Browsing by Author "Telman D."

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    Development of diploma platform and analyzing the impact of Beta Career platform to students diploma preparation
    (2023) Telman D.
    Internships for students are essential to their academic and professional development. This study examines how internships impact students’ graduation projects using a comparative methodology. The research looks at how internships impact the overall quality, importance, and creativity of students’ diploma projects. The study’s goal is to identify the key factors that control how internships affect final projects. It will look at a variety of subjects, such as how internship experiences relate to the project topic chosen, how practical skills are developed, and how real-world challenges are encountered. Additionally, the study will examine how internship mentorship and support might raise the quality and originality of students’ work. Graduate student unemployment is one of the world’s most pressing issues right now. I will study unemployment in Kazakhstan and compare it with student unemployment with the effect of the beta career program.
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    ItemOpen Access
    KAZAKH HANDWRITING RECOGNITION
    (СДУ хабаршысы - 2023, 2023) Bazarkulova A.; Mutalivev Y.; Chazhabayev A.; Telman D. ; Bazarkulova D.
    Abstract. Recognition of handwritten text is one aspect of object recognition and known as handwriting detection cause of a computer’s potential to recognize and comprehend readable handwriting from resources including paper files, touch smart devices, images, etc. Data is categorized into a number of classes or groups using pattern recognition. The paper presents a successful experiment in recognizing handwritten Kazakh text using Convolutional Recurrent Neural Network based architectures and the Kazakh Autonomous Handwritten Text Dataset. The proposed algorithm achieved an overall accuracy of 86.36% and showed promising results. However, the paper suggests that further research could be conducted to improve the model, such as correlating and enlarging the database or incorporating other models and libraries. Additionally, the paper emphasizes the importance of considering language specifics when building a text recognition model, as modern algorithms that work well in one language may not guarantee the same performance in another.