[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124913-en":3,"doc-seo-124913-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},124913,4398048950312,"Violet","https://ap-avatar.wpscdn.com/avatar/400002538284de19e3c?_k=1778320343897328908",8,"Research & Report","DEEP AND MACHINE LEARNING MODELS FOR RECOGNIZING STATIC AND DYNAMIC GESTURES OF THE KAZAKH ALPHABET - paper","The work develops machine learning and deep learning approaches for recognizing gestures from the Kazakh sign language alphabet, supporting communication for individuals with limited abilities. The research focuses on hand-gesture classification and recognition, with the primary difficulty being dynamic gesture identification, since 42 letters exist and 12 are dynamic. By combining multiple recognition methods, the study aims to construct a hybrid neural network model. The discussion also frames gesture recognition within pattern recognition theory and computer vision applications.","DOI: 10. 37943/18JYLU4904  \n© Samat Mukhanov*, Raissa Uskenbayeva, Abdul Ahmad Rakhim, Young Im Cho, Aknur Yemberdiyeva, Zhansaya Bekaulova  \n75  \nDOI: 10.37943/18JYLU4904  \nSamat Mukhanov*  \nPhD, Senior-lecturer, Department of Computer Engineering [s.mukhanov@iitu.edu.kz](s.mukhanov@iitu.edu.kz), [orcid.org/0000-0001-8761-4272](orcid.org/0000-0001-8761-4272)[ ](orcid.org/0000-0001-8761-4272)International Information Technology University, Kazakhstan  \nRaissa Uskenbayeva  \nDoctor of technical science, Professor, Vice-Rector for Academic Affairs [r.k.uskenbayeva@satbayev.university](r.k.uskenbayeva@satbayev.university), [orcid.org/0000-0002-8499-2101](orcid.org/0000-0002-8499-2101)[ ](orcid.org/0000-0002-8499-2101)Satbayev University, Kazakhstan  \nAbdul Ahmad Rakhim  \nPhD, Professor, Department of Computing and Informatics [abdrahim@uniten.edu.my](abdrahim@uniten.edu.my), [orcid.org/0000-0001-7923-0105](orcid.org/0000-0001-7923-0105)[ ](orcid.org/0000-0001-7923-0105)Universiti Tenaga Nasional, Malaysia  \nYoung Im Cho  \nPhD, Professor, Faculty of Computer Engineering  \n[yicho@gachon.ac.kr](yicho@gachon.ac.kr), [orcid.org/0000-0003-0184-7599](orcid.org/0000-0003-0184-7599)  \nGachon University, Korea  \nAknurYemberdiyeva  \nMaster of Technical Sciences, Lecturer, Department of Computer Engineering  \n[a.yemberdiyeva@iitu.edu.kz](a.yemberdiyeva@iitu.edu.kz), [orcid.org/0009-0005-5078-2412](orcid.org/0009-0005-5078-2412)[ ](orcid.org/0009-0005-5078-2412)International Information Technology University, Kazakhstan  \nZhansaya Bekaulova  \nMaster of Technical Sciences, Senior-lecturer, Department of Computer Engineering  \n[zh.bekaulova@iitu.edu.kz](zh.bekaulova@iitu.edu.kz), [orcid.org/0009-0000-9339-9222](orcid.org/0009-0000-9339-9222)[ ](orcid.org/0009-0000-9339-9222)International Information Technology University, Kazakhstan  \nDEEP AND MACHINE LEARNING MODELS FOR RECOGNIZING STATIC AND DYNAMIC GESTURES OF THE KAZAKH ALPHABET  \nAbstract: Currently, an increasing amount of research is directed towards solving tasks using computer vision libraries and artificial intelligence tools. Most common are the solutionsand approaches utilizing machine and deep learning models of artificial neural networks for recognizing gestures of the Kazakh sign language based on supervised learning methods and deep learning for processing sequential data. The research object is the Kazakh sign language alphabet aimed at facilitating communication for individuals with limited abilities. The research subject comprises machine learning methods and models of artificial neural networksand deep learning for gesture classification and recognition. The research areas encompass Machine Learning, Deep Learning, Neural Networks, and Computer Vision.  \nThe main challenge lies in recognizing dynamic hand gestures. In the Kazakh sign language alphabet, there are 42 letters, with 12 of them being dynamic. Processing, capturing, and recognizing gestures in motion, particularly in dynamics, pose a highly complex task. It is imperative to employ modern technologies and unconventional approaches by combining various recognition methods/algorithms to develop and construct a hybrid neural network model for gesture recognition. Gesture recognition is a classification task, which is one of the directions  \nCopyright © 2024, Authors. This is an open access article under the Creative Commons CC BY-NC-ND license Received: 31.05.2024 Accepted: 25.06.2024 Published: 30.06.2024  \n76  \nScientific Journal of Astana IT University ISSN (P): 2707-9031 ISSN (E): 2707-904X VOLUME 18, JUNE 2024  \nof pattern recognition. The fundamental basis of recognition is the theory of pattern recognition. The paper discusses pattern recognition systems, the environment and application areas of these systems, and the requirements for their development and improvement. It presents tasks such as license plate recognition, facial recognition, and gesture recognition. The field of computer vision in image recogniti","cbCaidfvwzEJtM9M","https://ap.wps.com/l/cbCaidfvwzEJtM9M","pdf",6528942,1,21,"English","en",105,"# Abstract\n# Keywords\n# Introduction\n## Pattern recognition theory and applications\n## Cognitive mechanism and pattern matching\n# Gesture recognition challenges and dynamic gestures","[{\"question\":\"What is the document’s main objective?\",\"answer\":\"To design machine learning and deep learning models for recognizing static and dynamic gestures of the Kazakh sign language alphabet.\"},{\"question\":\"Why are dynamic gestures considered the main challenge?\",\"answer\":\"In this alphabet, there are 42 letters and 12 are dynamic, so capturing and recognizing gestures in motion is highly complex.\"},{\"question\":\"Which technical areas and methods are highlighted?\",\"answer\":\"The document emphasizes machine learning, deep learning, neural networks, and computer vision, and lists keywords such as SVM, LSTM, CNN, and MediaPipe.\"}]","DEEP AND MACHINE LEARNING MODELS FOR RECOGNIZING STATIC AND DYNAMIC GESTURES OF THE KAZAKH ALPHABET - 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