OJS Tashkent State University of Economics
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SONLARNI KO‘PAYTIRISH QOIDASIGA ASOSLANGAN FAKTORIZATSIYALASH ALGORITMI
No full textUshbu maqolada faktorizatsiya masalasini hal qilish uchun katta sonlarni ko‘paytirishning Tom-Kuk algoritmi qadamlariga asoslangan xotira va hisoblash imkoniyatiga qarab o‘zgarishi mumkin bo‘lgan 200 bitli sonni faktorlash imkoniyatini beruvchi yechim taklif qilindi. Tajribalar katta sonlarni oson va tez ko‘paytirishning Shenhag-Shtrassenning ko‘phadlar va Fur’ye almashtirishlariga asoslangan algoritmi, Fyurer algoritmi, Shenhag-Shtrassenning modul arifmetikasiga asoslangan ikkinchi algoritmi, Karatsuba algoritmi kabi ko‘plab algoritmlaridan foydalanganda ham faktorizatsiya masalasini samaraliroq hal qilish mumkinligini ko‘rsatdi. O‘tkazilgan tajribalar natijalari haqida keyingi maqolalarda batafsil to‘xtalib o‘tish ko‘zda tutilgan
MINTAQADA TURIZM HUNARMANDCHILIGINI RIVOJLANTIRISHNING DESTINATSION YONDASHUVI
No full textUshbu maqolada Qashqadaryo viloyati Jahonda global o‘zgarishlar tendensiyalari so‘nggi davrda turizm hunarmandchiligi sohasining yaratilayotgan yalpi ichki mahsulot tarkibidagi o‘sish sur’ati ortib borayotganligini ko‘rsatmoqda. Turizm hunarmandchilikni rivojlantirish bilan bog‘liq omillar turizm faoliyatini kengaytirish hamda uni boshqarish jarayonlarini takomillashtirish orqali iqtisodiy taraqqiyotni ta’minlash zarurati yoritilgan
ADVANCEMENTS IN IMAGE QUALITY ASSESSMENT: A COMPREHENSIVE SURVEY
No full textImage Quality Assessment (IQA) plays a critical role in ensuring the effectiveness of various image-based applications, including medical imaging, autonomous driving, entertainment media, and more. This work offers an in-depth assessment of IQA techniques, emphasizing the improvements in no-reference (NR) techniques generated by deep learning while also highlighting conventional full-reference (FR) and reduced-reference (RR) models. The survey includes a comparison of metrics, datasets, and methods used for both synthetic and real-world images. We discuss the difficulties when evaluating IQA models and offer ideas for future study, with a focus on addressing a variety of distortions, including aspects of human perception and resolving data scarcity problems through semi-supervised learning
VIRTUAL REALLIK TEXNOLOGIYASIDAN FOYDALANIB E-AVTOMAKTAB TIZIMI UCHUN 3D MODELLARNI MODELLASHTIRISH
No full textUshbu maqola, virtual texnologiyalar va 3D modellashtirishning e-avtomaktab tizimida qo‘llanishini tahlil qiladi. Tadqiqotning maqsadi-avtomobil boshqarish jarayonini o‘rganishda yangi, innovatsion yondashuvni yaratish bo‘lib, bunda virtual haqiqat (VR) texnologiyalari va 3D modellashtirishning integratsiyasi orqali o‘quvchilarga xavfsiz muhitda avtomobilni boshqarish imkoniyati yaratildi. Unreal Engine va 3D Max dasturlari yordamida ishlab chiqilgan modellar o‘quvchilarga nazariy bilimlarni amaliy ko‘nikmalar bilan mustahkamlashga yordam berdi.
Tadqiqot davomida olingan natijalar, 3D modellashtirish va VR texnologiyalarining ta’lim jarayonidagi samaradorligini isbotladi. Tizim yordamida o‘quvchilar interaktiv tarzda avtomobil boshqarishni o‘rganishdi, bu esa ta’limni yanada samarali va qiziqarli qilishga xizmat qildi. Biroq, tizimni yanada takomillashtirish uchun texnik resurslar va foydalanuvchi interfeysini optimallashtirish zarurati mavjud. Kelajakda, ushbu tizimni kengaytirish va yangi interaktiv elementlar qo‘shish orqali ta’lim samaradorligini yanada oshirish mumkin.
Maqola, virtual va 3D modellashtirish texnologiyalarining ta’lim jarayonidagi imkoniyatlarini ko‘rsatib, avtomobil boshqarish bo‘yicha samarali o‘quv muhitini yaratishda ularning qo‘llanilishini yanada kengaytirishni taklif qiladi
TEXT MINING AND SENTIMENT ANALYSIS FOR UZBEK: EVALUATING SVM AND NAIVE BAYES FOR UNDER-RESOURCED LANGUAGE PROCESSING
No full textThis study explores the application of text mining techniques to classify and analyze Uzbek text, focusing on the performance of Support Vector Machine (SVM) and Naive Bayes algorithms. Due to the unique linguistic structure of Uzbek, an under-resourced language with an agglutinative morphology and dual-script usage (Cyrillic and Latin), text mining presents several challenges. We collected a dataset from various Uzbek text sources, including news articles and social media posts, and applied customized preprocessing steps such as script normalization, tokenization, and stop word removal.The processed text was represented using Term Frequency-Inverse Document Frequency (TF-IDF) features with n-grams to capture contextual nuances. Both SVM and Naive Bayes classifiers were trained on the dataset and evaluated using accuracy, precision, recall, and F1-score metrics. SVM demonstrated higher accuracy and precision, making it well-suited for tasks requiring specificity, while Naive Bayes showed robustness in recall, effectively capturing diverse linguistic patterns.Our findings indicate that both models, with tailored preprocessing, can effectively handle Uzbek’s morphological and syntactic features. However, each model has distinct strengths: SVM excels in handling high-dimensional, well-preprocessed data, while Naive Bayes is more resilient to informal and morphologically diverse language contexts. Future research directions include integrating ensemble models, exploring deep learning approaches, and expanding resources for Uzbek NLP to improve model accuracy and adaptability further. This work contributes to advancing natural language processing for under-resourced languages, providing insights into efficient text mining techniques for Uzbek
TABIIY TILNING STATISTIK MODELLARI
No full textTabiiy tilning statistik modeli (Statistical Language Model, SLM) – tabiiy tilni qayta ishlashda qo‘llaniladigan zamonaviy vosita bo‘lib, u ma’lum tildagi so‘zlar ketma-ketligi ehtimolini bashorat qilishga qaratilgan. SLM asosida gapdagi muayyan ketma-ketlikdan keyingi so‘z bashorat qilinadi. SLM so‘zlarning tabiiy til ma’lumotlari korpusida paydo bo‘lishiga asoslangan ketma-ketlik ehtimolini hisobga oladi. Katta hajmdagi matn ma’lumotlarini tahlil qilish orqali model so‘zlarning tilda qanday qo‘llanilishi qoliplarini o‘rganishi va ushbu qoliplar asosida keyingi ehtimoli yuqori so‘zni bashorat qilishi mumkin. NLP sohasi rivojlanishda davom etar ekan, statistik til modellari tilni tushunish va qayta ishlash uchun asosiy vosita bo‘lib hisoblanadi. SLMlar yordamida tabiiy til texnologiyasida mumkin bo‘lgan chegaralarni kengaytirishni davom ettirishimiz va yanada innovatsion va kuchli NLP ilovalarni yaratishimiz mumkin. Ushbu maqolada tabiiy tilning statistik modellaridan hiosblangan N-gram modelini o‘zbek tili korpusi asosida ishlab chiqish usullari keltiriladi. Shuningdek, N-gram modellarining matematik tavsifi va baholash usullari hamda umumlashtirish, sezgirlik, OOV (noma’lum so‘zlar), maxsus kontekst muammolari va ularni bartaraf qilish yo‘llari keltiriladi
TA’LIM TIZIMIDA DASTURLASH ASOSLARINI O‘QITISHNING RIVOJLANISH TARIXI
No full textMaqola maktablarda dasturlashni o‘qitishning rivojlanish tarixi va bu jarayonning hozirgi zamon ta\u27lim tizimlaridagi ahamiyatini o‘rganadi. Dasturlashni maktablarda o‘rgatishning dastlabki bosqichlari, rivojlanish tendensiyalari, yangi pedagogik yondashuvlar va texnologiyalarni joriy etish haqida batafsil ma\u27lumot beriladi. Maktab ta\u27limi tizimida dasturlashni o‘rgatish nafaqat kompyuter texnologiyalari bilan bog‘liq, balki yoshlarning analitik fikrlash, kreativlik va muammolarni hal qilish ko‘nikmalarini rivojlantiradi. Ushbu maqolada dasturlashni o‘rgatishda qo‘llaniladigan metodikalar, pedagogik yondashuvlar va ulardan olinadigan natijalar muhokama qilinadi
COMPARATIVE ANALYZING FEATURES SELECTION METHODS FOR DATA MINING TASKS
No full textThis paper discusses feature selection methods for data mining and machine learning, focusing on three main approaches: wrapper, filter, and hybrid methods. These techniques help reduce dimensionality, improve computational efficiency, and enhance model accuracy by selecting the most relevant features and eliminating unnecessary data. Additionally, the paper presents a software tool designed to facilitate the feature selection process, utilizing the Java Data Mining API for efficient and scalable implementation. The software allows users to process large datasets and apply different feature selection techniques based on specific requirements. The paper also outlines the steps involved in the feature selection process, providing insights into its practical application. By combining a review of feature selection methods with a practical software solution, this study aims to assist researchers and practitioners in selecting the most suitable techniques for data preprocessing in machine learning. The findings contribute to improving model performance and optimizing computational resources, making machine learning applications more effective and efficient
THE DEVELOPMENT STRATEGY AND IMPORTANCE OF ONLINE EDUCATION SYSTEM
No full textThis article analyzes the advantages and challenges of traditional and online education, exploring key aspects such as the flexibility, cost-effectiveness, and significance of online education during the pandemic. Online learning allows students and teachers to set individual learning paces, expands remote teaching opportunities, and enables effective use of time and resources. The article also discusses the problems encountered in online education and strategies for overcoming them, including the need to enhance digital literacy, support teachers, and develop the infrastructure of educational institutions
EXPERIENCE OF FOREIGN COUNTRIES IN APPLICATION OF AI INSTRUMENTS TO ENSURE THE ECONOMIC SECURITY OF INDUSTRIAL ENTERPRISES
No full textThe article examines the role of Artificial intelligence (AI) as an increasingly important tool for ensuring the economic security of industrial enterprises. Analyzed the level of development of AI in Uzbekistan, highlighted governmental strategy and support in this area. Furthermore, experience of foreign developed countries in using AI instruments in ensuring economic security of industrial enterprises, such as USA, China and Japan were analyzed