DIGITAL TRANSFORMATION AND ARTIFICIAL INTELLIGENCE

PARAMETRIK DIZAYNGA ASOSLANGAN FRAKTAL ZARGARLIK BUYUMLARI

Shahzoda Anarova — 2024-10-28

Mazkur maqola innovatsion zargarlik buyumlari dizaynida qo‘llaniladigan kompyuterlashtirilgan dizayndagi parametrik modellashtirish xususiyatlaridan foydalanish, zargarlik buyumlari dizayni sohasida parametrik dizayndan foydalanishning muhimliligi va qiyosiy tahlil qilish orqali yangi g‘oyalarni yaratishga qaratilgan. Tahlil qilish uchun asosiy fikrlar quyidagilardan iborat: tabiatdagi parametrik naqshlar, fraktallar, Vonoroi diagrammalarining tabiat bilan bo‘g‘liqligi va ularning zargarlik buyumlarida qo‘llanilishining tasavvur holatidan tayyor model tasviri qanday bo‘lishi ko‘rib chiqiladi. Shuningdek, fraktalga asoslangan bilaguzuklar va kulonlarning rekursiv funksiyalar orqali geometrik modelini vizuallashtirish va yangi texnologiyalardan foydalanish zargarlik buyumlari dizayniga, shuningdek fraktal taqinchoqlar ko‘rinishiga ta’siri, parametrik texnologiyaga asoslangan yangi dizaynlarni ishlab chiqishni muhokama qiladi.

CHACHA20 OQIMLI SHIFRLASH ALGORITMINING RAUND FUNKSIYASINING MODIFIKATSIYA VARIANTLARINI BAHOLASH

Ilhom Rahmatullayev — 2024-10-28

Ushbu maqolada ChaCha20 shifrlash algoritmining modifikatsiya qilingan variantlari tasodifiylik bo‘yicha baholangan va NIST statistik testlari yordamida tahlil qilingan. Algoritmning raund funksiyasidagi akslantirishlarni o‘zgartirish orqali hosil qilingan kalit oqimlari bir nechta yaxshi natijalarga ega bo‘ldi. Shuningdek, ba'zi variantlar original ChaCha20 algoritmiga nisbatan tezlik va tasodifiylik nuqtai nazaridan yuqori ko‘rsatkichlarni qayd etdi. Xususan, 2, 3, 4-variantlar NIST testlaridan muvaffaqiyatli o‘tib, original algoritm bilan teng tezlikda ishlagan bo‘lsa, 6, 8, 10, 13 va 15-variantlar tezlik va tasodifiylik shartlari bo‘yicha ham yaxshi natijalar ko‘rsatdi. Kelgusi tadqiqotlarda akslantirishlar ta’sirini chuqurroq o‘rganish va o‘tmagan testlar uchun qo‘shimcha tahlillar o‘tkazish rejalashtirilgan.

PREDICTION THE YIELD OF GRAIN CROPS USING BASIC MACHINE LEARNING ALGORITHMS

Dilmurod Khasanov — 2024-10-28

This article presents the development of an AI model and a software tool designed to predict the yield of grain crops using Machine Learning (ML) algorithms and a dataset from kaggle.com. The research focuses on analyzing a variety of environmental, climatic, and agricultural factors that influence crop productivity. By leveraging regression techniques, the model aims to provide accurate yield forecasts based on historical data and real-time inputs. The software tool developed offers a user-friendly interface for farmers and agricultural professionals, enabling them to make informed decisions regarding resource management, crop selection, and harvest planning. The model effectiveness is evaluated through empirical testing such as Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), Mean Squared Error (MSE) highlighting its potential for improving agricultural efficiency and food security.

ADVANCEMENTS IN IMAGE QUALITY ASSESSMENT: A COMPREHENSIVE SURVEY

Abdulazizxon Axmedov — 2024-10-28

Image 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.

"AUTOMATING ECONOMIC ISSUES: THE EXAMPLE OF AUTOMATING WAREHOUSE PRODUCT STORAGE ACCOUNTING AND ITS SOFTWARE"

Dostonbek Abduraimov — 2024-10-28

The automation of warehouse product storage accounting represents a transformative approach to addressing economic issues within businesses. By implementing specialized software, companies can streamline inventory management, reduce operational costs, and enhance overall efficiency. This automation facilitates real-time tracking, minimizes human error, and allows for timely decision-making based on accurate data analysis. As a result, businesses are better equipped to respond to market fluctuations and customer demands, leading to improved profitability and competitiveness. Ultimately, automating these processes not only optimizes individual operations but also contributes to the stability and growth of the broader economic landscape.

APPLICATION OF STRUCTURAL SIMILARITY INDEX FOR ANALYSIS OF X-RAY IMAGES OF INSPECTION-INSPECTION COMPLEXES OF CUSTOMS CONTROL

A.I. Dusmukhamedov — 2024-10-28

This article discusses a method for analyzing X-ray images obtained using Inspection and Examination Complexes using digital transformation algorithms. The relevance of the topic is due to the need to increase the efficiency of control over the transit of goods at the border. The structural similarity index is evaluated as a method for detecting anomalies in X-ray images.

MASHINALI O‘QITISH ALGORITMLARI YORDAMIDA HUJJATLARNI TASNIFLASH USULLARI TAHLILI

Xamdam Kenjayev — 2024-10-28

Matnli hujjatning avtomatik tasnifi onlayn matnli ma’lumotlarning payda bo‘lganidan beri matn tahlili sohasidagi tadqiqotda ahamiyatli hisoblanadi. Raqamli kutubxonalar, elektron pochtalar, bloglar va boshqalar kabi manbalar raqamli davrda matnli hujjatlarning tez o‘sishini ta’minlaydi. Umuman olganda, matnli hujjatning toifalari ma’lumot olish, mashinani o‘qitish va tabiiy tilni qayta ishlash kabi bir qancha sohalarini o‘z ichiga oladi. Ushbu maqola matnli hujjatlar to‘plamini oldindan belgilangan toifa belgilariga tasniflash uchun ham nazorat ostida va nazoratsiz mashinali o‘qitish usullaridan foydalanadigan tadqiqotlar tahlil qilingan.

НУТҚ СИГНАЛЛАРИДАН ШОВҚИНЛАРНИ БАРТАРАФ ЭТИШ ВОСИТАЛАРИ

Нилуфар Ниёзматова — 2024-10-28

Мазкур тадқиқот иши нутқ сигналларини қайта ишлашни замонавий технологиялари, хусусан, шовқин пасайтириш воситалари таҳлили ҳамда аудио маълумотлар сифатини яхшилашда улардан фойдаланишга бағишланган. Сўнгги ўн йилликларда нейрон тармоқ ва машинали ўқитиш усулларини жорий этиш орқали нутқни аниқлаш ҳамда шовқин пасайтириш тизимлари сезиларли даражада ривожланди. Мазкур ишда аудио ва видео маълумотлардаги фон шовқинларини пасайтиришда фойдаланиладиган сунъий интеллект технологияларига асосланган Adobe Enhance Speech, Audio Enhancer, Veed.io, Audio Tool Set, AI Costic, UniConverter, Noise Blocker каби платформа ва дастурий маҳсулотлар ўрганилиб, дастурий маҳсулотлар имкониятлари, қўллаб-қувватлаш форматлари ва платформалари асосида қиёсий таҳлиллар амалга оширилган. Бунда асосий эътибор аудио ва видео маълумотларни ёзиб олишда нутқни идрок этишга халақит берувчи шовқинларни бартараф этиш воситаларига қаратилган.