Ochilov Mannon Musinovich

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Associate Professor, Artificial Intelligence/Computer engineering
Tashkent University of Information Technologies named after Muhammad al-Khwarizmi

Ochilov Mannon Musinovich


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https://researchid.co/mannon_ochilov

EDUCATION

Samarkand branch of Tashkent University of Information Technologies: Samarkand, Samarkand, UZ, 2012-09-02 to 2016-06-01 | bachelor (Informatics and Information Technologies)

Tashkent University of Information Technologies named after Muhammad al-Khwarizm: Tashkent, Tashkent, UZ 2016-09-02 to 2018-06-01 | magister (Computer systems)

RESEARCH, TEACHING, or OTHER INTERESTS

Artificial Intelligence, Signal Processing, Computer Vision and Pattern Recognition, Computer Engineering
21

Scopus Publications

313

Scholar Citations

10

Scholar h-index

12

Scholar i10-index

Scopus Publications

  • Accelerating Matrix Multiplication with CPU Multithreading and CUDA Block-Based GPU Parallelization
    Mekhriddin Rakhimov, Mannon Ochilov, Rashid Nasimov, Shakhzod Javliev
    Icfnds 2025 2025 the 9th International Conference on Future Networks and Distributed Systems, 2026
    As technology advances, we can see that the amount of data is also increasing. This article examines the problems associated with the speed of computing devices when performing arithmetic operations on large matrices. One of the optimal methods for matrix multiplication is to calculate a large matrix by dividing it into blocks using the Block-based method. This is achieved by multiplying matrices of different sizes using the Block-based parallel method on the computer's graphics processor using CUDA (Compute Unified Device Architecture) technology, as well as on the central processor using the OpenMP (Open Multi-Processing) parallel library for devices without a graphics processor. The study examines the time-consuming problem of multiplying matrices of sizes 64x64, 128x128, 512x512, 1024x1024 and 2048x2048 using these parallel processing technologies, using the simple sequential Naive method and the parallel Block-based method. The study concludes with a systematic analysis of performance metrics for several block sizes (8x8, 16x16, 32x32, etc.), an assessment of the comparative efficiency of CPU and GPU matrix multiplication implementations, and the determination of optimal limits for real-world parallel processing by comparing the efficiency of block sizes on GPUs using the OpenMP parallel programming model for CPUs and CUDA technology for NVIDIA GPUs.
  • CUDA Block Size Optimization for Gaussian and Sobel Filters: Benchmarking Against CPU Implementations
    Mekhriddin Rakhimov, Mannon Ochilov, Shakhzod Javliev, Rashid Nasimov
    Icfnds 2025 2025 the 9th International Conference on Future Networks and Distributed Systems, 2026
    This article evaluates the efficiency of central processing unit (CPU) and graphics processing unit (GPU)-based computations in image filtering. Gaussian and Sobel filters were selected as the most effective methods for image filtering; these filtering processes are performed sequentially on the CPU and in parallel on the GPU. In order to organize parallel computations in image filtering, the most effective CUDA (Compute Unified Device Architecture) technology is used to implement parallel programming on the GPU. The CUDA programming model provides parallel processing of images by performing all computations in the kernel function of thousands of threads in blocks, which are grouped together. Therefore, the correct selection of GPU blocks in the program also affects the speed of the computation process. During the study, using the CUDA programming model, 8×8, 16×16, and 32×32 block sizes were selected appropriately for image filtering and allowed optimal use of GPU resources. As a result, large blocks (16x16 and 32x32) of images were filtered much faster than sequential image filtering on the CPU and small blocks (8x8) on the GPU due to the improved use of shared memory. Parallel image filtering on 8, 16 and 32 block sizes was accelerated by an average of 96.26% for the Gaussian filter and 91.41% for the Sobel filter compared to a conventional processor (CPU).
  • Development of Uzbek Question-Answering Models through Fine-Tuning Approaches
    Ilyos Khujayarov, Mannon Ochilov, Malika Abdullayeva, Orzimurod Kholmatov, Rashid Nasimov
    Icfnds 2025 2025 the 9th International Conference on Future Networks and Distributed Systems, 2026
    In this article, experiments were conducted on modern models such as T5, Flan-T5, mT5 and Llama for use in Retrieval-Augmented Generation (RAG) technologies. During the research, each model was fine-tuned based on a special domain data set consisting of Context, question, and answer columns, and the results were evaluated using evaluation metrics such as BLEU, ROUGE and METEOR. The use of RAG technology allows for searching for the necessary information from external knowledge bases and generating answers to questions. The results obtained are of great importance in the development of question-answer systems in the Uzbek language, including the development of auxiliary systems in areas such as call centers and public services.
  • Optimizing Block Size and Memory Usage for Image Processing on GPU Using CUDA
    Mekhriddin Rakhimov, Mannon Ochilov, Shakhzod Javliev, Azizbek Khojamurotov
    International Conference on Artificial Intelligence Computer Data Sciences and Applications Acdsa 2026, 2026
    In this article, the Canny algorithm was used to accelerate the computational processes by parallel processing in image processing. In this case, all stages of the Canny algorithm were implemented in the computer's graphics processor using Compute Unified Device Architecture technology, and in order to accelerate the computational process, the image data was optimally used from the graphics processor's global, shared memory, and texture memories. During the study, images of different sizes were tested in the global and shared memory of the graphics processor using the Compute Unified Device Architecture model to optimize the use of graphics processor memory, with block sizes of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$8 \times 8,16 \times 16$</tex> and <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$32 \times 32$</tex>. For comparison, the program execution was measured on the central processor using the Open Computer Vision Library. Based on the results obtained, the <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{8 x 8}$</tex> blocks of the graphics processor were considered the most optimal for small-sized images, and the <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{3 2} \boldsymbol{\times} \mathbf{3 2}$</tex> blocks were considered the most optimal for large-sized images. The <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$16 \times 16$</tex> block configuration showed the highest efficiency for all images. Shared memory and <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{1 6 x 1 6}$</tex> block configuration showed <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\sim 81$</tex> times faster performance compared to CPU on a <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$4096 \times 4096$</tex> image and <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\boldsymbol{\sim} \mathbf{4 9 \%}$</tex> faster performance compared to global memory.
  • Restoring Punctuation in Uzbek Texts Using LLM’s Fine Tuning Approaches
    Mukhammadjon Musaev, Mannon Ochilov, Malika Abdullaeva, Rashid Nasimov
    ACM International Conference Proceeding Series, 2025
    This study draws attention to the use of transformation-based ad-hoc tuning models for detecting and recovering punctuation marks in Uzbek language texts. The research was conducted to accurately predict punctuation marks including commas, full stops, exclamation marks and question marks using Bert and XML-Roberta architectures. The research is to develop a specialized database on key aspects of Uzbek punctuation, which helps the existence of focused research in this area. It has yielded good results in tests, confirming the potential of transformer models for punctuation recovery in low-resource languages, including Uzbek. The paper focuses on the development of tokenizers specialized exclusively for Uzbek. Methods for recovering punctuation marks in Uzbek language and improving model accuracy in case of class label mismatch were also discussed for future research. The study indicated that the mean F1 score in determining whether a word after 4 punctuation marks and punctuation marks is uppercase or lowercase in Uzbek language was 87.9%.
  • Analysis and Possibilities of Parallel Approach in Big Data Processing
    Mekhriddin Rakhimov, Mannon Ochilov, Shakhzod Javliev, Rashid Nasimov
    ACM International Conference Proceeding Series, 2025
    The amount of data is growing significantly these days as a result of technological advancements. In processing such large amounts of data, the computing resources of modern computers can also spend a lot of time, which in turn causes problems with the speed of data processing in computational processes. To eliminate such problems, it is advisable to perform parallel processing in computational processes and computing devices and provide the expected speed result. In this article, we will consider the problem of speed in processing large amounts of data, and we will study ways to solve the speed problems in computational processes using OpenMP, one of the most effective parallel processing technologies for central processors. Also, at the end of our research, we will make a comparative analysis of the efficiency achieved through parallel processing technologies and present our comparative results.
  • High-Performance Matrix Multiplication Using Block Parallelization on CPU and GPU
    Mekhriddin Rakhimov, Mannon Ochilov, Shakhzod Javliev
    Proceedings of the 17th International Scientific and Technical Conference Actual Problems of Electronic Instrument Engineering Apeie 2025, 2025
    This paper examines the performance issues associated with computing devices performing arithmetic operations on large matrices. One of the optimal methods for matrix multiplication is to calculate a large matrix by dividing it into blocks using the block-based method. This is achieved by multiplying matrices of different sizes using the parallel block-based method on a computer’s graphics processing unit using Compute Unified Device Architecture technology, as well as on a central processor using the Open Multi-Processing parallel library for devices without a graphics processing unit. The study examines the time-consuming task of multiplying matrices of sizes 64x64, 128x128, 512x512, 1024x1024, and 2048x2048 using a simple sequential naive method and a parallel block-based method using these parallel processing technologies. The performance of the block parallel method implemented on a graphics processing unit using Compute Unified Device Architecture technology and on a central processing unit using Open Multi-Processing technology is also compared with the existing CUDA basic linear algebra subprograms libraries for NVIDIA graphics processing units and Intel Math Kernel Library for Intel processors. The proposed approach allows the user to fully control the programming model, customize the algorithm, change the block size, and perform computations as quickly as existing libraries.
  • Text Processing Technology In Uzbek Speech To Sign Language Translation Systems
    Mukhammadjon Musaev, Dilshod Juraev, Mannon Ochilov, Malika Abdullaeva
    Aip Conference Proceedings, 2024
  • Ensuring Information Security in the Intelligent Scientific and Technical Information Systems
    Komil Kerimov, Zarina Azizova, Fayzi Bekkamov, Mekhriddin Rakhimov, Mannon Ochilov
    Vide Tehnologija Resursi Environment Technology Resources, 2024
    Scientific and scientific-technical information is a valuable tool for the development of education, technology and society as a whole. The increase in the volume of information and the development of information networks of data exchange requires special means to ensure information protection of data. Methods, means and systems for information security of scientific, technical and scientific-educational resources in intellectual information systems are of particular importance. The purpose of the research is to develop methods and software tools to ensure information security of valuable scientific and technical information resources in intelligent information systems. The proposed solution for intrusion detection in intelligent system is a web application firewall, which is used for enhanced security, detecting and preventing attacks before they reach the web application. It will protect the system from a whole range of attacks while allowing HTTP traffic monitoring and analyzing small changes or persistent state online. The Web Application Firewall (WAF) has the following features: logging of all HTTP protocol transactions, including request termination permissions and logging of the response; HTTP traffic can be examined in real time to detect attacks; preventing attacks before they reach the web application. This work is performed within the framework of the project on creation of an integrated intelligent system “SMART TUIT”, which includes several subsystems (Information Retrieval, Voice Recognition, Pattern Recognition, Scientific Information Assessment, Geoinformation System).
  • Development of Language Models for Continuous Uzbek Speech Recognition System
    Abdinabi Mukhamadiyev, Mukhriddin Mukhiddinov, Ilyos Khujayarov, Mannon Ochilov, Jinsoo Cho
    Sensors, 2023
    Automatic speech recognition systems with a large vocabulary and other natural language processing applications cannot operate without a language model. Most studies on pre-trained language models have focused on more popular languages such as English, Chinese, and various European languages, but there is no publicly available Uzbek speech dataset. Therefore, language models of low-resource languages need to be studied and created. The objective of this study is to address this limitation by developing a low-resource language model for the Uzbek language and understanding linguistic occurrences. We proposed the Uzbek language model named UzLM by examining the performance of statistical and neural-network-based language models that account for the unique features of the Uzbek language. Our Uzbek-specific linguistic representation allows us to construct more robust UzLM, utilizing 80 million words from various sources while using the same or fewer training words, as applied in previous studies. Roughly sixty-eight thousand different words and 15 million sentences were collected for the creation of this corpus. The experimental results of our tests on the continuous recognition of Uzbek speech show that, compared with manual encoding, the use of neural-network-based language models reduced the character error rate to 5.26%.
  • Speech Recognition Technologies Based on Artificial Intelligence Algorithms
    Muhammadjon Musaev, Ilyos Khujayarov, Mannon Ochilov
    Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 2023
  • Uzbek Speech Synthesis Using Deep Learning Algorithms
    M. I. Abdullaeva, D. B. Juraev, M. M. Ochilov, M. F. Rakhimov
    Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 2023
  • Advanced Feature Extraction Method for Speaker Identification Using a Classification Algorithm
    M. Musaev, M. Abdullaeva, M. Ochilov
    Aip Conference Proceedings, 2022
  • Distribution of Operations in Heterogeneous Computing Systems for Processing Speech Signals
    Mekhriddin Rakhimov, Manon Ochilov
    15th IEEE International Conference on Application of Information and Communication Technologies Aict 2021, 2021
  • Formant set as a main parameter for recognizing vowels of the Uzbek language
    Malika Abdullaeva, Ilyos Khujayorov, Mannon Ochilov
    International Conference on Information Science and Communications Technologies Applications Trends and Opportunities Icisct 2021, 2021
  • Automatic Recognition of Uzbek Speech Based on Integrated Neural Networks
    Muhammadjon Musaev, Ilyos Khujayorov, Mannon Ochilov
    Advances in Intelligent Systems and Computing, 2021
  • USC: An Open-Source Uzbek Speech Corpus and Initial Speech Recognition Experiments
    Muhammadjon Musaev, Saida Mussakhojayeva, Ilyos Khujayorov, Yerbolat Khassanov, Mannon Ochilov, Huseyin Atakan Varol
    Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 2021
  • Development of integral model of speech recognition system for Uzbek language
    Muhammadjon Musaev, Ilyos Khujayorov, Mannon Ochilov
    14th IEEE International Conference on Application of Information and Communication Technologies Aict 2020 Proceedings, 2020
  • The Use of Neural Networks to Improve the Recognition Accuracy of Explosive and Unvoiced Phonemes in Uzbek Language
    Muhammadjon Musaev, Ilyos Khujayorov, Mannon Ochilov
    2020 Information Communication Technologies Conference Ictc 2020, 2020
  • Parallel Signal Processing Based-On Graphics Processing Units
    Ilyos Khujayorov, Manon Ochilov
    International Conference on Information Science and Communications Technologies Applications Trends and Opportunities Icisct 2019, 2019
  • Image Approach to Speech Recognition on CNN
    Muhammadjon Musaev, Ilyos Khujayorov, Mannon Ochilov
    ACM International Conference Proceeding Series, 2019

RECENT SCHOLAR PUBLICATIONS

  • Optimizing Block Size and Memory Usage for Image Processing on GPU Using CUDA
    M Rakhimov, M Ochilov, S Javliev, A Khojamurotov
    2026 International Conference on Artificial Intelligence, Computer, Data … , 2026
    2026
  • Developing A Question-Answering System In The Uzbek Language Based On The Xlm-Roberta Model
    KI Sh, MM Ochilov, OA Kholmatov
    Stanford Database Library of American Journal of Applied Science and … , 2025
    2025
  • Accelerating Matrix Multiplication with CPU Multithreading and CUDA Block-Based GPU Parallelization
    M Rakhimov, M Ochilov, R Nasimov, S Javliev
    Proceedings of the 9th International Conference on Future Networks and … , 2025
    2025
  • CUDA Block Size Optimization for Gaussian and Sobel Filters: Benchmarking Against CPU Implementations
    M Rakhimov, M Ochilov, S Javliev, R Nasimov
    Proceedings of the 9th International Conference on Future Networks and … , 2025
    2025
  • Development of Uzbek Question-Answering Models through Fine-Tuning Approaches
    I Khujayarov, M Ochilov, M Abdullayeva, O Kholmatov, R Nasimov
    Proceedings of the 9th International Conference on Future Networks and … , 2025
    2025
  • High-Performance Matrix Multiplication Using Block Parallelization on CPU and GPU
    M Rakhimov, M Ochilov, S Javliev
    2025 IEEE XVII International Scientific and Technical Conference on Actual … , 2025
    2025
  • KO ‘P TILLI AVTOMATIK NUTQNI TANISH TIZIMLARINI YARATISH MUAMMOLAR VA YECHIMLARI
    M Ochilov, O Narzullaev
    COMPUTER LINGUISTICS: PROBLEMS, SOLUTIONS, PROSPECTS 1 (1) , 2025
    2025
  • Advanced Speaker Diarization Techniques for Analyzing Uzbek Speech Signals
    K Shukurov, M Ochilov, U Khasanov, S Kholdorov, M Rakhmanova
    International IOT, Electronics and Mechatronics Conference, 615-629 , 2025
    2025
  • Restoring Punctuation in Uzbek Texts Using LLM's Fine Tuning Approaches
    M Musaev, M Ochilov, M Abdullaeva, R Nasimov
    Proceedings of the 8th International Conference on Future Networks … , 2024
    2024
    Citations: 2
  • Analysis and possibilities of parallel approach in big data processing
    M Rakhimov, M Ochilov, S Javliev, R Nasimov
    Proceedings of the 8th International Conference on Future Networks … , 2024
    2024
    Citations: 14
  • Text processing technology in Uzbek speech to sign language translation systems
    M Musaev, D Juraev, M Ochilov, M Abdullaeva
    AIP Conference Proceedings 3244 (1), 030062 , 2024
    2024
    Citations: 4
  • Ensuring Information Security in the Intelligent Scientific and Technical Information Systems
    K Kerimov, Z Azizova, F Bekkamov, M Rakhimov, M Ochilov
    ENVIRONMENT. TECHNOLOGY. RESOURCES. Proceedings of the International … , 2024
    2024
    Citations: 1
  • MАTNDАGI TINISH BELGILАRINI TIKLАSH MUАMMOLАRI VА YECHIMLАRI
    M Ochilov, D Jurayev, O Narzullayev
    COMPUTER LINGUISTICS: PROBLEMS, SOLUTIONS, PROSPECTS 1 (1) , 2024
    2024
  • Sun’iy intellekt algoritmlari asosida matn tilini avtomatik aniqlash
    I Xujayarov, M Ochilov, O Xolmatov, D Jurayev
    Международный Журнал Теоретических и Прикладных Вопросов Цифровых … , 2024
    2024
    Citations: 4
  • Development of language models for continuous Uzbek speech recognition system
    A Mukhamadiyev, M Mukhiddinov, I Khujayarov, M Ochilov, J Cho
    Sensors 23 (3), 1145 , 2023
    2023
    Citations: 64
  • Development of Language Models for Continuous Uzbek Speech Recognition System
    MA NURALIEVICH, M Mukhiddinov, I Khujayarov, M Ochilov, J Cho
    MDPI , 2023
    2023
  • Using the CTC-based Approach of the End-to-End Model in Speech Recognition
    MM Ochilov
    International Journal of Theoretical and Applied Issues of Digital … , 2023
    2023
    Citations: 3
  • НЕЙРОН ТAРМОҚНИ ЎҚИТИШНИ ТЕЗЛAШТИРИШДA MKL ПАРАЛЛЕЛЛАШТИРИШ КУТУБХОНAСИНИНГ ИМКОНИЯТИ
    ММ Очилов, ШАЎ Жавлиев
    Science and innovation 2 (Special Issue 3), 231-235 , 2023
    2023
  • Uzbek speech synthesis using deep learning algorithms
    MI Abdullaeva, DB Juraev, MM Ochilov, MF Rakhimov
    International Conference on Intelligent Human Computer Interaction, 39-50 , 2022
    2022
    Citations: 10
  • Speech recognition technologies based on artificial intelligence algorithms
    M Musaev, I Khujayarov, M Ochilov
    International Conference on Intelligent Human Computer Interaction, 51-62 , 2022
    2022
    Citations: 13

MOST CITED SCHOLAR PUBLICATIONS

  • Development of language models for continuous Uzbek speech recognition system
    A Mukhamadiyev, M Mukhiddinov, I Khujayarov, M Ochilov, J Cho
    Sensors 23 (3), 1145 , 2023
    2023
    Citations: 64
  • Image approach to speech recognition on CNN
    M Musaev, I Khujayorov, M Ochilov
    Proceedings of the 2019 3rd international symposium on computer science and … , 2019
    2019
    Citations: 49
  • USC: An open-source Uzbek speech corpus and initial speech recognition experiments
    M Musaev, S Mussakhojayeva, I Khujayorov, Y Khassanov, M Ochilov, ...
    International Conference on Speech and Computer, 437-447 , 2021
    2021
    Citations: 39
  • The use of neural networks to improve the recognition accuracy of explosive and unvoiced phonemes in Uzbek language
    M Musaev, I Khujayorov, M Ochilov
    2020 Information Communication Technologies Conference (ICTC), 231-234 , 2020
    2020
    Citations: 23
  • Development of integral model of speech recognition system for Uzbek language
    M Musaev, I Khujayorov, M Ochilov
    2020 IEEE 14th International Conference on Application of Information and … , 2020
    2020
    Citations: 20
  • Distribution of operations in heterogeneous computing systems for processing speech signals
    M Rakhimov, M Ochilov
    2021 IEEE 15th International Conference on Application of Information and … , 2021
    2021
    Citations: 19
  • Parallel signal processing based-on graphics processing units
    I Khujayorov, M Ochilov
    2019 International Conference on Information Science and Communications … , 2019
    2019
    Citations: 15
  • Analysis and possibilities of parallel approach in big data processing
    M Rakhimov, M Ochilov, S Javliev, R Nasimov
    Proceedings of the 8th International Conference on Future Networks … , 2024
    2024
    Citations: 14
  • Automatic recognition of Uzbek speech based on integrated neural networks
    M Musaev, I Khujayorov, M Ochilov
    World Conference Intelligent System for Industrial Automation, 215-223 , 2020
    2020
    Citations: 14
  • Speech recognition technologies based on artificial intelligence algorithms
    M Musaev, I Khujayarov, M Ochilov
    International Conference on Intelligent Human Computer Interaction, 51-62 , 2022
    2022
    Citations: 13
  • Uzbek speech synthesis using deep learning algorithms
    MI Abdullaeva, DB Juraev, MM Ochilov, MF Rakhimov
    International Conference on Intelligent Human Computer Interaction, 39-50 , 2022
    2022
    Citations: 10
  • Advanced feature extraction method for speaker identification using a classification algorithm
    M Musaev, M Abdullaeva, M Ochilov
    AIP Conference Proceedings 2656 (1), 020022 , 2022
    2022
    Citations: 10
  • Formant set as a main parameter for recognizing vowels of the Uzbek language
    M Abdullaeva, I Khujayorov, M Ochilov
    2021 International Conference on Information Science and Communications … , 2021
    2021
    Citations: 9
  • Text processing technology in Uzbek speech to sign language translation systems
    M Musaev, D Juraev, M Ochilov, M Abdullaeva
    AIP Conference Proceedings 3244 (1), 030062 , 2024
    2024
    Citations: 4
  • Sun’iy intellekt algoritmlari asosida matn tilini avtomatik aniqlash
    I Xujayarov, M Ochilov, O Xolmatov, D Jurayev
    Международный Журнал Теоретических и Прикладных Вопросов Цифровых … , 2024
    2024
    Citations: 4
  • Using the CTC-based Approach of the End-to-End Model in Speech Recognition
    MM Ochilov
    International Journal of Theoretical and Applied Issues of Digital … , 2023
    2023
    Citations: 3
  • Restoring Punctuation in Uzbek Texts Using LLM's Fine Tuning Approaches
    M Musaev, M Ochilov, M Abdullaeva, R Nasimov
    Proceedings of the 8th International Conference on Future Networks … , 2024
    2024
    Citations: 2
  • Ensuring Information Security in the Intelligent Scientific and Technical Information Systems
    K Kerimov, Z Azizova, F Bekkamov, M Rakhimov, M Ochilov
    ENVIRONMENT. TECHNOLOGY. RESOURCES. Proceedings of the International … , 2024
    2024
    Citations: 1
  • Optimizing Block Size and Memory Usage for Image Processing on GPU Using CUDA
    M Rakhimov, M Ochilov, S Javliev, A Khojamurotov
    2026 International Conference on Artificial Intelligence, Computer, Data … , 2026
    2026
  • Developing A Question-Answering System In The Uzbek Language Based On The Xlm-Roberta Model
    KI Sh, MM Ochilov, OA Kholmatov
    Stanford Database Library of American Journal of Applied Science and … , 2025
    2025