This paper describes a spam detection system for Kazakh Language using Hybrid Machine Learning Model. The lack of spam detection systems in the Kazakh language calls for the need of a proposed system that can identify unwanted messages. The system integrates multiple Machine Learning algorithms to accurately classify spam and non-spam messages. The performance of the system is evaluated using metrics such as accuracy, precision, recall, and Fl-score. Results show that the proposed solution outperforms existing spam detection techniques in terms of detecting spam with a low false positive rate and high accuracy. The findings of this research contribute to the development of effective spam detection systems for the Kazakh language and provide insights for future work in this field.
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    KAZAKH LANGUAGE-BASED QUESTION ANSWERING SYSTEM USING DEEP LEARNING APPROACH
    (СДУ хабаршысы - 2023, 2023) Bilakhanova A. ; Ydyrvs A.; Sultanova N.
    Abstract. Deep learning advances have resulted in considerable gains in a variety of natural language processing applications, including questionanswering (QA) systems. QA systems are intended to retrieve data from big datasets and respond to user queries using natural language. Deep learning-based techniques have yielded encouraging results in the development of QA systems capable of providing consistent answers to a wide range of inquiries. This research presents a deep learning-based Kazakh language-based QA system. A pre-processing module is also included in the proposed system to improve the quality of the input text and the accuracy of the final output. The results reveal that the system has a high level of accuracy. This study promotes to the advancement of question-answering technology and contributes to the development of natural language processing tools in the Kazakh language.
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    SMM (SUPPORT MEDICAL MACHINE)
    (СДУ хабаршысы - 2020, 2020) Askarova A. ; Gylymmedden Y. ; Temirbekova D.; Sultanova N.
    Abstract. Machine learning (ML) provides methods, techniques, and tools that can help solve diagnostic and prognostic problems in various fields of medicine. ML is used to analyze clinical parameters and their combinations for prognosis, such as disease progression, for maintenance therapy of treatment, and for General patient management. In our time, computer systems have already begun to be introduced into the healthcare environment, which makes it possible to facilitate and improve the efficiency of medical professionals and ultimately improve the efficiency and quality of medical care. Below is a demonstration to tell what the main applications of ML were used in the application and show how they can be useful for medical care. The main task was to combine all the useful functions that can help people in the field of medicine. The application will help to determine the authenticity of medicines through text recognition and search through the database of official drug registers, as well as using recommendation systems and algorithms (K-nearest neighbor Algorithm) to help in the selection of medicines, using the camera and phone flash to calculate the heart rate, which will accordingly help people with blood pressure problems. The application will be available on two platforms on 10S, as well as for the Android operating system, as it was implemented through the Flutter framework in Android Studio. For the algorithms, used Google's ML-Kit libraries. Jupyter, numpy, pandas, matplotlib, scipy, and scikit-learn Notepad are also used for data preparation.
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    USING THE GINI COEFFICIENT TO BUILD A CREDIT SCORING MODEL
    (СДУ хабаршысы - 2021, 2021) Sultanova N. ; Tulegenova A. ; Suleimen B.
    Abstract. The credit scoring model is widely used to predict the likelihood of a customer default. To measure the quality of such scoring models, you can use quantitative indicators such as the GINI index, KolmogorovSmirnov (KS) statistics, Lift, Mahalanobis distance, information statistics. This article discusses and illustrates the practical use of the GINI index.