DIGITAL TRANSFORMATION AND ARTIFICIAL INTELLIGENCE
The Journal is a publication specializing in the field of digital economy and information technologies. Raqamli transformatsiya va sun’iy intellekt ilmiy jurnali
AXBOROT TEXNOLOGIYALARI ASOSIDA DAVLAT XARIDLARIDA SHAFFOFLIK VA SAMARADORLIKNI TA’MINLASH
Mazkur maqolada budjet buyurtmachilari tomonidan tovar, ish, xizmatlar sotib olishda davlat xaridlarining zaruriyati ko‘rsatilgan. Davlat xaridlari ko‘lamining kengayishi mahalliy ishlab chiqaruvchilarni qo‘llab-quvvatlash, ish o‘rinlari yaratish, soliq tushumlarini oshirish bilan bog‘liqligi tahlil qilingan. Raqamli texnologiyalardan foydalanish asosida davlat xaridlarining ochiqligi va samaradorligini ta’minlash bo‘yicha amaliy taklif va tavsiyalar ishlab chiqilgan.
Copyright (c) 2025 Kasimova Gulyar Axmatovna, Abdushukurov Zafar Ismatovich https://creativecommons.org/licenses/by/4.0
GASTROENTEROLOGIK KASALLIKLARNI KOʻP MEZONLI TASHXISLASH JARAYONLARI UCHUN AXBOROT MODELI
Mazkur tadqiqot ishida gastroenterologik kasalliklarni ko‘p mezonli tashxislash jarayonlarini avtomatlashtirishga mo‘ljallangan axborot modeli ishlab chiqilgan bo‘lib, taklif etilgan model bemordan olinadigan elektrogastrogramma signallari, klinik simptomlar, laborator va instrumental tekshiruv natijalarini kompleks tahlil qilishga asoslanadi. Axborot modeli signalni yig‘ish, filtrlash, tahlil qilish va qaror qabul qilish bosqichlarini o‘z ichiga olgan holda matematik va algoritmik yondashuvlar asosida shakllantirilgan. Bemorning biofizik signallarini qayta ishlash jarayonida Butterworth bandpass filtri qo‘llanilib, signalning foydali chastota diapazonlari ajratib olinadi. Filtrlangan signal ma’lumotlar bazasida saqlanayotgan diagnostik signallar bilan kosinus o‘xshashligi usuli yordamida solishtiriladi va kasalliklarga tegishlilik darajalari aniqlanadi. Qaror qabul qilish jarayonida avtomatlashtirilgan algoritmlar bilan bir qatorda ekspert shifokor ishtiroki ham ta’minlanadi, bu esa tashxisning aniqligi va ishonchliligini oshiradi. Tizimning funksional modeli IDEF0 diagrammalari asosida ishlab chiqilib, unda axborot oqimlari, kirish va chiqish ob’ektlari, boshqaruv va mexanizmlar aniq belgilangan. Tadqiqot natijalari gastroenterologik kasalliklarni tezkor, aniqligi yuqori va xolis tarzda tashxislash imkonini beruvchi axborot tizimini yaratishga xizmat qiladi hamda tibbiy qaror qabul qilish jarayonlarini qo‘llab-quvvatlashda muhim amaliy ahamiyatga ega.
Copyright (c) 2025 Aliqulov Akmal Xushmuratovich https://creativecommons.org/licenses/by/4.0
KASALLIKLARNI TASHXISLASHDA ZARRACHALAR TO‘DASI VA INTELLEKTUAL SUV TOMCHISI ALGORITMLARIGA ASOSLANGAN GIBRID YONDASHUVDAN FOYDALANISH
Ushbu maqolada ko‘p sinfli klassifikatsiya masalalarini samarali yechish uchun zarrachalar to‘dasi va intellektual suv tomchisi algoritmlarining afzalliklarini birlashtiruvchi PSO–IWD gibrid algoritmi taklif etilgan. Taklif etilgan yondashuvda PSO algoritmi global qidiruv mexanizmi sifatida yechimlar maydonini keng ko‘lamda o‘rganib, maqbul yechimlarni aniqlaydi. IWD algoritmi yordamida esa lokal qidiruv jarayonida suv tomchilarining harakati orqali eng qisqa va samarali yo‘llar aniqlanadi hamda global qidiruvda topilgan eng yaxshi yechimlar optimallashtiriladi. Taklif etilgan PSO–IWD algoritmi yurak-qon tomir kasalliklari tashxisida qo‘llaniladigan ikki xil tanlanmada sinovdan o‘tkazildi va mos ravishda 99 % hamda 98 % klassifikatsiya aniqligiga erishildi. Olingan natijalar gibrid yondashuvning murakkab tibbiy va ko‘p sinfli klassifikatsiya masalalarini yechishda yuqori samaradorlikka ega ekanini ko‘rsatadi.
Copyright (c) 2025 Klicheva Firuza Gulmuratovna https://creativecommons.org/licenses/by/4.0
SO‘ZLOVCHILARNI AJRATISHDA ECAPA-TDNN ASOSIDAGI XUSUSIYATLAR VEKTORLARIDAN FOYDALANISH
So‘zlovchilarni ajratishda xususiyatlar vektorlarini shakllantirish eng muhim bosqichlardan biri hisoblanadi. Zamonaviy chuqur neyron tarmoqlar so‘zlovchining individual xususiyatlarini aniqlash va ularni ifodalashda yuqori aniqlikni ta’minlay oladi. Xususan, x-vector kabi mashhur xususiyatlar vektorlari hozirgi tizimlarda asosiy komponent sifatida qo‘llanib kelmoqda. So‘nggi yillarda TDNN arxitekturasi ustida turli takomillashtirishlar ishlab chiqildi. ECAPA-TDNN modeli esa o‘ziga xos kanal e’tibor mexanizmi va ko‘p qatlamli agregatsiya orqali so‘zlovchini tekshirish vazifalarida yuqori samaradorlikni ko‘rsatdi. Tadqiqot mazkur modelni diarizatsiya sohasiga tatbiq etish imkoniyatlarini namoyish etadi.
so‘zlovchilarni ifodalovchi xususiyatlar vektori
Copyright (c) 2025 Umidjon Xasanov Komiljon o‘g‘li https://creativecommons.org/licenses/by/4.0
TURIZMNING BARQAROR RIVOJLANISHIDA KREATIV INDUSTRIYALARNING ROLI
Mazkur ilmiy maqolada kreativ sanoatlarning turizmning barqaror rivojlanishidagi o‘rni va ahamiyati tahlil qilinadi. Tadqiqotning asosiy maqsadi kreativ iqtisodiyot elementlari – hunarmandchilik, gastronomiya, san’at, dizayn, madaniy meros va raqamli kreativ faoliyatning turizmning iqtisodiy, ijtimoiy hamda ekologik barqarorligiga ta’sirini aniqlashdan iborat. Tadqiqot sifatli analitik va qiyosiy metodologiyaga asoslanib, BMTning Jahon Turizm Tashkiloti (UNWTO), UNESCO, UNCTAD va OECD ma’lumotlari, shuningdek, O‘zbekiston, Indoneziya va Ispaniya tajribalarini qamrab oladi. Tadqiqot natijalari shuni ko‘rsatadiki, kreativ sanoatlar turizm mahsulotlarini diversifikatsiya qilish, mahalliy jamoalarni iqtisodiy jihatdan qo‘llab-quvvatlash, madaniy merosni asrash va ekologik mas’uliyatni kuchaytirish orqali barqaror turizmni rivojlantirishda strategik omil hisoblanadi. O‘zbekiston misolida Xiva, Buxoro va Samarqand shaharlari tajribasi ushbu yondashuvning yuqori samaradorligini tasdiqlaydi.
Copyright (c) 2025 Qurbonova Zebiniso Azimovna https://creativecommons.org/licenses/by/4.0
IOT TIZIMLARIDA XESH FUNKSIYALARIDAN FOYDALANISHNING XAVFSIZLIK VA SAMARADORLIK JIHATLARI
Internet of Things (IoT) texnologiyasining jadal rivojlanishi butun dunyo bo‘ylab ulangan qurilmalar sonini keskin oshirmoqda. So‘nggi statistik ma’lumotlarga ko‘ra, 2024 yilga kelib dunyoda 15 milliarddan ortiq IoT qurilmalari faoliyat ko‘rsatmoqda, bu raqam 2025 yilga borib 25 milliardga yetishi kutilmoqda. Bu o‘sish nafaqat texnologik taraqqiyotni, balki kiberxavfsizlik sohasida yangi muammolarni ham keltirib chiqarmoqda. IoT tizimlarining o‘ziga xos xususiyatlari – cheklangan protsessor quvvati, kam xotira, past energiya sarfi – an’anaviy kriptografik usullarni qo‘llashni qiyinlashtiradi. Ayniqsa, sog‘liqni saqlash, moliya, transport va energetika kabi muhim sohalarda IoT qurilmalarining ishonchliligi va xavfsizligi inson hayoti bilan bevosita bog‘liq bo‘lishi mumkin. Shu sababli, resurslar chegaralangan muhitda samarali ishlay oladigan kriptografik yechimlarni ishlab chiqish dolzarblik kasb etmoqda. Xesh funksiyalari IoT tizimlarida ma’lumotlar butunligini tekshirish, parollarni xavfsiz saqlash va raqamli imzolarni yaratishda asosiy vositadir. Biroq, turli xesh algoritmlarining xavfsizlik darajasi va samaradorligi turlicha bo‘lib, ularni maqsadli tanlash va qo‘llash talab etiladi. Ushbu maqolaning asosiy maqsadi IoT muhitida eng ko‘p qo‘llaniladigan xesh algoritmlarining tezlik, energiya sarfi va xavfsizlik ko‘rsatkichlarini qiyosiy tahlil qilish, shu asosida turli soha va talablar uchun optimal algoritmlarni tavsiya etishdan iborat.
Copyright (c) 2025 Allanov Orif Menglimuratovich, Saidahmedov Eldor Islomovich https://creativecommons.org/licenses/by/4.0
FORECASTING PERFORMANCE INDICATORS WITH THE HELP OF ARTIFICIAL INTELLIGENCE: TSUE PRACTICE
This article presents an AI-driven approach for predicting employee performance within the KPIup digital platform, designed to enhance staff evaluation processes in higher education institutions. Multidimensional HR data were analyzed and predictive models were developed using Gradient Boosting, Random Forest, and regression algorithms to forecast future employee productivity. Data preprocessing, normalization, and feature engineering significantly improved prediction accuracy. The results demonstrate that integrating AI analytics into the KPIup platform increases the speed and reliability of managerial decision-making, reduces subjectivity in performance evaluation, and enables full automation of HR processes.
Copyright (c) 2025 Shuhratov Mamurjon Shuhrat o‘g‘li https://creativecommons.org/licenses/by/4.0
AXBOROT XAVFSIZLIGIDA KO‘P AGENTLI TIZIMLARDAN FOYDALANISH AFZALLIKLARI
Maqolada raqamli texnologiyalar tez sur’atlar bilan rivojlanayotgan bugungi kunda axborot xavfsizligini ta’minlashda ko‘p agentli axborot xavfsizligi tizimlaridan foydalanishning nazariy asoslari, tarkibiy tuzilmasi va amaliy qo‘llanilish sohalari tahlil qilingan. Intellektual agentlar va ularning "zaif" hamda "kuchli" shakllari, xususiyatlar orqali axborot tizimlarida tahdidlarni aniqlash, monitoring qilish yoritilgan. Agentlar asosidagi yondashuvning IDPS, DDoS himoyasi, bulutli hisoblash va IoT tizimlaridagi rollari ko‘rsatilgan.
Copyright (c) 2025 Umarov Shuxratjon Azizjonovich https://creativecommons.org/licenses/by/4.0
SUN’IY INTELLEKT VA RAQAMLI BOSHQARUVNING TURIZM SOHASIDAGI AHAMIYATI
Bugungi kunda turizm sanoati sun’iy intellekt (AI) va raqamli texnologiyalar yordamida tubdan o‘zgarib bormoqda. Ushbu texnologiyalar sayohat tajribasini shaxsiylashtirish, xizmat ko‘rsatish sifatini oshirish va operatsion jarayonlarni optimallashtirish imkonini beradi. Raqamli turizm va AI qo‘llanilishi orqali bronlash, yo‘nalish tavsiyalari, narxni optimallashtirish va xavfsizlik monitoringi kabi jarayonlar avtomatlashtiriladi, natijada sayyohlar va korxonalarga qulayliklar yaratiladi. Shu bilan birga, AI va raqamli boshqaruv innovatsiyalarni rag‘batlantiradi va turizm sanoatining raqobatbardoshligini oshiradi.
Copyright (c) 2025 Husenova Madina Farxodovna https://creativecommons.org/licenses/by/4.0
KUTUBXONA TIZIMLARIDA FOYDALANUVCHILARGA AXBOROT EHTIYOJLARIGA KO‘RA RESURSLARNI TAVSIYA ETISH
Hozirda kutubxona tizimlari jamiyatning axborotga bo‘lgan ehtiyojlarini qondirishda muhim o‘rin tutadi hamda ular katta foydalanuvchilar auditoriyasiga ega. Ushbu tizimlar katta hajmdagi qimmatli axborot resurslarini o‘z ichiga olgan bo‘lib, foydalanuvchilarga zarur adabiyotlarni topishda yordam beruvchi qidiruv xizmatlarini ham taklif etadi. Qidiruv tizimlari foydalanuvchi so‘rovlarini qayta ishlaydi va mos deb topilgan natijalarni taqdim etadi. Biroq, bu jarayon natijasida foydalanuvchilar ko‘plab ma’lumotlar orasidan o‘zlariga eng mosini tanlashda qiyinchiliklarga duch kelishadi. Shu bois, zamonaviy axborot tizimlarida sun’iy intellektga asoslangan tavsiya etish tizimlari keng qo‘llanilib, foydalanuvchiga mos obyektlarni samarali tarzda taklif etish imkonini bermoqda. Kutubxona tizimlariga bunday tavsiya etish tizimi vositalarini joriy etish orqali foydalanuvchilarning axborot ehtiyojlarini aniqlash va ularga axborot ehtiyojiga ko‘ra resurslarni taqdim etish mumkin. Ushbu maqolada tavsiya etish tizimlari asosida foydalanuvchilarning axborot ehtiyojlarini aniqlash va resurslarni taqdim etishning nazariy asoslari hamda amaliy yondashuvlari tahlil qilinadi.
Copyright (c) 2025 Fayzi Bekkamov https://creativecommons.org/licenses/by/4.0
THEORETICAL FOUNDATIONS AND EFFICIENT ALGORITHMS FOR FEATURE DETECTION AND EXTRACTION
Feature detection and extraction play a critical role in numerous applications within artificial intelligence, computer vision, and data science. As datasets grow in complexity and volume, traditional feature extraction techniques often struggle to maintain efficiency and accuracy. This paper explores the theoretical foundations and efficient algorithms for feature detection and extraction, emphasizing the intersection of classical statistical methods and modern machine learning-based approaches. The study provides an in-depth analysis of key methodologies, including edge detection algorithms, deep learning-based feature extraction, and dimensionality reduction techniques such as Principal Component Analysis (PCA) and t-Distributed Stochastic Neighbor Embedding (t-SNE). Furthermore, the research highlights the challenges associated with computational efficiency, accuracy trade-offs, and real-time processing constraints. A critical gap in existing literature is identified—the disconnect between theoretical models and their practical implementations—which often hinders the optimization of feature extraction processes. By synthesizing current advancements and addressing these challenges, this paper aims to provide a comprehensive framework for improving feature detection and extraction methodologies, ultimately contributing to the development of more efficient and adaptable systems across various domains.
t-Distributed Stochastic Neighbor Embedding (t-SNE)
Copyright (c) 2025 Iskandarov Sanjar, Rakhmonova Ma’rifat https://creativecommons.org/licenses/by/4.0
R-FUNKTSIYALAR VA ITERATSION FUNKSIYALAR TIZIMI ASOSIDA KUB-TSILINDR KOMPOZITSIYASIDAN HOSIL BOʻLGAN 3D FRAKTAL STRUKTURALARNI GEOMETRIK MODELLASHTIRISH
Mazkur maqolada R-funktsiyalar va iteratsion funksiyalar tizimi (IFS) integratsiyasi asosida kub va tsilindr shakllarining analitik kombinatsiyasidan hosil bo‘lgan uch o‘lchovli fraktal struktura modellashtirilgan. Tadqiqotda kubning chegaralari va uchta o‘zaro perpendikulyar tsilindr tenglamalari R-funktsiyalar yordamida differensial tarzda birlashtirilib, natijada silliq sirtli va markaziy simmetriyaga ega fraktaldan avvalgi shakl (pred-fraktal) modeli hosil qilindi. So‘ngra ushbu shakl IFS yondashuvi orqali rekursiv takrorlanib, Menger tipidagi 3D fraktal strukturalar ketma-ket iteratsiyalar asosida yaratildi. Natijalar fraktal antennalar dizayni, 3D bosma texnologiyalar, grafik dizayn va matematik vizualizatsiya sohalarida amaliy qo‘llanish imkoniyatiga ega.
Copyright (c) 2025 Nuraliyev Faxriddin Murodillayevich, Narzulloyev Oybek Mirzayevich, Nurimbetov Baxbergen Tolibayevich https://creativecommons.org/licenses/by/4.0
COMPACT DIFFERENCE SCHEMES FOR THE ALLER-LYKOV MOISTURE TRANSFER EQUATION
The generalized Aller-Lykov equation is a mathematical model for many applied heat and moisture transfer problems. This includes nonstationary problems in hydrogeology, agrophysics, biology, ecology, and other fields, which are primarily solved using numerical methods. In this paper, high-precision difference schemes (compact schemes) are constructed and analyzed for the nonstationary generalized Aller-Lykov equation. The scheme is constructed in space and time using the finite difference method. The stability and convergence of the resulting difference scheme are proven, and accuracy estimates are obtained for a sufficiently smooth solution to the original differential problem. An implementation algorithm for the constructed scheme is proposed.
Copyright (c) 2025 Tleuov Kuatbay Orazbayevich https://creativecommons.org/licenses/by/4.0
TASVIR SIFATINI BAHOLASH KO‘RSATKICHLARI
Tasvirlash sohasidagi yutuqlar tez sur’atlar bilan davom etmoqda va bozorga yangi hamda ilg‘or mahsulotlar doimiy ravishda kirib kelmoqda. Yangi texnologiyalar joriy texnologiyaga nisbatan yuqori sifatli tasvirlarni ishlab chiqarishini tekshirish uchun sifatni baholashning samarali bir turi talab etiladi. Bu o‘z navbatida, tasvir sifatini baholash ko‘rsatkichlarini keng qamrovli tahlil qilish hamda ularni har biri bo‘yicha ma’lumotlarni bitta joyga yig‘ishni dolzarb masalaga aylantiradi. Shuning uchun, mazkur ishda tasvir sifatini baholash ko‘rsatkichlarini tarixi va tavsifi, ularni tasniflanishi haqida ma’lumotlar keltirilgan. Shuningdek, soha bo‘yicha mavjud adabiyotlar tahlili, tasvir sifatini baholash ko‘rsatkichlarini yutuq hamda kamchiliklari keltirilgan. Mazkur tadqiqot ishini maqsadi sifatni baholash ko‘rsatkichlarini alohida guruhlarga ajratish hamda ularni subyektiv idrok bilan mos kelishini baholashdan iborat. Bunday tasniflash ma’lum bir muammo yoki buzilish uchun eng mos keladigan sifatni baholash ko‘rsatkichini tanlashda foydali bo‘lishi mumkin.
Copyright (c) 2025 Mamatov Narzullo Solidjonovich, Jalelova Malika Moyatdin qizi, Jumayev Bobur Juma o‘g‘li https://creativecommons.org/licenses/by/4.0
TO‘LIQ BO‘LMAGAN MA’LUMOTLARGA ASOSLANGAN MURAKKAB TEXNIK TIZIMLARNING INTELLEKTUAL BOSHQARUV MODELLARINI ISHLAB CHIQISH
Ushbu maqolada to‘liq bo‘lmagan va noaniq ma’lumotlar sharoitida murakkab texnik tizimlarni samarali boshqarish masalalari ko‘rib chiqiladi. Tadqiqotning maqsadi – axborot yetishmovchiligi, o‘lchov xatoliklari va tashqi omillar ta’siri ostida ishlaydigan tizimlar uchun intellektual boshqaruv modellarini ishlab chiqish va ularning barqarorligini ta’minlashdir. Taklif etilgan yondashuv sun’iy intellekt, noaniqlik nazariyasi, hamda moslashuvchan boshqaruv usullariga asoslanadi. Maqolada ma’lumotlarni qayta ishlashning ehtimollik va neyron tarmoqlar asosidagi metodlari, shuningdek, qaror qabul qilishning gibrid algoritmlari tahlil qilinadi. Natijada, to‘liq bo‘lmagan ma’lumotlar sharoitida tizim holatini aniqlash, prognozlash va optimal boshqaruv signalini shakllantirish imkonini beruvchi model taklif etiladi. Tadqiqot natijalari avtomatlashtirilgan ishlab chiqarish, robototexnika, energetika va transport tizimlarida qo‘llash uchun amaliy ahamiyatga ega.
Copyright (c) 2025 Zayniddinov Bobirjon G‘ofirovich https://creativecommons.org/licenses/by/4.0
OʻZBEK TILIDAGI MATNLARNI TAHLIL QILISHDA NOMLI OBYEKTNI TANIB OLISH (NER) YONDASHUVI
Ushbu maqolada o‘zbek tilidagi matnlarni tahlil qilish jarayonida nomlangan obyektlarni tanib olish (Named Entity Recognition-NER) yondashuvlari har tomonlama tahlil qilingan. Tadqiqot doirasida NER tizimlarining nazariy asoslari, qoida asoslangan, mashinali o‘qitishga asoslangan hamda gibrid yondashuvlar batafsil ko‘rib chiqilgan va ularning afzalliklari hamda cheklovlari solishtirma tahlil asosida yoritilgan. Shuningdek, turli tillar va sohalarda, jumladan tibbiyot, oziq-ovqat va axborot tizimlarida qo‘llanilgan zamonaviy NER modellarining ishlash samaradorligi tahlil qilingan. Tadqiqot natijalari NER tizimlarining katta hajmdagi matnli ma’lumotlardan semantik axborotni avtomatik ajratib olishdagi ahamiyatini ko‘rsatadi hamda kam resursli tillar, xususan o‘zbek tili uchun moslashuvchan va samarali NER modellarini ishlab chiqish istiqbollarini belgilab beradi.
Copyright (c) 2025 Kenjayev Xamdam Bazarbayevich https://creativecommons.org/licenses/by/4.0
SELF-ADAPTING HYBRID EVOLUTIONARY ALGORITHM FOR TRANSPORT LOGISTICS OPTIMIZATION: DEVELOPMENT, STABILITY ANALYSIS AND PRACTICAL APPLICATION
This paper presents a self-adaptive hybrid evolutionary algorithm for solving transport route optimization problems in agricultural logistics. The developed algorithm combines differential evolution (DE), a genetic algorithm (GA), and variable-environment local search (VNS) with a self-adaptive parameter optimization (SaDE) mechanism. SaDE automatically adjusts key algorithm parameters based on their success in previous generations, ensuring an adaptive balance between global search and local exploitation. A comprehensive analysis of the algorithm's stability was conducted on problems of varying dimensions (from 50 to 200 orders). Experimental results demonstrate a 15-18% reduction in transportation costs compared to baseline heuristics with a variation coefficient of only 1.40%, which is 3.5 times better than a classical genetic algorithm. The high stability and reproducibility of the results confirm the algorithm's readiness for integration into real-world transport logistics management systems.
Copyright (c) 2025 Sulyukova Larisa Faritovna, Akhmedzhanova Zarrina Iskandarovna, Shamsiev Rasul Zairovich https://creativecommons.org/licenses/by/4.0
SELECTING THE OPTIMAL COMBINATION OF MEMBERSHIP FUNCTIONS FOR QUERIES BASED ON FUZZY LOGIC BY EVALUATING THE SENSITIVITY
Efficient request distribution is one of the important factors for the stable and uninterrupted operation of modern information systems. In the context of increasing cloud computing, real-time services and multi-user environments, traditional distribution algorithms (e.g. Round Robin, Least Connection, Weighted Response Time) are losing their effectiveness due to insufficient consideration of uncertainty and dynamic changes in server state. In this context, there is a growing need for decision-making systems based on adaptive and state-sensitive approaches. Fuzzy logic-based models have a significant advantage in representing uncertainties in real-world environments, especially due to the ability to estimate system parameters through linguistic variables. However, the effectiveness of these systems directly depends on the shape and parameters of the membership functions, i.e. their semantic relevance and sensitivity. Using membership functions of the same shape for each linguistic value (for example, only a triangle) can reduce the accuracy of the system's decision-making.
Copyright (c) 2025 Sanjar Sobirovich Kenjaev, Akmal Rustamovich Akhatov, Ma’ruf Ruziqulovich Tojiev https://creativecommons.org/licenses/by/4.0
MUHIM XUSUSIYATLARNI ANIQLASH MASALASIDA TAKOMILLASHTIRILGAN GROVER ALGORITMI
Ushbu maqolada katta oʻlchamli ma’lumotlar toʻplamlarida muhim xususiyatlarni aniqlash masalasini hal etish uchun kvant Grover algoritmiga asoslangan takomillashtirilgan yondashuv taklif etiladi. Klassik statistik va mashinali oʻqitish usullari koʻp sonli xususiyatlar bilan ishlashda yuqori hisoblash murakkabligiga duch kelishi sababli, kvant hisoblash imkoniyatlaridan foydalanish dolzarb hisoblanadi. Tadqiqotda xususiyatlar kombinatsiyalarini baholash uchun Pearson korrelyatsiyasiga asoslangan maqsad funksiyasi ishlab chiqilib, Monte Karlo usuli yordamida qidiruv fazosi qisqartiriladi. Grover algoritmi doirasida Oracle operatori shakllantirilib, optimal xususiyatlar toʻplamining amplitudalarini kuchaytirish mexanizmi amalga oshiriladi. Shuningdek, xususiyatlar oʻrtasidagi statistik bogʻliqliklar kvant chalkashlik operatorlari orqali modellashtiriladi. Taklif etilgan yondashuv hisoblash samaradorligini oshirish, ortiqcha xususiyatlarni kamaytirish hamda yashirin korrelyatsion munosabatlarni aniqlash imkonini beradi. Natijada, model uchun eng informatsion xususiyatlar ajratib olinib, keyingi bashoratlash bosqichlarining aniqligi va barqarorligi oshiriladi.
Copyright (c) 2025 Raximov Nodir Odilovich, Primqulov Oybek Dilmurot oʻgʻli https://creativecommons.org/licenses/by/4.0
TIBBIY MA’LUMOTLARNI TAHLIL QILISHNING INNOVATSION ALGORITMLARI ORQALI INSULTNI ERTA ANIQLASH
Ushbu maqolada ko‘p tipli ma’lumotlar — nominal va qiymatli belgilardan tashkil topgan o‘quv tanlanmalarni tahlil qilishda Fisher mezoniga asoslangan informativ belgilarning tanlanishi masalasi yoritiladi. Ma’lumotlarning turlicha tabiati tahlil jarayonini murakkablashtirishi sababli, sinflararo farqlanish va sinflar ichidagi o‘xshashlik darajasini aniqlash imkonini beruvchi funksional mezonlardan foydalanish muhim ahamiyatga ega. Shu nuqtai nazardan Fisher mezoni eng samarali yondashuvlardan biri sifatida ko‘rib chiqilib, optimal informativ belgilarning aniqlanishi bo‘yicha ikki bosqichli algoritm taklif etiladi. Birinchi bosqichda qiymatli va nominal belgilarga asoslangan optimizatsiya masalasi yordamida informativ vektorlar tanlanadi, ikkinchi bosqichda esa sinf obyektlarining o‘zaro o‘xshashlik darajasi hisoblanib, optimal kombinatsiya aniqlanadi. Taklif etilgan usul turli tipdagi ma’lumotlar fazosida samarali natijalar berishi va tahlil jarayonining aniqligini oshirishi bilan ahamiyatlidir.
Copyright (c) 2025 Aktamov Shohruhbek Ulug’bek o’g’li https://creativecommons.org/licenses/by/4.0
INTEGRATIVE MACHINE LEARNING AND NATURAL LANGUAGE PROCESSING FRAMEWORK FOR THE DETECTION AND CLASSIFICATION OF AI-GENERATED TEXTS IN ACADEMIC RESEARCH PUBLICATIONS
The rapid proliferation of large language models (LLMs) has transformed the landscape of academic writing and scientific communication, yet it has also raised serious concerns about the authenticity and integrity of scholarly publications. This study proposes an integrative Machine Learning (ML) and Natural Language Processing (NLP) framework designed to detect and classify AI-generated texts within academic research articles. The framework combines linguistic, semantic, and stylometric features extracted from large, open-source datasets collected from Kaggle and other academic repositories. The analytical pipeline—implemented and validated using Google Colab—integrates both supervised and deep transformer-based models such as Support Vector Machines (SVM), Random Forest, and fine-tuned BERT-derived architectures. Experimental results demonstrate that the ensemble classifier achieves an overall F1-score of 0.94 and exhibits strong generalization when applied to unseen AI-generated texts. The proposed model not only outperforms existing AI-text detectors in cross-domain evaluations but also remains robust against paraphrased and hybrid human-AI compositions. The findings contribute to the development of transparent and reliable tools for preserving academic integrity in the era of generative artificial intelligence.
Copyright (c) 2025 Mukhriddin Mukhiddinov, Kungratov Ilmurod Kuzibay ugli, Mirzarahmatov Shahzodbek Ulugbekovich, Sanakulov Otaniyoz Erjigit ugli https://creativecommons.org/licenses/by/4.0
QISHLOQ XO‘JALIGI MAHSULOTLARINI SAQLASHDA KO‘P BOSQICHLI ADAPTIV BOSHQARUV TIZIMI MODELI VA ALGORITMI
Ushbu maqolada ko‘p bosqichli o‘zgaruvchan parametrli ob’yektlar (KBO‘PO) uchun adaptiv boshqaruv modeli va algoritmi ishlab chiqilgan. Texnologik jarayonlar dekompozitsiya qilinib, ularning modellashtirish va boshqarish imkoniyatlari o‘rganilgan. Har bir boshqaruv bosqichiga mos matematik modellar va optimizatsiya usullari tanlangan. Model parametrlarini real sharoitlarga moslashtirish adaptiv identifikatsiya algoritmlari orqali amalga oshirilgan. Tadqiqot davomida kartoshka, bodring va pomidor mahsulotlarini saqlash jarayonlari bo‘yicha tajribalar o‘tkazilib, ularning asosida moslashuvchan matematik model tuzilgan. Modelning adekvatligi Fisher mezoni bilan baholanib, tuzatishlar kiritilgan. Tadqiqot natijalariga ko‘ra tez buziluvchi qishloq xo‘jaligi mahsulotlari omborlarida mikroiqlimni samarali boshqarish bo‘yicha amaliy tavsiyalar ishlab chiqilgan.
Copyright (c) 2025 Pirova Rashida Qudratovna https://creativecommons.org/licenses/by/4.0
YEYILISH DINAMIKASINI TADQIQ ETISHDA EMPERIK TENGLAMALARNI KELTIRIB CHIQARISHDA MAHSUS DASTURLARDAN FOYDALANISHNING ISTIQBOLLARI
Maqolada dala tajriba sinovlaridan olingan ma‘lumotlar asosida o‘qyoysimon panjaning еyilishi uchun emperik tenglamalarni keltirib chiqarilish uchun mahsus dasturdan foydalanish va bu usul bilan tadqiqot ishlari uchun vaqtni tejab, katta hajmda еyilishini o‘rganish tadqiqotlarini olib borish hamda ishonchli natijalar olish mumkinligi kеltirilgan.
Copyright (c) 2025 Kuvandikov Yokub Tursunbayevich, Kabilov Begzod Uktam o‘g‘li https://creativecommons.org/licenses/by/4.0
TURKIY TILLAR GRAMMATIKASINING KOMPYUTERGA YO‘NALTIRILGAN FORMAL MODELLARI TAHLILI
Ushbu maqolada turkiy tillar grammatikasini kompyuterli qayta ishlash uchun formal modellashtirish tamoyillari yoritiladi. Morfologik agglyutinatsiya, erkin affiksatsiya kabi xususiyatlarga ega turkiy tillar uchun grammatik qoidalarni algoritmlashtirishda qo‘llaniladigan kompyuterli modellar: qoidalarga asoslangan grammatikalar (Rule Based Grammars), statistik modellar (Statistical Models), neyron tarmoqlar (Neural Network Models), gibrid NLP (Hybrid NLP) arxitekturalari tahlil qilinadi.
Copyright (c) 2025 Norov Abdisait Muradovich https://creativecommons.org/licenses/by/4.0
DEVELOPMENT OF THE DIGITAL ECONOMY OF UZBEKISTAN: IMPLEMENTATION OF PERSONALIZED MONETIZATION MODELS BASED ON ARTIFICIAL INTELLIGENCE AS A TOOL FOR INCREASING REVENUE FOR LOCAL VIDEO GAME STUDIOS
This article examines the implementation of a personalized AI-based monetization system in the Uzbek gaming industry. Particular attention is paid to recommendation systems as a tool for increasing revenue for local studios and developing the digital economy. A review of international practices and their potential for adaptation to the local context is provided, along with proposals for integrating AI technologies into game development processes. Using analytical and survey methods, this study serves as a foundation for subsequent applied research and practical recommendations.
Copyright (c) 2025 Shvets Stanislav Fedorovich https://creativecommons.org/licenses/by/4.0
MODAL GRAPH TRANSFORMER FOR GROUP ENGAGEMENT IN ONLINE MULTIPARTY CONVERSATIONS
Online multiparty conversations such as virtual classrooms, remote collaboration meetings, and live discussions pose unique challenges for understanding group engagement. Traditional models typically rely on isolated participant features or unimodal data, failing to capture the rich, relational dynamics across modalities. In this work, we propose a novel approach that models group engagement as a dynamic, multimodal graph learning problem. Our framework introduces a Multimodal Graph Transformer (MGT) that combines audio-visual fusion with structure-aware graph attention. Each participant is represented as a graph node enriched with fused video and speech embeddings, while edges capture interaction intensity through gaze, turn-taking, and vocal overlap. To preserve graph structure, we incorporate hop-level positional encodings and restrict attention to top-k neighbors for scalable relational modeling. The architecture is designed to capture both localized interaction cues and global group dynamics without requiring pre-defined templates or scripted behavior. By integrating techniques from graph representation learning, multimodal attention, and social signal processing, our method offers a generalizable and theoretically grounded framework for engagement estimation in complex multiparty scenarios.
Copyright (c) 2025 Pirimqulova Zilola Avaz qizi, Hojiyev Sunatullo Nasridin o‘g‘li, Xo‘jamqulov Abdulaziz Xazrat o‘g‘li https://creativecommons.org/licenses/by/4.0
SSRE-ID: KO‘P OBYETLAR KUZATUVLARIDA TO‘LIQ O‘Z-O‘ZINI O‘QITISHGA ASOSLANGAN QAYTA IDENTIFIKATSIYALASH
Ko‘p obyektli kuzatish (Multi-object Tracking, MOT) kompyuter ko‘rishida eng murakkab masalalardan biri bo‘lib qolmoqda. Buning asosiy sabablari — obyektlarning bir-birini to‘sib qo‘yishi, keskin harakat trayektoriyalari, detektorlar keltirib chiqaradigan shovqinlar va vizual muhitning keskin farqlanishidir. So‘nggi yillarda Tracking-by-detection paradigmalarida yangi yutuqlar kuzatildi, ammo ular hanuzgacha, ayniqsa murakkab sahnalarda, obyektlarning identifikatsiya barqarorligini saqlashda muammolarga uchraydi.Ushbu tadqiqotda biz OCSORT algoritmi asosidagi assotsiatsiya mexanizmi bilan integratsiyalangan to‘liq o’z-o’zini o’qitishga asoslangan re-ID modelini taklif qilamiz. Avvalgi yondashuvlardan farqli ravishda, bizning tizim katta hajmdagi belgilangan ReID ma’lumotlar bazasiga tayanmaydi. Taklif etilgan metod MOT17 dagi belgilanmagan o‘quv ketma-ketliklaridan bevosita kuchli identifikatsiya embeddinglarini o‘rganadi. Bu jarayon videoketma-ketlik darajasidagi self-supervised kontrastiv o‘qitish orqali amalga oshirilib, vizual belgilar to‘silish, yoritilishning o‘zgarishi va pozalardagi farqlar mavjud bo‘lgan holatlarda ham barqaror bo‘lib qoladi.Bundan tashqari, biz geometrik IoU ko‘rsatkichlari va ko‘rinish o‘xshashligini birlashtiruvchi gibrid assotsiatsiya strategiyasini ishlab chiqdik. Bu yondashuv faqat harakat signallariga tayanuvchi kuzatuv tizimi(tracker)larning cheklovlarini bartaraf etishga imkon beradi. MOT17 ning barcha 21 ta ketma-ketligida o‘tkazilgan keng qamrovli tajribalar shuni ko‘rsatadiki, bizning tizim identifikatsiya izchilligida sezilarli yaxshilanishga erishadi. Bu ko‘rsatkichlar bir nechta mavjud supervised bazaviy modellardan yuqori natija beradi. Kod:https://github.com/zilolapirimqulova/SSL_MOT
COMPARATIVE ANALYSIS OF PHYSICAL DEVELOPMENT IN CHILDREN ENGAGED IN SPORTS AND PHYSICAL EDUCATION
This study explores the functional characteristics of the cardiovascular and pulmonary systems in children engaged in physical education and sports. It examines how different types of physical training influence heart rate, blood pressure, oxygen uptake, lung capacity, and ECG indicators. The research highlights physiological adaptations resulting from endurance, strength, and control group, providing insights into optimizing training programs and early diagnosis of potential cardiovascular and pulmonary conditions. Recommendations include regular ECG and spirometry screenings. The findings suggest that early engagement in structured physical activity significantly improves cardiovascular efficiency, pulmonary function and overall health
Copyright (c) 2025 Usmankhodjaeva Adibakhon Amirsaidovna, Rakhmatova Fotima Utkirovna https://creativecommons.org/licenses/by/4.0
TARMOQ MA’LUMOTLARIGA INTELLEKTUAL ISHLOV BERISH MODELLARI VA USULLARI
Ushbu ishda tarmoq ma’lumotlariga intellektual ishlov berishning zamonaviy modellari va usullari tahlil qilingan. Tarmoq ma’lumotlarining hajmi va murakkabligi ortib borayotgani sababli, ularni samarali tahlil qilish va qayta ishlash uchun sun’iy intellekt, mashinali o‘qitish hamda ma’lumotlar mayning (data mining) texnologiyalaridan foydalanish zarurligi asoslab berilgan. Tadqiqotda tarmoq ma’lumotlarining tuzilishi, ularning turlari (ijtimoiy tarmoqlar, kompyuter tarmoqlari, sensor tarmoqlari va boshqalar) hamda ma’lumot oqimlari bilan bog‘liq muammolar yoritilgan. Shuningdek, intellektual ishlov berishda qo‘llaniladigan asosiy usullar — klassifikatsiya, klasterlash, prognozlash, anomaliyalarni aniqlash va graf analizi usullarining afzalliklari va qo‘llanilish sohalari keltirilgan. Ishda shuningdek, neyron tarmoqlar, graf neyron tarmoqlari (GNN), konvolyutsion va rekurrent modellardan foydalanish imkoniyatlari hamda ularning tarmoq ma’lumotlari tahlilidagi samaradorligi tahlil qilingan. Natijada, intellektual ishlov berish usullari orqali tarmoq ma’lumotlaridan qimmatli ma’lumotlarni avtomatik tarzda ajratib olish, xavfsizlikni ta’minlash va boshqaruv qarorlarini qabul qilishni takomillashtirish mumkinligi ko‘rsatilgan.
Copyright (c) 2025 Mo‘minov Bahodir Boltayevich, Husanov Sherzod Abdimonnonovich https://creativecommons.org/licenses/by/4.0
O‘ZBEK TILIDAGI GAPLARNI PERIFRAZ QILISH AXBOROT TIZIMINING FUNKSIONAL JARAYONLARINI LOYIHALASHTIRISH
Hozirgi kunda biznes-jarayonlarni modellashtirish metodologiyalari va vositalari bir vaqtning o‘zida ilmiy tadqiqot yo‘nalishi va dasturiy ta’minot (DT) bozorining rivojlanayotgan sektoriga aylangan. Ushbu usullar axborot tizimlarining zaif tomonlarini aniqlash va ularni bartaraf etish uchun zarur choralarni taklif qilish uchun ishlatilishi mumkin. Ushbu maqolada o‘zbek tilida gaplarni perefraz qiluvchi axborot tizimining biznes-jarayonlari BPMN metadologiyasi yordamida shakllantirildi. Semantik analizator axborot tizimining umumiy biznes-jarayonidagi har bir jarayonda alohida to‘xtalib o‘tilgan. Tabiiy tildagi matnlarni perefraz qilishda foydalaniladigan yondashuvlar ko‘rib chiqilgan.
Copyright (c) 2025 Axmedova Xusniya Xusanovna https://creativecommons.org/licenses/by/4.0