Management and Future Technologies | Volume 1, Issue 1 | № 001

April, 2024 № 001 Volume 1, Issue 1 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 TA’LIMNI BOSHQARISH VA MONITORINGINI YURITISH 6 OLIY JARAYONLARINI RAQAMLASHTIRISH MASALASI VA ISTIQBOLLARI Utkir Xamdamov, Alisher Abdullaev, Xurshid Sultonov, Shaxrizoda Raxmatillaeva ROLE AND PROSPECTS OF DIGITAL AND INNOVATIVE TECHNOLOGIES 12 THE IN EDUCATION FOR THE VIZUALLY IMPAIRED PEOPLE Mukhriddin Mukhiddinov, Oybek Djuraev, Jasurbek Djuraev, Shakhrizoda Rakhmatillaeva HOSSALARIGA ASOSLANGAN MASOFAVIY TA’LIM 17 VIDEOMONITORING JARAYONLARINI TAKOMILLAShTIRISHNING AXBOROT MODELI Sirojiddin Xalilov, Farangiza Omonullaeva, Maftuna Mannapova 23 ELEKTRON SO‘ZLARNI VEKTORLI IFODALASH TEXNOLOGIYASI Baxtiyor Akmuradov COMPARATIVE ANALYSIS OF TRADITIONAL AND DEEP LEARNING-BASED 28 AAPPROACHES FOR FACE DETECTION: PERFORMANCE, ADVANCEMENTS, AND CHALLENGES Elmurod Urinov, Nurbek Kholmurodov MATNDAN NUTQ SIZTEZLASH DASTURIY TIZIMI UCHUN 32 O‘ZBEKCHA DIFONLAR BAZASININING QURILISHI Farrux Axmedov, Muhriddin Umarov 37 NUTQNI TANIB OLISHNI AMALGA OSHIRISH BOSQICHLARI O‘rinboy Naziraliyev

TARMOQLARDA BUYURTMALARNI TUSHISH VA 43 INFOKOMMUNIKATSIYA ULARGA XIZMAT KO’RSATISH JARAYONLARINI MODELLASHTIRISH A.F.Izbasarov, Z.Z. Nigmatov, A.A.Yaxyoyev IJTIMOIY FANLAR 50 BLOKCHEYN TEXNOLOGIYASINING QO`LLANILISHI VA NAZARIY JIHATLARI Sherzod Abduraimov IQTISODIYOTINING RAQAMLI TRANSFORMATSIYASI: 59 JAHON TENDENSIYALAR VA ISTIQBOLLARI Djangirova Umida Abdugalimovna 65 THE APPLICATION OF DIGITAL MARKETING STRATEGIES Abdulakhatov Muzaffarjon EFFECTS OF DIGITAL MARKETING ON A COMPANY’S OVERALL 74 THE COMMERCIAL GROWTH IN UZBEKISTAN Dadabayeva Ra’no ROLE AND IMPORTANCE OF THE DIGITAL ECONOMY IN THE LABOR 82 THE MARKET Ulugbek Narmanov, Guzal Abdishukurov 88 RAQAMLI IQTISODIYOTNI RIVOJLANTIRISHNING XORIJIY TAJRIBALARI Lutfiy Farangiz Sharidin qizi, Ismoilova Mohigul Yusubjon qizi. HARBIY TA’LIM MUASSASALARIDA RAQAMLI PEDAGOGIKANING 95 OLIY O‘RNI VA AHAMIYATI Izbasarov A.F., Abdurayimov S.S., Nigmatov Z.Z.

MUHANDIS-PEDAGOGLARNI LOYIHALASH 101 BO‘LAJAK KOMPETENSIYALARINI KOMPYUTER GRAFIKASI VOSITASIDA RIVOJLANTIRISH MODELI Kayumova Kamola, Dilshod Mansurov TA’LIM OLUVCHILAR FAOLIYATINI INTEGRALLASHGAN 107 MASOFAVIY MONITORINGINI OLIB BORISH KO‘RSATKICHLARI Odil Asqaraliyev, Dilshod Mansurov, Kayumova Kamola MATEMATIKA VA FIZIKA FANLARI O‘QITISHDA “PhET – INTERACTIVE SIMULATIONS” DASTURIDAN 114 FIZIKANI FOYDALANISH Asror Ulashov, Umida Naziraliyeva, Rahmatillayeva Shahrizoda 121 TERMOELEKTRIK HODISALAR AFZALLIK VA KAMCHILIKLARINI O‘RGANISH Umida Naziraliyeva, Asror Ulashov DARSLARIDA ZAMONAVIY TEXNOLOGIYALARDAN FOYDALANIB 128 FIZIKA TALABALAR BILIMLARINI BAHOLASH Ya.A. Axmedov, X.N. Karimov (MATEMATIKA) FANINI O‘RGANISHDA ONLINE TIZIMLARNING 136 HISOB IMKONIYATLARI Quvonchbek Asadov, Xonxo‘jayev Lazizxon

Volume 1, Issue 1 April 2024 6 Mualliflar Utkir Xamdamov Kommunikatsiya va raqamli texnologiyalar kafedrasi, “University of management and future technologies” universiteti, Toshkent, 100208, O‘zbekiston; utkir.hamdamov@mail.ru Alisher Abdullaev Oliy ta’lim, fan va innovatsiyalar vazirligi, Toshkent, O‘zbekiston; ali1914@gmail.com Xurshid Sultonov Oliy ta’lim, fan va innovatsiyalar vazirligi, Toshkent, O‘zbekiston; Shaxrizoda Raxmatillaeva Kommunikatsiya va raqamli texnologiyalar kafedrasi, “University of management and future technologies” universiteti, Toshkent, 100208, O‘zbekiston; Mas’ul: utkir.hamdamov@mail.ru Management and Future Technologies journal.umft.uz OLIY TA’LIMNI BOSHQARISH VA MONITORINGINI YURITISH JARAYONLARINI RAQAMLASHTIRISH MASALASI VA ISTIQBOLLARI A nnotatsiya: Maqola oliy ta’lim muassasalari faoliyatini raqamlashtirish orqali oliy ta’lim jarayonlarini modellashtirish, boshqarish va axborot tizimini ishlab chiqish hamda joriy etish masalalariga bag‘ishlangan. Maqolada oliy ta’lim muassasalarining ma’muriy, akademik, ilmiy-uslubiy, moliyaviy-xo‘jalik faoliyatlarini avtomatlashtirish, oliy ta’lim muassasalari bilan vazirlik o‘rtasida axborot almashinuvini yo‘lga qo‘yish hamda oliy ta’lim faoliyati qatnashchilari uchun elektron ta’lim xizmatlarini ko‘rsatish masalalari ko‘rilgan. Kalit so‘zlar: oliy ta’lim muassasasi, axborot tizimi, modellashtirish, akademik faoliyat, ilmiy-uslubiy faoliyat, avtomatlashtirish, raqamlashtirish, elektron ta’lim xizmatlari. 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 THE PROBLEM AND PROSPECTS OF DIGITALIZING PROCESSES OF MANAGEMENT AND MONITORING OF HIGHER EDUCATION A bstract: The article is committed to issues of modeling, management, information system development, and implementation of higher education processes through the digitalization of higher education institutions. The article deals with the issues of automating the administrative, academic, scientific-methodical, and financial-economic activities of higher education institutions, establishing information exchange between higher education institutions and the ministry, and providing electronic education services for participants of higher education activities. Keywords: higher education institution, information system, modeling, academic activity, scientific-methodical activity, automation, digitization, electronic education services. April 2024 Volume 1, Issue 1 7 Authors Utkir Khamdamov Department of Communication and Digital Technologies, “University of Management and Future Technologies”, Tashkent, 100208, Uzbekistan; utkir.hamdamov@mail.ru Alisher Abdullaev Ministry of Higher Education, Science and Innovation, Tashkent, Uzbekistan; Khurshid Sultanov Ministry of Higher Education, Science and Innovation, Tashkent, Uzbekistan; Shahrizoda Rakhmatillaeva Department of Communication and Digital Technologies, “University of Management and Future Technologies”, Tashkent, 100208, Uzbekistan; Correspondence: utkir.hamdamov@ mail.ru Copyright: © 2024 by the authors. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/ by/4.0/).

Volume 1, Issue 1 8 April 2024 Management and Future Technologies journal.umft.uz Kirish Oliy ta’lim jarayonlarini boshqarish axborot tizimini ishlab chiqish hamda uni oliy ta’lim muassasalariga samarali tadbiq etish masalasi bilan bog‘liq asosiy vazifalar O‘zbekiston Respublikasi Prezidentining 2017 yil 22 martdagi PQ-2848-son qarori, 2020 yil 5 oktabrdagi “Raqamli O‘zbekiston - 2030” strategiyasini tasdiqlash va uni samarali amalga oshirish chora-tadbirlari to‘g‘risida”gi PF6079-son Farmoni, O‘zbekiston Respublikasi Prezidenti Sh.M.Mirziyoev rahbarligida 2021 yil 16 iyun kuni “Oliy ta’lim sohasidagi islohotlar natijadorligi hamda yangi o‘quv yiliga tayyorgarlik jarayonlari to‘g‘risida” o‘tkazilgan videoselektor yig‘ilishining 34-sonli bayoni, O‘zbekiston Respublikasi Oliy va o‘rta maxsus ta’lim vazirining 2020 yil 13 oktabrdagi Oliy ta’lim muassasalarida oliy ta’lim jarayonlarini boshqarish axborot tizimini joriy etish to‘g‘risidagi 535-son buyrug‘i hamda 2021 yil 26 avgustdagi Oliy ta’lim muassasalarida “Oliy ta’lim jarayonlarini boshqarish - HEMIS” axborot tizimini joriy etish to‘g‘risidagi 372-son buyrug‘i bilan belgilab berilgan. Oliy ta’lim jarayonlarini raqamlashtirish va boshqarish axborot tizimini ishlab chiqishdan asosiy ko‘zlangan maqsad vazirlik va oliy ta’lim muassasalari o‘rtasida administrativ, akademik, ilmiy, va moliyaviy-xo‘jalik axborotlarini almashish uchun yagona axborot muhitini yaratish; oliy ta’lim muassasalarining administrativ, akademik, ilmiy va moliyaviy - xo‘jalik axborotlarini yig‘ish, ishlov berish va taqdim qilish jarayonlarini avtomatlashtirish; oliy ta’lim ishtirokchilari va davlat boshqaruv organlariga axborot xizmatlari ko‘rsatish va analitik ma’lumotlarni taqdim etish; oliy ta’lim sohasida boshqarish bo‘yicha qarorlar qabul qilinishini qo‘llab-quvvatlash va monitoring qilish; oliy ta’lim muassasalarining alohida faoliyat yo‘nalishlari bo‘yicha turli ko‘rinishdagi hisobotlarni shakllantirish va taqdim etishni tashkil etishdan iborat hisoblanadi [1-2]. Bugungi kunda oliy ta’lim jarayonlarini boshqarish axborot tizimi muvaffaqiyatli ishlab chiqilib jami 221 ta oliy ta’lim muassasalariga (OTM) joriy etilgan jumladan, 120 ta (5 tasi harbiy OTM) Davlat OTMlariga, 25 ta xorijiy OTM filiallariga hamda 76 ta nodavlat OTMlarga joriy etilgan. Hozirda axborot tizimining veb texnologiyalariga asoslangan platformasidan tashqari axborot tizimining mobil ilovasi ishlab chiqilib “GooglePlay” (550 mingdan ortiq yuklab olishlar) hamda “AppStore” (64 mingdan ortiq yuklab olishlar) mobil ilovalar do‘konlariga joylashtirilgan hamda 600 mingdan ortiq foydalanuvchi foydalanib kelmoqda. Shuningdek, talabalarga qulaylik yaratish maqsadida Hemisedu_bot telegram boti ishlab chiqilib 150 mingdan ortiq foydalanuvchini qamrab olgan. Bundan tashqari statistik ma’lumotlarni shakllantirish va tahlil etish hamda tegishli axborot is’temolchilariga taqdim etish maqsadida statistika dashbordi ham joriy etilgan. 1. HEMIS Axborot tizimini ishlab chiqish bosqichlari Oliy ta’lim jarayonlarini boshqarish axborot tizimi 4 ta quyi tizimni o‘z ichiga olib har biri quyidagi ketma-ketlikda bosqichma-bosqich ishlab chiqilgan va oliy ta’lim muassasalariag joriy etilgan [3-4]: • axborot tizimining “o‘quv jarayonini boshqarish” qismida oliy ta’lim muassasalarining akademik faoliyati, xususan, o‘quv me’yoriy hujjatlari, talabalar kontingenti, talabaning davomat hisoboti, o‘zlashtirish qaydnomasi va reyting daftarchasi, shartnoma va diplom hujjatlari shuningdek, guruh jurnali, dars va imtihon jadvallarini raqamlashtirish amalga oshirilgan; • axborot tizimining «Ilmiy jarayonni boshqarish» qismida professor-o‘qituvchilarning ilmiy loyihalari, maqolalari, ilmiy-uslubiy nashrlari, ilmiy izlanuvchilar to‘g‘risidagi ma’lumotlar bazasi shakllantirish hamda professor-o‘qituvchilar va kafedralarning ilmiy salohiyatini baholash masalalari avtomatlashtirilgan;

Management and Future Technologies journal.umft.uz April 2024 Volume 1, Issue 1 • axborot tizimining «Ma’muriy jarayonni boshqarish» qismida oliy ta’lim muassasasining tuzilmasi, boshqaruv kadrlari, professor-o‘qituvchilar tarkibi va talabalar to‘g‘risidagi ma’lumotlarni yuritish hamda tegishli hisobotlarni tayyorlashni raqamlashtirish amalga oshirilgan; • axborot tizimining «Moliya-xo‘jalik faoliyatini boshqarish» qismida oliy ta’lim muassasasi auditoriya fondi va AKT jihozlarinig bazasi shakllantiriladi, talabalarning to‘lov-shartnomalari va hisob-fakturalari elektron ko‘rinishda taqdim etiladi, stipendiya to‘lovlari to‘g‘risidagi ma’lumotlar tizim orqali amalga oshirish belgilangan. 2. HEMIS axborot tizimining boshqa tizimlar bilan integratsiyasi. HEMIS axborot tizimi orqali boshqa davlat idoralari va tashkilotlarining axborot tizimlariga elektron hukumatning idoralararo integratsion platformasi orqali oliy ta’lim muassasalari talabalari va professor-o‘qituvchilari hamda OTMning o‘quv faoliyatiga tegishli hujjatlar to‘g‘risidagi ma’lumotlar uzatiladi. Jumladan, Axborot tizimi asosida shakllantirilgan ma’lumotlar bazalari Iqtisodiyot va moliya vazirligining “Yagona ijtimoiy himoya reestri”, “UzASBO”, “Ta’lim krediti”, “Pensiya jamg‘armasi”, Markaziy bankning “MUNIS kliring tizimi”, Toshkent shahar hokimligining “Geoaxborot tizimi”, Hisob palatasining “Masofaviy nazorat” axborot tizimi, Aloqabank AT bankining “Zoomrad mobil ilovasi”, “Universal talaba kartasi”, Transport vazirligining “O‘zbekiston temir yo‘llari”, “O‘zbekiston havo yo‘llari”, Soliq qo‘mitasining “Ijara” tizimi, Markaziy saylov komissiyasining axborot tizimi, Bilim va malakalarni baholash agentligining “Virtual markaz interaktiv xizmatlar portali”, Yoshlar ishlari agentligining “Yoshlar daftari”, Kambag‘allikni qisqartirish va bandlik vazirligining “Yagona milliy mehnat tizimi”, Maktabgacha va maktab ta’limi vazirligining “attestat.uzedu.uz” axborot tizimiga integratsiya qilingan. VEWQ%\FSVSXXM^MQMRMRKVSPPEVMWXVYOXYVEWM Декан Ўқув бўлими Ходимлар бўлими Кафедра мудири Супер администрато р Докторантура Ўқитувчи Маркетинг Илмий бўлим 1-rasm. Axborot tizimining rollari strukturasi. ,)1-7E\FSVSXXM^MQMRMRKWEQEVEHSVPMOko‘rsatkichlari %\FSVSX XM^MQMRM NSVM] IXMWL REXMNEWMHE UY]MHEKM WEQEVEHSVPMO ZE MQOSRM]EXPEVKE IVMWLMPHM - Ma’lumotlarniWLEOPPERXMVMWL ZE YPEVRM UE]XE MWLPEWL MQOSRM]EXM ]EVEXMPHM ZE HER SVXMUXYVPMLMWSFSXPEVZEWXEXMWXMOma’lumotlarIPIOXVSRko‘rinishgao‘tkazildi - 8EPEFE o‘qish NS]MHER ma’lumotnoma XVERWOVMTX VI]XMRK HEJXEVGLE HMTPSQ ma’lumotlarWLEVXRSQEma’lumotlariVEUEQPEWLXMVMPHM - :E^MVPMO ZE MHSVEPEVRMRK NEQM XE E\FSVSX XM^MQM FMPER o‘zaro ma’lumot EPQEWLMRMWLM XEWLOMP IXMPMF ]EKSRE MRXIVEOXMZ HEZPEX \M^QEXPEVM TSVXEPMHE Q]KSZY^ XE 9

Volume 1, Issue 1 10 April 2024 Management and Future Technologies journal.umft.uz Aholi va tadbirkorlik sub’ektlariga elektron ko‘rinishdagi xizmatlarni ko‘rsatish ko‘lamini kengaytirish hamda yagona yondashuvni ta’minlash maqsadida HEMIS axborot tizimi va Yagona interaktiv davlat xizmatlari portali (my.gov.uz) o‘rtasida doimiy ma’lumot almashish jarayonlari amalga oshirilmoqda. Bundan tashqari HEMIS axborot tizimi Vazirlikning ichki axborot tizimlari bilan integratsiya qilinishi natijasida ikkinchi mutaxassislikka ariza topshirish, shartnoma olish, magistraturaga kirish uchun hamda o‘qishni ko‘chirish yoki tiklash uchun ariza yuborish imkoniyatlari yaratildi. 3. HEMIS axborot tizimi foydalanuvchilarini rolli boshqaruv mexanizmi. HEMIS axborot tizimida jarayonlarni tashkil etish va boshqarishda foydalanuvchilar toifalari va rollarga murojaat qilingan. Rollar o‘z navbatida axborot tizimini shakllantirish va undan foydalanishda yuqori samaradorlikni taqdim etadi. HEMIS axborot tizimida quyidagiga 17 turdagi rollar mavjud: Super administrator, Dekan, Kafedra mudiri, o‘qituvchi, o‘quv bo‘limi, Marketing bo‘limi, Kadrlar bo‘limi, Doktorantura bo‘limi, Ilmiy bo‘lim, Buxgalteriya, Kichik admin, Kutubxona, Moliya-nazorati, Raxbariyat, Registrator ofis, Tyutor, Yotoqxona mas’uli 4. HEMIS axborot tizimining samaradorlik ko‘rsatkichlari. Axborot tizimini joriy etish natijasida quyidagi samaradorlik va imkoniyatlarga erishildi: • Ma’lumotlarni shakllantirish va ularni qayta ishlash imkoniyati yaratildi va 35 dan ortiq turli hisobotlar va statistik ma’lumotlar elektron ko‘rinishga o‘tkazildi; • Talaba o‘qish joyidan ma’lumotnoma, transkript, reyting daftarcha, diplom ma’lumotlar, shartnoma ma’lumotlari raqamlashtirildi; • Vazirlik va idoralarning jami 22 ta axborot tizimi bilan o‘zaro ma’lumot almashinishi tashkil etilib yagona interaktiv davlat xizmatlari portalida (my.gov.uz) 9 ta davlat xizmati ishga tushirildi; • Talabalar kontingentining hisobi va monitoringini yuritish (fakultetlar, ta’lim yo‘nalishlari, mutaxassislik va akademik guruhlar kesimida); • Professor-o‘qituvchilar kontingentining hisobi va monitoringini yuritish (fakultetlar, kafedralar va fanlar kesimida); • o‘quv jarayonini tashkil etish (o‘quv yili, o‘quv semestrlari, bakalavriat va magistratura o‘quv dasturlari, talabalarning akademik guruhlari uchun dars jadvali); • Talabalar davomati monitoringini yuritish (fanlar, mashg‘ulot turlari, o‘qituvchilar, dars jadvali va elektron jurnal); • Talabalarning fanlar bo‘yicha o‘zlashtirish monitoringi (o‘quv reja va fanlar, oraliq va yakuniy imtihonlar jadvali, reyting qaydnomasi, elektron reyting daftarchasi); • To‘lov-shartnoma bo‘yicha amalga oshirilgan to‘lovlar hisobi (shartnomalar va hisob varaqalarni ro‘yxatga olish, pul tushumlarini hisobga olish, ma’lumotnomalar taqdim etish); • Interaktiv onlayn xizmatlarni taqdim etish (OTM rahbariyati, talabalar, o‘qituvchilar, ota-onalar

Management and Future Technologies journal.umft.uz April 2024 Volume 1, Issue 1 va davlat idoralari xodimlari uchun) imkoniyatlari yaratildi. Xulosa Xulosa qilib aytganda ushbu axborot tizimi davlat oliy ta’lim muassasalarining ma’muriy boshqaruv, o‘quv, ilmiy va moliyaviy faoliyatini raqamlashtirishga qaratilgan bo‘lib 35 dan ortiq turli hisobotlar va statistik ma’lumotlar faqat elektron tarzda shakllantirilmoqda. Talabalarning guruh jurnallari, o‘zlashtirish qaydnomalari to‘liq raqamlashtirilib respublika bo‘yicha qog‘oz sotib olish uchun xar yili sarflanadigan 2 milliard so‘mdan ortiq mablag‘ni tejashga erishilmoqda. Axborot tizimini joriy etish natijasida ma’lumotlarni onlayn shakllantirish, ortiqcha vaqt va resurslar tejash, faoliyatni ochiqligini ta’minlash bilan bir qatorda quyidagi yo‘nalishlarda sezilarli darajada iqtisodiy samaradorlik kuzatildi. Adabiyotlar 1. Исаева Е. С. Современные LMS платформы дистанционного обучения: анализ и сравнение. // Педагогика. Вопросы теории и практики. 2021. Том 6. Выпуск 6. С. 10451050 2. Jamshid Elov, Utkir Khamdamov, Inomjon Narzullayev, Djamshid Sultanov, Alisher Abdullaev. Development of a database of higher education process management information system based on the relational model. // International conference on information science and communications technologies Applications, Trends and Opportunities (ICISCT 2021). Tashkent -2021. 3-5 November. 4p. 3. Abdullaev A. Oliy va o‘rta maxsus ta’lim vazirligi uchun oliy ta’lim jarayonlarini boshqarish axborot tizimining funksional imkoniyatlari. // Muxammad al-xorazmiy avlodlari ilmiy-amaliy va axborot tahliliy jurnal. 4(22)/2022. –B.30-35 4. Хамдамов У. Перспективы внедрения информационной системы управление высшим образованием. // “Iqtisodiyot tarmoqlarining innovatsion rivojlanishida axborotkommunikatsiya texnologiyalarining ahamiyati” Respublika ilmiy-texnik anjumani ma’ruzalar to‘plami. 2022 йил 10-11 mart, Toshkent - 2022. – B. 6-9 11

Volume 1, Issue 1 April 2024 12 Authors Mukhiddinov Mukhriddin Department of Communication and Digital Technologies, University of Management and Future Technologies, Tashkent 100208, Uzbekistan; mukhidddinov@umft.uz Djuraev Oybek Department of Communication and Digital Technologies, University of Management and Future Technologies, Tashkent 100208, Uzbekistan; djurayev@umft.uz Djuraev Jasurbek Department of Software Engineering, Tashkent University of Information Technologies named after Mukhammad al-Khwarizmi, Tashkent 100200, Uzbekistan; Rakhmatillaeva Shakhrizoda Department of Communication and Digital Technologies, University of Management and Future Technologies, Tashkent 100208, Uzbekistan; Correspondence: mukhiddinov@ umft.uz Copyright: © 2024 by the authors. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/ by/4.0/). Management and Future Technologies journal.umft.uz THE ROLE AND PROSPECTS OF DIGITAL AND INNOVATIVE TECHNOLOGIES IN EDUCATION FOR THE VIZUALLY IMPAIRED PEOPLE A bstract: In the realm of aiding the visually impaired and blind population, challenges arise in enabling independent mobility and mitigating daily obstacles. To address these concerns, the integration of artificial intelligence and computer vision methodologies offers promising solutions for individuals with visual impairments, reducing their reliance on external assistance. Smart glasses emerge as a prospective assistive technology, serving to enhance autonomous navigation and fostering a sense of social security and convenience. Nevertheless, practical limitations persist for the visually impaired, notably in navigating dark environments and during nighttime hours. This research endeavors to propose a smart glass system tailored for individuals with visual impairments, leveraging sophisticated computer vision techniques, deep learning models, auditory cues, and tactile graphics. The primary aim is to empower autonomous movement within nighttime settings, fostering self-sufficiency and safety for the visually impaired demographic. The architecture comprises four distinct models: a low-light image enhancement model, an object recognition system integrated with audio feedback, a salient object detection model, and a text-to-speech mechanism coupled with tactile graphics generation. Keywords: smart glasses; artificial intelligence; blind and visually impaired; deep learning; assistive technologies; object detection; refreshable tactile display.

VIPE]MRJSVQEXMSREFSYXLE^EVHWSVZILMGYPEVXVEJJMG[LIRREZMKEXMRKWXVIIXW7MQMPEVP]XLI XVEMRMRKERHYTOIITSJKYMHIHSKWEVIPEFSVMSYWERHGSWXP]HIQERHMRKWTIGMEPEXXIRXMSR 1SVISZIV [LMPI WQEVXTLSRI ETTPMGEXMSRW PMOI ZSMGI EWWMWXERGI ERH REZMKEXMSR QETW XEMPSVIH JSV &:- MRHMZMHYEPW EVI EHZERGMRK XLIMV GSQTVILIRWMZI ERH TVSJMGMIRX YXMPM^EXMSR Management and Future Technologies journal.umft.uz April 2024 Volume 1, Issue 1 VIQEMRWVIPEXMZIP]PS[ 7QEVXKPEWWIWWXERHSYXEWETVIJIVVIH[IEVEFPIEWWMWXMZIXIGLRSPSK]JSVMRHMZMHYEPW [MXLZMWYEPMQTEMVQIRXWSJJIVMRKFSXLGSQJSVXERHJYRGXMSREPMX]EOMRXSEGSQTYXIV7MRGI Introduction +SSKPI MRXVSHYGIH +SSKPI +PEWW MR XEVKIXMRK XLI ZMWYEPP] MQTEMVIH RYQIVSYW In the contemporary landscape of information and communication technology, the lifestyle and GSQTERMIWPMOI)TWSR7SR]1MGVSWSJX)RZMWMSRI7MKLX2Y)]IW3\WMKLXERH3V'EQLEZI autonomous individuals withWQEVX visual impairments constitute paramount societal concern. IRXIVIH XLImobility QEVOIXofIEGL SJJIVMRK KPEWWIW [MXL ZEV]MRKa PIZIPW SJ YWEFMPMX] 8LIWI Governments and specialized organizations have instituted numerous laws andWYTTSVXMRK standards, establishing HIZMGIW GSQQSRP] JIEXYVI ER IQFIHHIH STIVEXMRK W]WXIQ [MVIPIWW essential infrastructure to support those with visual disabilities. According to the World Health GSRRIGXMZMX]ZME;M*MSV&PYIXSSXL Organization, as of 2020, a staggering 2.2 billion individuals globally grapple with vision impairment or blindness, with at least 1 billion facing preventable or unaddressed vision impairments [1]. The 4VSTSWIH7]WXIQ causes3YV encompass factors, including cataractsSJ(94 million), unattended refractive errors (88.4 EMQ MWdiverse XS IRLERGI XLI EYXSRSQ] MRHMZMHYEPW [MXL ZMWYEP MQTEMVQIRXW F] million), glaucoma (7.7 million), corneal opacities (4.2 million), diabetic retinopathy (3.9 million), IREFPMRKMRHITIRHIRXXVEZIPFSXLHYVMRKXLIHE]ERHEXRMKLX8SEGGSQTPMWLXLMWSFNIGXMZI trachoma (2 million), among others [1]. [I ZIHIZMWIHE[IEVEFPIWQEVXKPEWWW]WXIQMRXIKVEXIH[MXLEQYPXMJYRGXMSREPWIXYT8LMW W]WXIQ IQTPS]W E QMRMEXYVI GEQIVE XS GETXYVI MQEKIW SFNIGX(BVI) VIGSKRMXMSR The primary challenges confronting individuals who are blind orPIZIVEKMRK visually impaired in their XIGLRSPSK] XS TVSZMHI YWIVW [MXL EYHMXSV] JIIHFEGO %HHMXMSREPP] JSV XEGXMPI MRXIVEGXMSR daily lives revolve around issues of action and environmental awareness. Numerous solutions have [I MRGSVTSVEXIH E VIJVIWLEFPI HMWTPE] XLEX EPPS[W YWIVW XS I\TPSVI HIXIGXIH WEPMIRX beenZI proposed, leveraging navigation and object recognition methodologies. However, widely adopted SFNIGXWF]XSYGLGEXIVMRKXSXLIWTIGMJMGRIIHWSJXLIZMWYEPP]MQTEMVIH8SIRWYVITVIGMWI navigation aids, such as canes, trained guide dogs, and smartphone applications, exhibit inherent SFNIGX HIXIGXMSR SYVcanes W]WXIQ SR over HIIT 'SRZSPYXMSREP 2IYVEP 2IX[SVOW '22W limitations. For instance, proveVIPMIW ineffective extended distances and in crowded environments, RIGIWWMXEXMRK E VSFYWX TVSGIWWSV JSV VIEPXMQI TVSGIWWMRK 8LYW [I ZI HIZMWIH E lacking the ability to relay information about hazards or vehicular traffic when navigating GPMIRX– streets. WIVZIV EVGLMXIGXYVI XLEX GSQTVMWIW XLI WQEVX KPEWW ERH E WQEVXTLSRI VIJIVVIH XS EW E Similarly, the training and upkeep of guide dogs are laborious and costly, demanding special attention. XEGXMPITEHJSVWMQTPMGMX] 8LMWPSGEPWIXYTGSQQYRMGEXIWHEXEZMEE&PYIXSSXLGSRRIGXMSR Moreover, while smartphone applications like voice assistance and navigation maps tailored for BVI 7MQYPXERISYWP] ER EVXMJMGMEP MRXIPPMKIRGI WIVZIV VIGIMZIW XLI GETXYVIH individuals are advancing, their comprehensive and proficient utilization remains MQEKIW relativelyJVSQ low. XLI PSGEPWIXYTI\IGYXIWMQEKITVSGIWWMRKXEWOWERHVIPE]WXLIVIWYPXWMREYHMSJSVQEX8LI SmartHIWMKR glasses SJ stand as a preferred wearable assistive MR technology for8LI individuals with visual SZIVEPP SYVout TVSTSWIH W]WXIQ MW HITMGXIH *MKYVI PSGEP GSQTSRIRX impairments, offering both comfort and WQEVXTLSRI functionality akin to a computer. Since Google introduced IRGSQTEWWIW XLI WQEVX KPEWW ERH JEGMPMXEXMRK HEXE XVERWJIV ZME &PYIXSSXL “Google Glass” in 2013 targeting the visually impaired, numerous companies like Epson, Sony, 1IER[LMPIXLIEVXMJMGMEPMRXIPPMKIRGIWIVZIVTVSGIWWIWVIGIMZIHMQEKIWERHXVERWQMXWXLI Microsoft, Envision, eSight, NuEyes, Oxsight, and OrCam have entered the market, each offering SYXGSQIWMREYHMSJSVQEX-X WMQTSVXERXXSRSXIXLEXXLIWQEVXKPEWWLEVH[EVIMRGPYHIWE smart glasses with varying levels of usability. These devices commonly operating FYMPXMR WTIEOIV JSV HMVIGX SYXTYX ERH ER IEVTLSRI TSVX JSV feature YWIVW an XSembedded EGGIWW XLI EYHMS system, supporting wireless connectivity via Wi-Fi or Bluetooth.. VIWYPXWVIPE]IHJVSQXLIWQEVXTLSRI *MKYVI8LISZIVEPPHIWMKRSJXLITVSTSWIHW]WXIQ Figure 1. The overall design of the proposed system. 13

Volume 1, Issue 1 14 April 2024 Management and Future Technologies journal.umft.uz Proposed System Our aim is to enhance the autonomy of individuals with visual impairments by enabling independent travel, both during the day and at night. To accomplish this objective, we’ve devised a wearable smart glass system integrated with a multifunctional setup. This system employs a miniature camera to capture images, leveraging object recognition technology to provide users with auditory feedback. Additionally, for tactile interaction, we’ve incorporated a refreshable display that allows users to explore detected salient objects by touch, catering to the specific needs of the visually impaired. To ensure precise object detection, our system relies on deep Convolutional Neural Networks (CNNs), necessitating a robust processor for real-time processing. Thus, we’ve devised a client–server architecture that comprises the smart glass and a smartphone (referred to as a tactile pad for simplicity). This local setup communicates data via a Bluetooth connection. Simultaneously, an artificial intelligence server receives the captured images from the local setup, executes image processing tasks, and relays the results in audio format. The overall design of our proposed system is depicted in Figure 1. The local component encompasses the smart glass and smartphone, facilitating data transfer via Bluetooth. Meanwhile, the artificial intelligence server processes received images and transmits the outcomes in audio format. It’s important to note that the smart glass hardware includes a built-in speaker for direct output and an earphone port for users to access the audio results relayed from the smartphone. To enable object recognition, we implemented a transformer-based encoder–decoder design [2], renowned for its efficacy in sequence prediction tasks. Transformers’ self-attention mechanisms, adept at modeling intricate interactions within sequences, make them well-suited for addressing prediction constraints, notably in eliminating redundant predictions. Specifically, the Detection Transformer (DETR) adopts a unique approach by simultaneously predicting all objects and training end-to-end using a unified loss function to establish bipartite matching between predicted and actual objects [2]. Notably, DETR distinguishes itself from conventional detection methods by obviating the need for custom layers, offering flexibility within structures combining standard CNN and transformer attributes. Experimental findings demonstrated DETR’s commendable accuracy in detecting larger objects. However, its performance diminished when detecting smaller objects. The DETR network comprises four key components: (1) a CNN backbone to extract concise feature representations, (2) a transformer encoder, (3) a transformer decoder, and (4) a straightforward feedforward network (FFN) responsible for generating the final detection predictions. Individuals with visual impairments can benefit from voice feedback not only regarding the presence of surrounding objects but also concerning textual information within natural scenes. This is particularly crucial in daily life as text data provides precise and unambiguous descriptions of the environment, aiding blind and visually impaired individuals in accessing vital visual information. Text is prevalent on diverse objects in natural settings, including billboards, road signs, and product packaging. However, recognizing scene text poses challenges due to factors such as illumination variations, blurring, color differences, complex backgrounds, suboptimal lighting conditions, noise, and discontinuities. To address these challenges, we employed our previously developed real-time end-to-end scene text recognition system, alongside the Tesseract OCR engine [72]. This combination enabled us to achieve robust and accurate results when working with datasets such as ExDark, LOL, and our captured natural scene images. One distinguishing feature of our smart glass system, setting it apart from existing solutions, is its capacity to generate tactile graphics. This functionality empowers the blind by offering visual information through the depiction of salient objects’ contours. Additionally, we employed specialized software for the tactile display system [3] to aid the blind and visually impaired in perceiving and identifying elements within natural scene images.

Management and Future Technologies SFNIGXW0PEVKISFNIGXW 1SHIPW journal.umft.uz &EGOFSRI2IX[SVO April 2024 %4 Volume 1, Issue 1 %4 %4 %47 %41 %40 3,)1?A :++2IX *EWXIV6'22[*42?A 6IW2IX*42 6IXMRE2IX?A 6IW2I<X*42 6IJMRI(IX?A 6IW2IX 6*&2IX)?A :++2IX 'SVRIV2IX?A ,SYVKPEWW ()86?A 6IW2IX 'SRGPYWMSR Experimental Results 8LMW TETIV SYXPMRIW E WQEVX KPEWW W]WXIQ MRXIKVEXMRK GSQTYXIV ZMWMSR ERH HIIT PIEVRMRKQSHIPWJSVMRHMZMHYEPW[MXLZMWYEPMQTEMVQIRXW8LIW]WXIQIRGSQTEWWIWSFNIGX The performance assessment of our object detection models on low-light images involved the HIXIGXMSRWEPMIRXSFNIGXI\XVEGXMSRERHXI\XVIGSKRMXMSRQSHYPIWSTIVEXMRKWIEQPIWWP]SR evaluation of the DETR model in comparison to 10 other state-of-the-art models. These models ER EVXMJMGMEP MRXIPPMKIRGI WIVZIV (IWMKRIH XS FI JYPP] EYXSRSQSYW[7], XLIRFBNet512-E W]WXIQ IJJIGXMZIP] include OHEM [4], Faster RCNNwFPN [5], RetinaNet [6], RefineDet512+ [8], and MHIRXMJMIW ERH VIGSKRM^IW SFNIGXW [MXLMR PS[PMKLX ERH HEVOWGIRI MQEKIW XLIVIF] CornerNet511 [9]. Performance comparisons were conducted utilizing the results presented in the JEGMPMXEXMRK REZMKEXMSR MRHMZMHYEPW [MXL ZMWYEP MQTEMVQIRXW MR RSGXYVREP respective papers and theJSV source code provided by the authors. Quantitative analysisIRZMVSRQIRXW was performed 7MKRMJMGERXP] EHZERGMRK FI]SRH GSRZIRXMSREP WQEVX W]WXIQW employing established metrics, including Precision, Recall, andKPEWW AP. Precision and XLMW recall ETTVSEGL rates were MRGSVTSVEXIW HIIT PIEVRMRK MRXVSHYGMRK WEPMIRX SFNIGXgenerated I\XVEGXMSR JSV XEGXMPI computed by comparing pixel-levelQSHIPW ground truth images with the outcomes by the proposed KVETLMGWERHXI\XVIGSKRMXMSRJSVXLIGSRZIVWMSRSJXI\XXSWTIIGL method. The comparative findings between the DETR model and other cutting-edge models, all of which have been published in prestigious conferences and journals like CVVR, ICCV, ECCV, and AAAI6IJIVIRGIW in recent years, are outlined in Table 1. Table 1. The performance comparison of DETR with other state-of-the-art methods on LOL and 7XIMRQIX^ .( &SYVRI 66 &VMERX 47 *PE\QER 76 8E]PSV ,6 .SREW .& ExDark datasets. The best results are marked with bold. S: small objects, M: medium objects, L: large %FHSPM %% %FVLE ;% %FYEPLEWER % %FY+LEVFMIL )+ IX EP 'EYWIW SJ objects. FPMRHRIWWERHZMWMSRMQTEMVQIRXMRERHXVIRHWSZIV]IEVWERHTVIZEPIRGI SJEZSMHEFPIFPMRHRIWWMRVIPEXMSRXS:-7-328LI6MKLXXS7MKLX%REREP]WMW Conclusion JSVXLI+PSFEP&YVHIRSJ(MWIEWI7XYH]0ERGIX+PSF,IEPXLI–I paper 'EVMSR 2 1EWWE 7]RREIZI + 9WYRMIV 2 /MVMPPSZ >EKSVY]OS 7 )RHXS This outlines a smart* glass system integrating computer vision% and deep learning models IRH SFNIGX HIXIGXMSR [MXL XVERWJSVQIVW -R )YVSTIER 'SRJIVIRGI 'SQTYXIV for individuals with visual impairments. The system encompasses object detection,SR salient object :MWMSR7TVMRKIV'LEQ7[MX^IVPERHTT– extraction, and text recognition modules, operating seamlessly on an artificial intelligence server. /MQ76]Y='LS.6]Y)8S[EVHW8ERKMFPI:MWMSRJSVXLI:MWYEPP]-QTEMVIH Designed to be fully autonomous, the system effectively identifies and recognizes objects within lowlight andXLVSYKL(1YPXMEVVE]&VEMPPI(MWTPE]7IRWSVW dark-scene images, thereby facilitating navigation for individuals with visual impairments 7LVMZEWXEZE%+YTXE%+MVWLMGO68VEMRMRKVIKMSRFEWIHSFNIGXHIXIGXSVW[MXL in nocturnal environments. Significantly advancing beyond conventional smart glass systems, this approachSRPMRILEVHI\EQTPIQMRMRK-R4VSGIIHMRKWSJXLI-)))'SRJIVIRGISR'SQTYXIV incorporates deep learning models, introducing salient object extraction for tactile graphics :MWMSRERH4EXXIVR6IGSKRMXMSR 0EW:IKEW2:97%.YRI–.YP] and text recognition for the conversion of text':46 to speech. 0MR 8= (SPPEV 4 +MVWLMGO 6 ,I /1 ,EVMLEVER & &IPSRKMI 7 *IEXYVI T]VEQMHRIX[SVOWJSVSFNIGXHIXIGXMSR-R4VSGIIHMRKWSJXLI-)))'SRJIVIRGISR 'SQTYXIV :MWMSR ERH 4EXXIVR 6IGSKRMXMSR ':46 ,SRSPYPY ,- 97% – .YP] 0MR8+S]EP4+MVWLMGO6,I/(SPPV4*SGEPPSWWJSVHIRWISFNIGXHIXIGXMSR -R 4VSGIIHMRKW SJ XLI -))) -RXIVREXMSREP 'SRJIVIRGI SR 'SQTYXIV :MWMSR -'': :IRMGI-XEP]–3GXSFIV >LERK7;IR0&MER<0IM>0M7>7MRKPIWLSXVIJMRIQIRXRIYVEPRIX[SVOJSV SFNIGXHIXIGXMSR-R4VSGIIHMRKWSJXLI-)))'SRJIVIRGISR'SQTYXIV:MWMSRERH 15

Volume 1, Issue 1 16 April 2024 Management and Future Technologies journal.umft.uz References 1. Steinmetz, J.D.; Bourne, R.R.; Briant, P.S.; Flaxman, S.R.; Taylor, H.R.; Jonas, J.B.; Abdoli, A.A.; Abrha, W.A.; Abualhasan, A.; Abu-Gharbieh, E.G.; et al. Causes of blindness and vision impairment in 2020 and trends over 30 years, and prevalence of avoidable blindness in relation to VISION 2020: The Right to Sight: An analysis for the Global Burden of Disease Study. Lancet Glob. Health 2021, 9, e144–e160. 2. Carion, N.; Massa, F.; Synnaeve, G.; Usunier, N.; Kirillov, A.; Zagoruyko, S. End-to-end object detection with transformers. In European Conference on Computer Vision; Springer: Cham, Switzerland, 2020; pp. 213–229. 3. Kim, S.; Ryu, Y.; Cho, J.; Ryu, E. Towards Tangible Vision for the Visually Impaired through 2D Multiarray Braille Display. Sensors 2019, 19, 5319. 4. Shrivastava, A.; Gupta, A.; Girshick, R. Training region-based object detectors with online hard example mining. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA, 26 June–1 July 2016. 5. Lin, T.Y.; Dollar, P.; Girshick, R.; He, K.M.; Hariharan, B.; Belongie, S. Feature pyramid networks for object detection. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA, 21–26 July 2017. 6. Lin, T.; Goyal, P.; Girshick, R.; He, K.; Dollr, P. Focal loss for dense object detection. In Proceedings of the IEEE International Conference on Computer Vision (ICCV), Venice, Italy, 22–29 October 2017. 7. Zhang, S.; Wen, L.; Bian, X.; Lei, Z.; Li, S.Z. Single-shot refinement neural network for object detection. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Salt Lake, UT, USA, 18–22 June 2018. 8. Liu, S.; Huang, D.; Wang, Y. Receptive field block net for accurate and fast object detection. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Salt Lake, UT, USA, 18–22 June 2018. 9. Law, H.; Deng, J. Cornernet: Detecting objects as paired keypoints. In Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany, 8–14 September 2018. 10. Carion, N.; Massa, F.; Synnaeve, G.; Usunier, N.; Kirillov, A.; Zagoruyko, S. End-to-end object detection with transformers. In European Conference on Computer Vision; Springer: Cham, Switzerland, 2020; pp. 213–229.

Management and Future Technologies journal.umft.uz VIDEOMONITORING HOSSALARIGA ASOSLANGAN MASOFAVIY TA’LIM JARAYONLARINI TAKOMILLASHTIRISHNING AXBOROT MODELI A nnotatsiya: So‘nggi bir ikik yil ichida ta’lim sohasida ulkan o‘zgarishlar sodir bo‘ldi deb aytish mumkin. Talim tizimi COVID-2019 pandemiyasi tufayli o‘quvchi va talabalarni ko‘proq resruslar bilan ta’minlash uchun ta’limning masofaviy shaklidan foydalana boshlashdi. Buning ortidan proktoring tizimlarga bo‘lgan talabalar ham o‘z navbatida o‘sa boshladi. Sun’iy intellektga asoslangan avtoproktoring tizimlar esa masofaviy ta’limga shiddan bilan kirib keldi. Onlayn proktoring tizimlari masofaviy ta’lim va imtihon jarayonlarini o‘tkazishda shafoflikni ta’minlash uchun foydalanildi. Ushbu maqolada sun’iy intellektga asoslangan proktoring tizimlarni ishlab chiqishning axborot modeli taklif etilgan. April 2024 Volume 1, Issue 1 17 Mualliflar Sirojiddin Xalilov Kommunikatsiya va raqamli texnologiyalar kafedrasi, “University of management and future technologies” universiteti, Toshkent, 100208, O‘zbekiston; kh.surajiddin@gmail.com Farangiza Omonullaeva Raqamli texnologilar kafedrasi, Vatan University universiteti, Toshkent, 100208, O‘zbekiston; Maftuna Mannapova Raqamli texnologilar kafedrasi, Vatan University universiteti, Toshkent, 100208, O‘zbekiston; Mas’ul: kh.surajiddin@gmail.com Kalit so‘zlar: Sun’iy intellekt, proktoring, tizimlar, tasvir, masofaviy talim. 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/).

Volume 1, Issue 1 April 2024 18 Mualliflar Sirojiddin Khalilov Department of Communication and Digital Technologies, “University of Management and Future Technologies”, Tashkent, 100208, Uzbekistan; kh.surajiddin@gmail.com Farangra Omonullaeva Department of Digital Technologists, Vatan University, Tashkent, 100208, Uzbekistan; Maftuna Mannapova Department of Digital Technologists, Vatan University, Tashkent, 100208, Uzbekistan; Correspondence: kh.surajiddin@ gmail.com Management and Future Technologies journal.umft.uz INFORMATION MODEL OF IMPROVING DISTANCE EDUCATION PROCESSES BASED ON VIDEO MONITORING PROPERTIES A bstract: In the last one or two years, it can be said that tremendous changes have taken place in the field of education. Due to the COVID-2019 pandemic, the education system has started using distance learning to provide more resources to students. After that, the number of students in proctoring systems also began to increase. And artificial intelligence-based autoproctoring systems have made inroads into distance learning. Online proctoring systems have been used to ensure transparency in conducting distance learning and examination processes. This paper proposes an information model for the development of proctoring systems based on artificial intelligence. Keywords: Artificial intelligence, proctoring, systems, image, distance learning. Copyright: © 2024 by the authors. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/ by/4.0/).

Management and Future Technologies journal.umft.uz April 2024 Volume 1, Issue 1 Butun jaxon bo‘yicha talim tizimi evolyusion rivojlanmoqda. So‘ngi yil mobaynida masofaviy talim tizimi ham sezilarli suratda rivojlandi. Bundan tashqari, COVID-19 pandemiyasi masofaviy talimni joriy etishga katta ta’sir ko‘rsatdi. Pandemiya butun dunyoni yopiq hududlarga majburlaganiligi sababli, talim sektorlari masofaviy talim jarayoniga o‘tish bilan bir qancha muammolar paydo bo‘lishiga olib keldi. Birinchi qiyinchilik talim oluvchilarning talim olish jarayonida har hil qoidabuzarliklarni sodir etishlari (dars mashg‘ulotlariga kirmasligi, o‘rniga org‘oti ishtiroki, yoki umuman tizimdan foydalanmaslik). Ushbu muammolarni hal qilish bo‘yicha talim muassalari ilmiy izlanishdar olib bormoqda. Bunda olimlar asosiy etiborni imtixon jarayonlari tashkil etish va boshqarishga samarali echimlarni taklif etishgan. Masofadan turib imtixonlarni tashkil etish va uni boshqarish tizimlarda videomonitoring (proktoring) qilish jarayonlar jaxon bo‘yicha ilmiy tadqiqotchilar samarali natijalarga erishgan. Ammo masofaviy talim jarayoni tashkil etish, boshqarish va talim oluvchilar nazorat qiluvchi tizimlar ishlab chiqish bo‘yicha yetarli muammolar mavjud. Masofadan turib imtixonlari tashkil qiluvchi tizimlarning barcha nazorat foydalunchilar nazorat qilishda barcha hususiyatlar mavjud lekin ular asosan yakuniy test sinovlarini nazorat kilishga mo‘ljallangan. Masofaviy nazorat - bu onlayn imtihon jarayonini kengaytiriladigan tarzda autentifikatsiya qilish, ruxsat berish va nazorat qilish jarayonidir. Bu to‘liq xavfsizlik standartlarini ta’minlab, tashkilotlarga istalgan joyda va istalgan vaqtda baholash imkonini beradigan jarayon. Boshqacha qilib aytganda, foydalanuvchilar malum bir joyga kelishlari shart emas, chunki ular o‘z uylaridan dars mashg‘ulotlari o‘zlashtirishlar va imtihon topshirishlari mumkin. An’anaviy imtihon jarayonini oladigan bo‘lsak, imtihonga kelgan nomzodlarni tekshirish uchun imtihon markazida nazoratchi bo‘lishi kerak. 30-40 nomzodni tekshirish uchun sizga bitta nazoratchi kerak, ammo 1000 dan ortiq nomzoddan imtihon topshirish uchun sizga imtihon jarayonini nazorat qiluvchi 25 dan ortiq nazoratchi kerak bo‘ladi. Bu bevosita ish kuchi va mablag‘ga bog‘liq. Onlayn proktoring tizimlarida esa nomzodning veb-kamerasi va internet tarmog‘idan foydalanib amalga oshirilishi mumkin. U har bir imtihon seansini boshidan oxirigacha faqat video orqali emas, balki ish stoli ekranlaridagi suhbat jurnallari va nazorat qilib boradi. Xozirgi kunda zamonaviy sunniy intellekt texnologiyalari yordamida masofadan talim foydalanuvchilarni samarali nazorat qilish mumkin. Bundan talim istalgan vaqtda, istalgan joydan turib o‘quv materiallari va test imtixonlarini markazlashgan platformadan topshirish imkoniyati yaratib beradi. Sunniy intellekt texnologiyalariga qo‘llab-quvvatlaydigan avtoproktoring tizimlaridan foydalanish doimiy ravishda moslashishni o‘rganishi va aqlliroq bo‘lishi mumkin. Sunniy intellektni proktoringga joriy etishdan maqsad odamlarni almashtirish emas, balki odamlarga past ovozli shivirlash, soyalarni aks ettirish kabi hususiyatlarni aniqlashda yordam berish orqali proktoringning aniqligini oshirishdir. Sunniy intellektni qo‘llab-quvvatlaydigan masofadan turib tekshirish quyidagi uchta sohaga qaratilgan, identifikatsiya firibgarligini aniqlash, aldash xatti-harakatlarini tahlil qiling, kontentni o‘g‘irlashni tahlil qilish. 19

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