01. Faculty of Engineering & Natural Sciences
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Browsing 01. Faculty of Engineering & Natural Sciences by Subject "accuracy"
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Item ANALYZE AND DEVELOPMENT SYSTEM WITH MULTIPLE BIOMETRIC IDENTIFICATION(СДУ хабаршысы - 2020, 2020) Baimuratov O. ; Dadakhanov Sh.Abstract. Cause of a rapid increase in technological development, increasing identity theft, consumer fraud, the threat to personal data is also increasing every day. Methods developed earlier to ensure personal the information from the thefts was not effective and safe. Biometrics were introduced when it was needed technology for more efficient security of personal information. Old-fashioned traditional approaches like Personal identification number (PIN), passwords, keys, login ID can be forgotten, stolen or lost. In biometric authentication system, user may not remember any passwords or carry any keys. As people they recognize each other by the physical appearance and behavioral characteristics that biometric systems use physical characteristics, such as fingerprints, facial recognition, voice recognition, in order to distinguish between the actual user and scammer. In order to increase safety in 2005, biometric identification methods were developed government and business sectors, but today it has reached almost all private sectors as Banking, Finance, home security and protection, healthcare, business security and security etc. Since biometric samples and templates of a biometric system having one biometric character to detect and the user can be replaced and duplicated, the new idea of merging multiple biometric identification technologies has so-called multimodal biometric recognition systems have been introduced that use two or more biometric data characteristics of the individual that can be identified as a real user or not.Item 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.