Browsing by Author "Amirzhanov A."

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    Automatic Speech Recognition for the Kazakh Language
    (2022) Amirzhanov A.
    In the presented study, the problem of automatic speech recognition (ASR) for the Kazakh language was discussed. It starts with general information about the field such as the history of ASR, invented algorithms, and models. Then the state-of-the-art architectures in the world will be studied. The following is a specific topic of implemented automatic speech recognition systems for the Kazakh language. Studying the works related to the topic of the thesis, an end-to-end model based on the Connection Temporal Classifier (CTC) algorithm was chosen to conduct experiments with various types of optimizers. The results showed that the proposed method works better with the Momentum optimizer than others such as Adam and Adagrad.
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    Plagiarism types and detection methods: a systematic survey of algorithms in text analysis
    (Frontiers in Computer Science, 2025) Makhmutova A.; Turan C.; Amirzhanov A.
    Plagiarism in academic and creative writing continues to be a significant challenge, driven by the exponential growth of digital content. This paper presents a systematic survey of various types of plagiarism and the detection algorithms employed in text analysis. We categorize plagiarism into distinct types, including verbatim, paraphrasing, translation, and idea-based plagiarism, discussing the nuances that make detection complex. This survey critically evaluates existing literature, contrasting traditional methods like string-matching with advanced machine learning, natural language processing, and deep learning approaches. We highlight notable works focusing on cross-language plagiarism detection, source code plagiarism, and intrinsic detection techniques, identifying their contributions and limitations. Additionally, this paper explores emerging challenges such as detecting cross-language plagiarism and AI-generated content. By synthesizing the current landscape and emphasizing recent advancements, we aim to guide future research directions and enhance the robustness of plagiarism detection systems across various domains.