Management and Future Technologies SCIENTIFIC JOURNAL | № 003

October, 2024 № 003 Volume 1, Issue 3 Management and Future Technologies SCIENTIFIC JOURNAL journal.umft.uz

Jurnal sohalari: menejment, dasturiy injiniring, sun’iy intellekt texnologiyalari, kompyuter injiniringi, infokommunikatsiya injiniringi, axborot xavfsizligi, raqamli iqtisodiyot, pedagogika va psixologiya, matematika va fizika Tahririyat kengashi raisi Muharrirlar t.f.d., prof. O‘tkir Xamdamov “University of Management and Future Technologies” universiteti rektori Farhod Ahmedov (J. Koreya) Sherzod Mustafaqulov (O‘zbekiston) Akmalbek Abdusalomov (J. Koreya) Bahtiyor Akmuradov (O‘zbekiston) Sherzod Abdullayev (O‘zbekiston) Shabir Ahmad (J. Koreya) Dilshod Rahmatov (Germaniya) O.A. Hidayov (Germaniya) Akmaljon Abdullayev (O‘zbekiston) Jinsoo Cho (J. Koreya) Jamshid Elov (O‘zbekiston) Young Im Cho (J. Koreya) Fazliddin Maxmudov (J. Koreya) Alpamis Kutlimuratov (O‘zbekiston) Faheem Khan (J. Koreya) Lilia Tightiz (J. Koreya) Shahnoza Sultanova (O‘zbekiston) Safiullah Khan (Buyuk Britaniya) Seytkamal Medetov (Fransiya) Avaz Qaxxorov (O‘zbekiston) Doston Xasanov (O‘zbekiston) Sabina Umirzakova (J. Koreya) Elmurod Urinov (O‘zbekiston) Muhriddin Umarov (O‘zbekiston) Urmanov Odil (J. Koreya) Sobir Radjabov (O‘zbekiston) Bosh muharrir t.f.f.d. dot. Muhriddin Muxiddinov “University of Management and Future Technologies” universiteti Ilmiy va uslubiy ishlar bo‘yicha prorektori Tahririyat kengashi a’zolari t.f.d., prof. Xakim Zaynidinov t.f.d., prof. Muxammadjon Musayev t.f.d., prof. Shavkat Fozilov f-m.f.d. prof. Aripov Mersaid f-m.f.d. prof. Аloev Raxmatillo t.f.d., prof. Marat Raxmatullayev t.f.d., prof. Dilnoz Muxamediyeva i.f.d., prof. Toxir Hasanov i.f.d., prof. To‘lqin Boboqulov t.f.d., prof. Jamshid Sultonov f-m.f.d. prof. Dildora Muhamediyeva i.f.d., prof. Sherzod Rajabov p.f.d., prof. Qurbonniyoz Panjiyev p.f.d., prof. Muhabbat Hakimova t.f.d., prof. Nargiza Usmanova p.f.d., prof. Nishonboy Kiyamov p.f.d., prof. Qutlug‘bek Qodirov t.f.d., dot. Halimjon Xujamatov t.f.d., dot. Ibragim Atadjanov Dizayner Eshmetov Daler Jurnal O‘zbekiston Respublikasi Prezidenti Administratsiyasi huzuridagi Axborot va ommaviy kommunikatsiyalar agentligidan 212842-raqamli guvohnoma bilan 26.01.2024 sanasida ro‘yxatdan o‘tkazilgan.

MUNDARIJA TEXNIKA FANLARI INTRUSION DETECTION FOR IOT EVCS: INTEGRATING 6 NEXT–GENERATION CNN, LSTM, AND GRU MODELS Dusmurod Kilichev METHODS OF MATHEMATICAL MODELING IN BIOMEDICAL 12 MODERN RESEARCH Beknazarova Saida, Khalikova Nasiba OF SMART GLASSES SYSTEM FOR BLIND AND VISUALLY 20 ANALYSIS IMPAIRED PEOPLE Mukhriddin Mukhiddinov, Oybek Djuraev, Jasurbek Djuraev QURILMALARDAGI MA’LUMOTLARNI CLOUDDA XAVFSIZ SAQLASH 27 MOBIL ALGORITMI Odilbek Asqaraliyev, Samandar Yusupov, Sirojiddin Xalilov TIZIMLARI DASTURIY TA’MINOTINING 35 AXBOROT-BOSHQARUV ISHONCHLILIGINI BAHOLASH MODЕL Maqsud Mardiyev, Sirojiddin Xalilov, Farangiza Omonullayeva OBYEKTINING ELEKTR TA’MINOTI TIZIMINI 42 TELEKOMMUNIKATSIYA MORFOLOGIK TAHLIL ORQALI MODELASHTIRISH Qodirov Fazliddin Misliddinovich

TANIB OLISHDA BELGILAR FAZOSINI QISQARTIRISH 55 TIMSOLLARNI USULLARINING QIYOSIY TAHLILI Rashidov X.Sh., Xandamov Y.X., Inomxo‘jayev A.A. TARMOQLAR: ARXITEKTURASI, MOBILLIK MODELLARI VA 65 OPPORTUNISTIK MARSHRUTLASH PROTOKOLLARI Elyanora Jolimbetova ВОССТАНОВЛЕНИЯ ГЕОЛОГИЧЕСКИХ СИГНАЛОВ С 72 АЛГОРИТМЫ ПОМОЩЬЮ СПЛАЙНОВ Гофуржонов Мухаммадали, Гофуржонова Маржона KOMMUNIKATSIYA VA RAQAMLI TEXNOLOGIYALARDA 80 AXBOROT AXBOROT ALMASHINUVINI TA’MINLASHDA QUYOSH VA SHAMOL ENERGIYASIDAN FOYDALANISH ISTIQBOLLARI Avazbek Madolimov 88 RAQAMLI TEXNOLOGIYALAR VA AXBOROT XAVFSIZLIGINI TA’MINLASHDA SUN’IY YO‘LDOSH ALOQA TIZIMINI QO‘LLASH USULLARI VA ALGORITMLARI Rustam Davronov HUDUDIDA QO‘LLANILADIGAN YO‘L BELGILARI 99 O‘ZBEKISTON MA’LUMOTLAR TO‘PLAMINI SHAKLLANTIRISH Umarov MuhriddiN, Rayimova Shaxnoza IJTIMOIY FANLAR 113 DIGITAL MARKETING COMMUNICATION TOOLS ANALYSIS Dadabayeva Rano, Abdulakhatov Muzaffarjon, Abdullaeva Iroda BLOKCHEYN TEXNOLOGIYALARI BOZORI DINAMIKASINING 124 JAHON PROGNOZI Azimov Dilmurod

130 РАЗВИТИЕ ЦИФРОВОЙ ЭКОНОМИКИ В УЗБЕКИСТАНЕ Юсупова Дильбар 137 KOMMUNAL SOHADA BOSHQARUV, HISOBINI YURITUVCHI TIZIMNI MODELLASHTIRISH VA ISHLAB CHIQISH Mirkarimov Akmal, Ro’ziev Faxriddin, Xasanov Ahmadjon TASHKILOTLARIDA MAHSULOT HISOBINI YURITISH JARAYONLARI 144 SAVDO AXBOROT MODELLARINING O‘RNI VA AHAMIYATI Elov Jamshid, Maxmudov Abduxalil KICHIK VA O‘RTA BIZNES SOHA VAKILLARINI SOLIQQA 155 O‘ZBEKISTONDA TORTISH TIZIMI Abdibositova Muxlisaxon 164 OBJECTIVES, TYPES AND METHODOLOGY OF CRM SYSTEM SELECTION Abdullayev Munis, Abdulakhatov Muzaffarjon MATEMATIKA VA FIZIKA FANLARI ОДНОМ АЛГОРИТМЕ ЧИСЛЕННОГО РЕШЕНИЯ КРАЕВЫХ ЗАДАЧ 172 ОБ УПРУГО-ПЛАСТИЧНОСТИ U.Z. Djumayozov ПРИМЕНЕНИЯ ЗАКОНОВ РАСПРЕДЕЛЕНИЯ В ОБСЛУЖИВАНИИ 180 РОЛЬ РАЗНОРОДНОГО ТРАФИКА В СЕТЯХ 5G Байрам Турумбетов, Нилуфархон Аскарова

Volume 1, Issue 3 October 2024 6 Author Dusmurod Kilichev Department of Computer Engineering, Gachon University, Seongnam 13120, Republic of Korea; dusmurod@gachon.ac.kr Management and Future Technologies journal.umft.uz NEXT–GENERATION INTRUSION DETECTION FOR IOT EVCS: INTEGRATING CNN, LSTM, AND GRU MODELS A bstract: In the evolving landscape of Internet of Things (IoT) and Industrial IoT (IIoT) security, novel and efficient intrusion detection systems (IDSs) are paramount. In this article, we present a groundbreaking approach to intrusion detection for IoT-based electric vehicle charging stations (EVCS), integrating the robust capabilities of convolutional neural network (CNN), long short-term memory (LSTM), and gated recurrent unit (GRU) models. The proposed framework leverages a comprehensive realworld cybersecurity dataset, specifically tailored for IoT and IIoT applications, to address the intricate challenges faced by IoT-based EVCS. The ensemble algorithm, accessible via GitHub, represents a significant stride in fortifying IoT-based EVCS against a diverse array of cybersecurity threats. Keywords: cybersecurity, electric vehicle charging station (EVCS), ensemble learning; Internet of Things (IoT), intrusion detection system (IDS). Copyright: © 2024 by the authors. Ushbu maqola Creative Commons Attribution (CC BY) litsenziyasi shartlari asosida tarqatiladigan ochiq foydalanish maqolasi hisoblanadi (https://creativecommons.org/ licenses/by/4.0/).

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Management and Future Technologies journal.umft.uz October 2024 Volume 1, Issue 3 References 1. Rimal, B.P.; Kong, C.; Poudel, B.; Wang, Y.; Shahi, P. Smart Electric Vehicle Charging in the Era of Internet of Vehicles, Emerging Trends, and Open Issues. Energies 2022, 15, 1908. 2. Aldaej, A.; Ahanger, T.A.; Ullah, I. Deep Learning-Inspired IoT-IDS Mechanism for Edge Computing Environments. Sensors 2023, 23, 9869. 3. Kilichev, D.; Kim, W. Hyperparameter Optimization for 1D-CNN-Based Network Intrusion Detection Using GA and PSO. Mathematics 2023, 11, 3724. 4. Rashid, M.M.; Khan, S.U.; Eusufzai, F.; Redwan, M.A.; Sabuj, S.R.; Elsharief, M. A Federated Learning-Based Approach for Improving Intrusion Detection in Industrial Internet of Things Networks. Network 2023, 3, 158–179. 5. Ferrag, M.A.; Friha, O.; Hamouda, D.; Maglaras, L.; Janicke, H. Edge-IIoTset: A New Comprehensive Realistic Cyber Security Dataset of IoT and IIoT Applications for Centralized and Federated Learning. IEEE Access 2022, 10, 40281–40306. 6. Tareq, I.; Elbagoury, B.M.; El-Regaily, S.; El-Horbaty, E.S.M. Analysis of ToN-IoT, UNW-NB15, and Edge-IIoT Datasets Using DL in Cybersecurity for IoT. Appl. Sci. 2022, 12, 9572. 7. Khacha, A.; Saadouni, R.; Harbi, Y.; Aliouat, Z. Hybrid Deep Learning-based Intrusion Detection System for Industrial Internet of Things. In Proceedings of the 2022 5th International Symposium on Informatics and its Applications (ISIA), M’sila, Algeria, 29–30 November 2022; pp. 1–6. 8. Tomar, K.; Bisht, K.; Joshi, K.; Katarya, R. Cyber Attack Detection in IoT using Deep Learning Techniques. In Proceedings of the 2023 6th International Conference on Information Systems and Computer Networks (ISCON), Mathura, India, 3–4 March 2023; pp. 1–6. 9. Ding, W.; Abdel-Basset, M.; Mohamed, R. DeepAK-IoT: An effective deep learning model for cyberattack detection in IoT networks. Inf. Sci. 2023, 634, 157–171. 10. Koppula, M.; LM, L.J. LNKDSEA: Machine Learning Based IoT/IIoT Attack Detection Method. In Proceedings of the 2023 International Conference on Advances in Electronics, Communication, Computing and Intelligent Information Systems (ICAECIS), Bengaluru, India, 19–21 April 2023; pp. 655–662. 11. Salih, K.M.M.; Ibrahim, N.B. Enhancing IoT Forensics through Deep Learning: Investigating Cyber-Attacks and Analyzing Big Data for Improved Security Measures. In Proceedings of the 2023 4th International Conference on Big Data Analytics and Practices (IBDAP), Bangkok, Thailand, 25–27 August 2023; pp. 1–8. 12. Ullah, S.; Boulila, W.; Koubâa, A.; Ahmad, J. MAGRU-IDS: A Multi-Head Attention-Based Gated Recurrent Unit for Intrusion Detection in IIoT Networks. IEEE Access 2023, 11, 114590– 114601. 11

Volume 1, Issue 3 October 2024 12 Author Beknazarova Saida Department of Television and Media Technology, Tashkent University of Information Technology named after Muhammad al-Khorezmi, Tashkent, 100200, Uzbekistan; saidabeknazarova1992@gmail.com Khalikova Nasiba Department of Communication and Digital Technologies, “University of Management and Future Technologies”, Tashkent, 100208, Uzbekistan; nasibaxalikova94@gmail. com Copyright: © 2024 by the authors. Ushbu maqola Creative Commons Attribution (CC BY) litsenziyasi shartlari asosida tarqatiladigan ochiq foydalanish maqolasi hisoblanadi (https://creativecommons.org/ licenses/by/4.0/). Management and Future Technologies journal.umft.uz MODERN METHODS OF MATHEMATICAL MODELING IN BIOMEDICAL RESEARCH A bstract: The subject of the study. The main approaches to the mathematical modeling of the interaction of optical radiation with biological tissues are investigated. Method. The Monte Carlo method is an approximation of the solution of the radiation transfer equation. This solution is achieved by sampling a set of possible trajectories of light quanta (photon packets) as they pass through the fabric. The resulting stochastic model allows us to simulate the propagation of light in a cloudy (scattering) medium. The paper considers the main types of interaction of photons with tissue: scattering, absorption and reflection/ refraction at the boundary of the medium. The main results. Effective methods for modeling the problem of Raman spectroscopy in turbid media are shown, taking into account the detector parameters and sample size.The reconstructed Raman spectrum of the skin is compared with clinically measured skin spectra in vivo. The general correspondence of the simulated process and experimental data is shown. Keywords: optical radiation, absorption, scattering, modeling, biological tissues, Monte Carlo method.

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Management and Future Technologies journal.umft.uz October 2024 Volume 1, Issue 3 References 1. Тучин В.В. Оптика биологических тканей. Методы рассеяния света в медицинской диагностике. М.: Физматлит, 2012. 812 с. 2 2. Arridge S.R. Optical tomography: forward and inverse problems // Inverse Problems. 2009. V. 25. N 12. P. 123010. https://doi. org/10.1088/0266-5611/25/12/123010 3. Bassi A., D’Andrea C., Valentini G., Cubeddu R., Arridge S.R. Temporal propagation of spatial information in turbid media // Optics Letters. 2008. V. 33. N 23. P. 2836–2838. https://doi. org/10.1364/ OL.33.002836 4. Meglinski I., Doronin A.V. Monte Carlo modeling of photon migration for the needs of biomedical optics and biophotonics // Advanced Biophotonics. 2012. P. 1–72. 5. Fischer D.G., Prahl S.A., Duncan D.D. Monte Carlo modeling of spatial coherence: free-space diffraction // Journal of the Optical Society of America A. 2008. V. 25. N 10. P. 2571–2581. https://doi. org/10.1364/JOSAA.25.002571 6. Jacques S. Monte Carlo modeling of light transport in tissue // Optical-thermal response of laser-irradiated tissue. Springer Netherlands, 2010. P. 109–144. https://doi.org/10.1007/97890-481- 8831-4_5 7. Flock S.T., Patterson M.S., Wilson B.C., Wyman D.R. Monte Carlo modeling of light propagation in highly scattering tissues. I. Model predictions and comparison with diffusion theory // IEEE Transactions on Biomedical Engineering. 1989. V. 36. N 12. P. 1162–1168. https:// doi. org/10.1109/tbme.1989.1173624 8. Flock S.T., Wilson B.C., Patterson M.S. Monte Carlo modeling of light propagation in highly scattering tissues. II. Comparison with measurements in phantoms // IEEE Transactions on Biomedical Engineering. 1989. V. 36. N 12. P. 1169–1173. https://doi. org/10.1109/10.42107 9. Alerstam E., Lo W., Han T., Rose J., Andersson-Engels S., Lilge L. Next-generation acceleration and code optimization for light transport in turbid media using GPUs // Biomedical Optics Express. 2010. V. 1. N 2. P. 658–675. https://doi.org/10.1364/boe.1.000658 10. Li P., Liu C., Li X., He H., Ma H. GPU acceleration of Monte Carlo simulations for polarized photon scattering in anisotropic turbid media // Applied Optics. 2016. V. 55. N 27. P. 7468– 7476. https://doi. org/10.1364/ao.55.007468 11. Doronin A., Meglinski I. Peer-to-peer Monte Carlo simulation of photon migration in topical applications of biomedical optics // Journal of Biomedical Optics. 2012. V. 17. N 9. P. 090504. https://doi. org/10.1117/1.jbo.17.9.090504 12. Korhonen V.O., Myllyla T.S., Kirillin M.Yu., Popov A.P., Bykov A.V., Gorshkov A.V., Sergeeva E.A., Kinnunen M., Kiviniemi V. Light propagation in NIR spectroscopy of the human brain // IEEE Journal of Selected Topics in Quantum Electronics. 2014. V. 20. N 2. P. 7100310. https:// doi.org/10.1109/jstqe.2013.2279313 13. Wang L.V., Wu H.-I. Biomedical Optics: Principles and Imaging. Hoboken, NJ, USA 13. Wang L.V., Wu H.-I. Biomedical Optics: Principles and Imaging. Hoboken, NJ, USA: Wiley, 2009. 376 p. 14. Yun S.H., Kwok S.J. Light in diagnosis, therapy and surgery // Nature Biomedical Engineering. 2017. V. 1. N 1. P. 0008. https://doi. org/10.1038/s41551-016-0008 19

Volume 1, Issue 3 October 2024 20 Authors Mukhriddin Mukhiddinov Department of Communication and Digital Technologies, “University of Management and Future Technologies”, Tashkent, 100208, Uzbekistan; mukhiddinov@umft.uz Oybek Djuraev Department of Communication and Digital Technologies, “University of Management and Future Technologies”, Tashkent, 100208, Uzbekistan; djurayev@umft.uz Jasurbek Djuraev Department of Software Engineering, Tashkent University of Information Technology named after Muhammad al-Khorezmi, Tashkent, 100200, Uzbekistan; Copyright: © 2024 by the authors. Ushbu maqola Creative Commons Attribution (CC BY) litsenziyasi shartlari asosida tarqatiladigan ochiq foydalanish maqolasi hisoblanadi (https://creativecommons.org/ licenses/by/4.0/). Management and Future Technologies journal.umft.uz ANALYSIS OF SMART GLASSES SYSTEM FOR BLIND AND VISUALLY IMPAIRED PEOPLE A bstract: In recent decades, advancing navigation and routing technologies has presented a formidable challenge for researchers aiming to devise sophisticated and intelligent guidance systems for individuals with visual impairments, both indoors and outdoors. Individuals suffering from visual impairments and blindness encounter difficulties in moving independently and overcoming various problems in their routine lives. As a solution, artificial intelligence and computer vision approaches facilitate blind and visually impaired (BVI) people in fulfilling their primary activities without much dependency on other people. Smart glasses are a potential assistive technology for BVI people to aid in individual travel and provide social comfort and safety. However, practically, the BVI are unable move alone, particularly in dark scenes and at night. In this analysis, we examine a technologically advanced eyewear system designed for individuals with visual impairments (BVIPs), which integrates computer vision methodologies and deep learning algorithms, alongside auditory cues and tactile representations, to enhance autonomous navigation within nocturnal settings. Keywords: smart glasses; artificial intelligence; blind and visually impaired; deep learning; low-light images; assistive technologies; object detection; refreshable tactile display.