OJS Tashkent State University of Economics
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MODERNIZATION OF THE EDUCATION SYSTEM IN THE CONTEXT OF DIGITAL TRANSFORMATION: ARTIFICIAL INTELLIGENCE, INNOVATIVE TECHNOLOGIES, AND CREATIVE APPROACHES
No full textThis article examines current trends in the digital transformation of education, the impact of artificial intelligence and innovative technologies on the educational process, and the training of new types of personnel. Particular attention is paid to the integration of AI into Uzbekistan\u27s education system, the development of digital competencies, and the fostering of creative thinking in students. The need for a balance between technological progress and humanistic values in the educational process is emphasized, and key areas for modernizing the educational environment are outlined
TIBBIYOTDA ENDOKRIN KASALLIKLARI PROFILAKTIKASINI AMALGA OSHIRISHDA MASHINALI O‘QITISHGA ASOSLANGAN YONDASHUV
No full textMaqolada tibbiyotda endokrin kasalliklari profilaktikasini amalga oshirishda mashinali o‘qitishga asoslangan yondashuvlar to‘liq ochib berilgan. Ma’lumotlarni yig’ish, maqsadni aniqlab olish, dastlab endokrin kasalliklarini profilaktika bosqichlarini mashinali o‘qishga moslashtirish yondashuvi ishlab chiqildi. Ma’lumotlarga ishlov berishning ananaviy va mashinali o‘qitish usul va algoritmlariga dastlabki ishlov berish, ma’loumotlarga ishlov berishning ananaviy va mashinali o‘qitish usul va algoritmlari tahlil qilindi. Mashinali o‘qitish algoritmlari orqali katta hajmdagi tibbiy ma’lumotlar tahlil qilinib, xavf omillari aniqlanadi va kasalliklarning erta bosqichlarini bashorat qilish imkoniyati yaratiladi. Ushbu texnologiyalar tibbiy diagnostika jarayonini optimallashtirish, bemorlar uchun individual profilaktik tavsiyalar ishlab chiqish hamda sog‘liqni saqlash tizimida resurslardan samarali foydalanishga yordam beradi. Shuningdek, maqolada sun’iy intellekt va mashinali o‘qitish usullari asosida ishlab chiqilgan innovatsion profilaktika dasturlarining afzalliklari va qo‘llanilish istiqbollari ko‘rib chiqiladi. Shu bilan birga, bunday tizimlarni amaliyotga joriy etishning muammolari va cheklovlari ham tahlil qilinadi. Natijalar shuni ko‘rsatadiki, mashinali o‘qitish tibbiyotda, xususan, endokrin kasalliklar profilaktikasida samarali vosita sifatida xizmat qilishi mumkin
РАҚАМЛИ ИҚТИСОДИЁТДА КОМПЬЮТЕР ЖИНОЯТЛАРИНИ БАРТАРАФ ЭТИШ АЛГОРИТМЛАРИНИНГ ТАДҚИҚИ
No full textУшбу мақолада рақамли иқтисодиёт шароитида компьютер жиноятларининг олдини олиш ва уларни бартараф этишга қаратилган замонавий усуллар таҳлили ўтказилган. Тармоқ ҳужумларини аниқлаш ва олдини олишда сунъий интеллект ҳамда дата мининг усулларидан фойдаланиш имкониятлари ёритилган. Асосан, Таянч вектор усули (SVM) ва энтропияга асосланган алгоритмлар ёрдамида тармоқ таҳлилининг самарадорлиги ўрганилади. Шунингдек, нейрон тармоқлар, генетик алгоритмлар, к-меанс каби усулларнинг қўлланилиши ва уларнинг самарадорлик даражаси таққосланади. Тадқиқот натижалари шуни кўрсатдики, энтропия ва SVM алгоритмларининг комбинацияланган ҳолда ишлатилиши таҳдидларни янада аниқроқ аниқлаш имконини беради. Шу билан бирга, компьютер жиноятларининг турлари, уларнинг иқтисодиётга таъсири ва замонавий таҳдидларга қарши самарали алгоритмлар тадқиқ қилинган
ПРОГНОЗИРОВАНИЕ ПОТРЕБНОСТИ РЕГИОНА В КАДРАХ С ПРОФЕССИОНАЛЬНЫМ ОБРАЗОВАНИЕМ
No full textВ статье обобщены теоретические подходы к систематизации факторов, определяющих соотношение спроса и предложения на рынках труда различных структурных уровней; конкретизировано значение экономических, социально-демографических, организационно-технических, институциональных и ресурсных факторов. Выделены ключевые факторы, определяющие спрос и предложение на рынке труда региона, и соответствующие им показатели (прирост валового регионального продукта, прирост численности населения региона, инвестиции в экономику региона и др.).
Систематизированы методические подходы к оценке и прогнозированию потребности в кадрах, выявлены основные недостатки и возможности использования различных групп методик для решения задачи прогнозирования потребности региона в кадрах, обоснована необходимость проведения мониторинга потребности в кадрах на уровне региона с целью улучшения качества получаемых прогнозных оценок, и разработано соответствующее методическое обеспечение
THE IMPACT OF VIDEO-ON-DEMAND SERVICES ON DIGITAL PIRACY: EXPLORING USER SATISFACTION AND SYSTEM FEATURES
No full textThis study explores how the emergence of legal distribution channels affects digital piracy. We use online video-on-demand services as a proxy for legal distribution channels and investigate how increased satisfaction with this service influences the consumption of pirated content. Data for this study were collected through an online survey of a representative sample of 332 participants and analyzed using the Structural Equation Modeling approach. The findings indicate that greater use of subscription-based video-on-demand services significantly reduces the consumption of pirated video content. Additionally, system features such as content richness, system quality, and recommendation mechanisms are crucial in shaping user satisfaction with such services. Based on these findings, this study recommends streaming service providers and policymakers to further promote legal digital consumption and reduce piracy
INNOVATIONS IN NEURAL NETWORKS AND THEIR IMAGE RECOGNITION
No full textLeukocytes (white blood cells) play a significant role in the processes of blood cell identification and classification. This study considers the application of neural network algorithms for the automatic analysis of blood cell microscopic samples. This method especially focuses on identifying chromatic, geometric, and textural features of leukocytes. An advanced solution is provided for early diagnosing blood Related diseases through image processing and artificial intelligence. Automated systems allow not only to shorten the analysis period but also to reduce the number of errors made by experts. The purpose of this work is to create an efficient neural network algorithm for classification of blood cells samples in order to help in early detection of diseases related to leukocytes. In this study, the efficiency of training models with different architectures of neural networks, namely Recurrent Neural Networks (RNN), ResNet (Residual Network) and Convolutional Neural Networks (CNN), was compared by using different parameters
THE IMPORTANCE OF DATA INTEGRITY VERIFICATION METHODS IN CLOUD COMPUTING SYSTEMS
No full textWith the rapid expansion of cloud computing, ensuring data integrity has become a critical concern for organizations and individuals relying on cloud-based services. Despite trust in cloud providers, risks such as data modification, corruption, and unauthorized access remain prevalent. This paper explores essential data integrity verification methods, including cryptographic protection, third-party auditing, and blockchain-based approaches. Cryptographic hash functions like SHA-256 provide enhanced security by detecting unauthorized alterations, while Zero Trust Architecture (ZTA) principles enforce continuous verification of data authenticity. Additionally, third-party auditors play a crucial role in independently assessing data integrity, mitigating concerns about service provider reliability. Infrastructure-as-a-Service (IaaS) models pose further challenges, as users often lack direct control over security measures. However, implementing advanced techniques such as secret sharing and homomorphic encryption enhances privacy and data protection in cloud environments. Ensuring robust verification methods fosters user trust, encourages cloud adoption, and safeguards critical information from potential cyber threats. This study highlights the importance of integrating comprehensive data integrity mechanisms to maintain the accuracy, consistency, and reliability of cloud-stored data. By strengthening security frameworks, organizations can confidently leverage cloud computing while mitigating risks associated with data breaches and integrity violations
DEVELOPMENT OF SYNTHETIC ACCELEROGRAMS AND BASELINE CORRECTION METHODS FOR DETERMINISTIC MODELING OF SEISMIC WAVE PROPAGATION
No full textThe article focuses on seismic excitation models and methods for generating and processing seismic records. Synthetic accelerograms were developed using probabilistic and stochastic methods following Eurocode 8 standards. The study addresses the correction of baseline errors in accelerograms to ensure accurate velocity and displacement values. Fourier Transform methods are applied to analyze temporal responses.
A proposed model incorporates P and S waves with varying incidence angles and amplitudes to assess their effects on structures. The article also presents a method for calculating transfer functions and transitioning between time and frequency domains. The work enhances seismic analysis accuracy and contributes to safer structural design in earthquake-prone areas
O‘ZBEK TILI MATNLARINI NAIVE BAYES USULI ASOSIDA SENTIMENT TAHLIL QILISH
No full textUshbu maqolada o‘zbek tilidagi matnlarni sentiment tahlil qilishda Naive Bayes (NB) usulining samaradorligi va cheklovlari tadqiq qilindi. Tadqiqotning asosiy maqsadi matnlarning hissiy ohangini (ijobiy, salbiy yoki neytral) aniqlash uchun Naive Bayes modelini qo‘llash va uning samaradorligini baholashdan iborat. O‘zbek tili milliy korpusidan olingan matnlar to‘plami matnlar tozalash, tokenizatsiya va chastota vektorlariga aylantirish bosqichlaridan o‘tkazilib, model uchun tayyorlandi. TF-IDF vektorizatsiyasi asosida qurilgan model 4,000 ta ijtimoiy tarmoq sharhlaridan iborat dataset yordamida o‘qitilib, 75.47% aniqlik (accuracy) natijasiga erishdi. Modelning aniqligi va F1-score ko‘rsatkichlari asosida baholangan natijalar oddiy va qisqa matnlarda yuqori samaradorlikni ko‘rsatdi. O‘zbek tiliga xos morfologik murakkabliklar (masalan, so‘z qo‘shimchalari, izohlovchi shakllar) modelning baʼzi murakkab iboralarni noto‘g‘ri talqin qilishiga sabab bo‘lishi aniqlandi. Qiyosiy tahlil shuni ko‘rsatdiki, NB Logistik Regressiyaga nisbatan 7% pastroq, lekin Decision Treesga nisbatan 15% tezroq ishlaydi. Matnni boshlang`ich ishlash (nomuhim so‘zlarni olib tashlash, kichik harflarga o‘tkazish) bilan modelning ishonchliligi 5% ga oshirildi. Biroq, murakkab sintaksis va kontekstga bog‘liq sentimentlarni tahlil qilishda modelning cheklovlari aniqlandi. Tadqiqot o‘zbek tili uchun sentiment tahlilining rivojlanishiga hissa qo‘shadi va usulning boshqa tillardagi modellar bilan taqqoslash imkonini beradi. Tadqiqot shuningdek, NBning mustaqillik gipotezasi tufayli so‘zlar o‘rtasidagi bog‘liqlikni eʼtiborsiz qoldirishi kabi cheklovlarini taʼkidlaydi. Kelajakda n-gram modellari va kontekstni hisobga oluvchi yondashuvlar bilan ushbu cheklovlarni bartaraf etish mumkinligi ko‘rsatilgan. Maqola yakunida NB usulining mijozlar sharhlarini tahlil qilish, ijtimoiy media monitoringi va taʼlim sohasidagi qisqa matnlarni baholash kabi amaliy dasturlarda qo‘llanilishi tavsiya etiladi
COWRIE HONEYPOT LOGLARI VA BELGILARI ASOSIDA HUJUMLARNI ANIQLASH HAMDA TAHLIL QILISH
No full textBrute force hujumi kompyuter tizimiga ruxsatsiz kirish uchun hali ham keng qo‘llaniladigan hujumlardan biridir. Brute force, shuningdek, eng xavfli hujum hisoblanib, tizimning nazoratdan chiqishi katta xavf tug‘diradi. Brute force hujumlarini tekshirish kuchli kompyuter tarmoq himoya tizimlarini qurish uchun foydalidir. Ushbu tadqiqotda Snort intrusiyani oldini olish tizimi sifatida, Cowrie Honeypot esa Brute force hujumi sodir bo‘lganda paydo bo‘ladigan anomalliklarni tekshirish vositasi sifatida ishlatilgan. Ushbu tadqiqotning maqsadi Cowrie Honeypot loglarini tekshirish natijalariga asoslanib, Snort qoida belgilarining Brute force hujumlariga qarshi samaradorligini oshirishdan iborat. Olingan natijalarga ko‘ra, Snort qoida belgilari aniqlash qobiliyatini muvaffaqiyatli yaxshiladi va bir xil paketni moslashtirish uchun qisqa ishlash vaqtini talab qiladi: Hydra hujumida 3,5 mikrosekund, Medusa hujumida 3,8 mikrosekund va Ncrack hujumida 2,3 mikrosekund