INTEGRATED INTELLIGENT SYSTEM FOR SCIENTIFIC AND EDUCATIONAL INFORMATION RETRIEVAL
DOI:
https://doi.org/10.17770/etr2024vol2.8028Keywords:
artificial intelligence, pattern recognition, voice recognition, information retrieval, database, knowledge baseAbstract
The relevance of creating information systems using artificial intelligence methods and tools is dictated by the following reasons: The volume of scientific and educational information is growing; Traditional information retrieval methods have exhausted themselves. Using only deterministic and iteration methods, rigid algorithms don't give the expected results. They require more time to process information and more memory. Significant progress in recent years in the development of artificial intelligence (AI) methods and systems gives hope that their use will significantly reduce the time needed to search for data for scientific research and educational activities. The aim of the research results presented in the article is to increase the efficiency for scientific and educational information retrieval based on the use of AI methods implemented in the integrated intelligent information system “SMART TUIT". The article presents the results of theoretical and applied research obtained by several departments of the Tashkent University of Information Technology (TUIT) in solving the following tasks: Voice recognition for subsequent processing; Pattern recognition in order to identify the users of information; Search and processing of scientific and educational resources in electronic libraries; Analysis of information needs of users depending on the level of competence and type of activity; Evaluation of scientific and educational information to identify the most important data sources; Geoinformation system to solve the problems of the location of the information source. Initially, each research area in the departments was aimed at solving a certain class of problems related to medicine, linguistics, electronic libraries, corporate networks, information security systems, etc. The TUIT creative group decided to combine efforts to apply the results obtained to solve the important problem of intellectualizing the search for sources of scientific and educational information among a large amount of data.
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References
A. I.Kapterev, Cognitive management and artificial intellect in libraries:Possibilities and highlights // Scientific and technical libraries. 2023. No. 6. P. 113–137. https://doi.org/10.33186/1027-3689-2023-6-113-137.
Asefeh Asemi, Andrea Ko and Mohsen Nowkarizi, Published by Emerald Publishing Limited. Intelligent libraries: a review on expert systems, artificial intelligence,and robot. Library Hi Tech Vol. 39 No. 2, 2021 pp. 412-434 Emerald Publishing Limited 0737-8831.DOI 10.1108/LHT-02-2020-0038
G. Liu, “The application of intelligent agents in libraries: a survey”, 2011, Program, Vol. 45 No. 1, pp. 78-97, doi: 10.1108/00330331111107411.
DCMI Metadata Terms. https://www.dublincore.org/specifications/dublin-core/dcmi-terms/
M. Musaev, I.Khujayorov, M Ochilov, The use of neural networks to improve the recognition accuracy of explosive and unvoiced phonemes in Uzbek language. 2020 Information Communication Technologies Conference (ICTC). China, 2020. -рр. 231-234.
M. Musaev, S. Mussakhojayeva, I. Khujayorov, Y. Khassanov, M.Ochilov, H.A. Varol, USC: An Open-Source Uzbek Speech Corpus and Initial Speech Recognition Experiments. Speech and Computer 23rd International Conference, SPECOM 2021. St. Petersburg, Russia, September 27–30, 2021, Proceedings. – рр. 437-447.
A.V. Sozykin, An Overview of Methods for Deep Learning in Neural Networks.. Bulletin of the South Ural State University. Series: Computational Mathematics and Software Engineering. 2017. vol. 6, no. 3. pp. 28–59. (in Russian) DOI: 10.14529/cmse170303.
A.Graves, Jaitly, N. Towards end-to-end speech recognition with recurrent neural networks. In Proceedings of the International Conference on Machine Learning, Beijing, China, 21 June–26 June 2014; pp. 1764–1772.
Yeh, C., Wang, Y., Shi, Y., Wu, C., Zhang, F., Chan, J., & Seltzer, M.L. (2021). Streaming Attention-Based Models with Augmented Memory for End-To-End Speech Recognition. 2021 IEEE Spoken Language Technology Workshop (SLT), pp. 8-14.
Sak, H.; Shannon, M.; Rao, K.; Beaufays, F. Recurrent Neural Aligner: An Encoder-Decoder Neural Network Model for Sequence to Sequence Mapping. In Proceedings of the INTERSPEECH, ISCA, Stockholm, Sweden, 20–24 August 2017; pp. 1298–1302.
Mukhamadiyev, A., Mukhiddinov, M. , Khujayarov, I., Ochilov, M., Cho, J., Development of Language Models for Continuous Uzbek Speech Recognition System, Sensors 2023, 23(3), 1145; https://doi.org/10.3390/s23031145
Musaev M., Khujayorov I., Ochilov M., Speech Recognition Technologies Based on Artificial Intelligence Algorithms, 14th International conference intelligent human computer interaction, Tashkent, Uzbekistan, October 20-22, 2022, https://doi.org/10.1007/978-3-031-27199-1
Ramasamy, Ammuthavali & Ali, Nor'ashikin. (2021). High value information for managers in organisations. electronic Journal of Computer Science and Information Technology. 7. 10.52650/ejcsit.v7i1.110.
Bendechache, Malika & Limaye, Nihar & Brennan, Rob. (2020). Towards an Automatic Data Value Analysis Method for Relational Databases. 10.5220/0009575508330840.
Rakhmatullaev M., Normatov Sh. Formalizing the processes of evaluating the characteristics of scientific and educational information resources. International Conference on Information Science and Communications Technologies (ICISCT), Tashkent, Uzbekistan, 2022, pp. 1-4, doi: 10.1109/ICISCT55600.2022.10146831.
M. Rakhmatullaev, S. Normatov, and F. Bekkamov, “FUZZY RELATIONS BASED INTELLIGENT INFORMATION RETRIEVAL FOR DIGITAL LIBRARY USERS”, ETR, vol. 2, pp. 80–83, Jun. 2023, doi: 10.17770/etr2023vol2.7218.
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Copyright (c) 2024 Mukhammadjon Musaev, Marat Rakhmatullaev, Sherbek Normatov, Kamoliddin Shukurov, Malika Abdullaeva
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