DIGITAL TRANSFORMATION AND ARTIFICIAL INTELLIGENCE
type: channel
description: The Journal is a publication specializing in the field of digital economy and information technologies. Raqamli transformatsiya va sun’iy intellekt ilmiy jurnali
publisher: Tashkent State University of Economics
language: en
publication name: DIGITAL TRANSFORMATION AND ARTIFICIAL INTELLIGENCE
issn: 3030-3346
items: TIBBIY MA’LUMOTLARNI TAHLIL QILISHNING INNOVATSION ALGORITMLARI ORQALI INSULTNI ERTA ANIQLASH
items: INTEGRATIVE MACHINE LEARNING AND NATURAL LANGUAGE PROCESSING FRAMEWORK FOR THE DETECTION AND CLASSIFICATION OF AI-GENERATED TEXTS IN ACADEMIC RESEARCH PUBLICATIONS
items: QISHLOQ XO‘JALIGI MAHSULOTLARINI SAQLASHDA KO‘P BOSQICHLI ADAPTIV BOSHQARUV TIZIMI MODELI VA ALGORITMI
items: YEYILISH DINAMIKASINI TADQIQ ETISHDA EMPERIK TENGLAMALARNI KELTIRIB CHIQARISHDA MAHSUS DASTURLARDAN FOYDALANISHNING ISTIQBOLLARI
items: TURKIY TILLAR GRAMMATIKASINING KOMPYUTERGA YO‘NALTIRILGAN FORMAL MODELLARI TAHLILI
items: 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
items: MODAL GRAPH TRANSFORMER FOR GROUP ENGAGEMENT IN ONLINE MULTIPARTY CONVERSATIONS
items: SSRE-ID: KO‘P OBYETLAR KUZATUVLARIDA TO‘LIQ O‘Z-O‘ZINI O‘QITISHGA ASOSLANGAN QAYTA IDENTIFIKATSIYALASH
items: COMPARATIVE ANALYSIS OF PHYSICAL DEVELOPMENT IN CHILDREN ENGAGED IN SPORTS AND PHYSICAL EDUCATION
items: TARMOQ MA’LUMOTLARIGA INTELLEKTUAL ISHLOV BERISH MODELLARI VA USULLARI
items: O‘ZBEK TILIDAGI GAPLARNI PERIFRAZ QILISH AXBOROT TIZIMINING FUNKSIONAL JARAYONLARINI LOYIHALASHTIRISH
items: KO‘P FAZALI SUYUQLIKLARNI GIDRAVLIK TRANSPORTI UCHUN OQUVCHANLIKNI BAHOLASHNING YANGI USULI
items: COMPARISON OF THE EXACT AND ASYMPTOTIC SOLUTIONS TO THE PROBLEM OF OSCILLATORY FLOW OF A VISCOUS FLUID IN A CYLINDRICAL PIPE
items: USING ARTIFICIAL INTELLIGENCE TO CALCULATE SOLAR COLLECTOR PARAMETERS
items: KORXONA SAVDO MA’LUMOTLARINI INTELLEKTUAL TAHLIL QILISH UCHUN MA’LUMOTLARNI TO‘PLASH, QAYTA ISHLASH VA DASTLABKI ISHLOV BERISH
items: GLOBALASHUV JARAYONIDA IQTISODIY STATISTIK TAHLILLASHDA SUN'IY INTELLEKTNING TUTGAN O’RNI VA AHAMIYATI
items: NARXLARNI MONOPOLIYAGA QARSHI TARTIBGA SOLISHNING BOZOR INSTITUTSIONALIZATSIYASINING MAKROIQTISODIY USTUVORLIKLARI
items: JAHON VALYUTA BOZORINI SUN’IY INTELLEKT VOSITALARI BILAN BOSHQARISH VA TAHLIL QILISH
items: OBJECT DETECTION SYSTEMS FOR DETERMINING CAR MODELS AND BODY COLOR: COMPREHENSIVE COMPARATIVE ANALYSIS AND PRACTICAL INTEGRATION BASED ON REAL IMAGES UNDER UZBEKISTAN CONDITIONS
items: OLORA+: A HYBRID APPROACH TO LOW-RANK ADAPTATION
items: NUMERICAL MODELING OF THE PROCESS OF TRANSFER AND DIFFUSION OF AEROSOLIC PARTICLES IN THE ATMOSPHERE
items: O‘PKA KT (KOMPYUTER TOMOGRAFIYASI) TASVIRLARIDA SUN’IY INTELLEKT ASOSIDA SEGMENTATSIYA ALGORITMLARINI ISHLAB CHIQISH
items: DEEP LEARNING APPROACHES IN CLASSIFICATION OF ECG SIGNALS FOR CARDIOVASCULAR DISEASE DETECTION
items: INNOVATSION RAQAMLI MUHITDA SAVDO KORXONALARINING AXBOROT TIZIMINI RIVOJLANTIRISH YO‘LLARI
items: G‘OVAK MUHITLARDA IKKI QATLAMLI GAZ FILTRATSIYA JARAYONIDA BIR O’LCHOVLI MASALANI SONLI MODELLASHTIRISH
items: MODERN APPROACHES TO FORECASTING FOR BREAST CANCER DIAGNOSIS BASED ON DATA MINING
items: KVANT HISOBLASH ASOSIDA FAKTORIZATSIYA MASALASINI YECHISHDA SHOR ALGORITMIDAN FOYDALANISH
items: PROCESSING ALGORITHM INCORPORATING COMPLEX DEPENDENCIES IN INPUT DATA
items: MODEL-BASED ASSESSMENT OF ENERGY AND RELIABILITY EFFICIENCY IN WIRELESS SENSOR NODES
items: HYBRID AI FOR GASTRIC DIAGNOSTICS: MULTI-MODAL FUSION AND INTERPRETABILITY FOR EARLY ULCER AND CANCER DETECTION

TIBBIY MA’LUMOTLARNI TAHLIL QILISHNING INNOVATSION ALGORITMLARI ORQALI INSULTNI ERTA ANIQLASH
type: item
description: 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.
subject: Technical sciences
  topic: section
subject: Fisher mezoni
  topic: keywords
subject: informativ belgi
  topic: keywords
subject: optimizatsiya
  topic: keywords
subject: nominal belgi
  topic: keywords
subject: qiymatli belgi
  topic: keywords
subject: sinflararo farqlanish
  topic: keywords
subject: o‘xshashlik ko‘rsatkichi
  topic: keywords
subject: funksional mezon
  topic: keywords
subject: informatsion vektor
  topic: keywords
subject: ma’lumotlar tahlili
  topic: keywords
subject: klassifikatsiya
  topic: keywords
subject: klasterlash
  topic: keywords
creator: Aktamov Shohruhbek Ulug’bek o’g’li
rights: Copyright (c) 2025 Aktamov Shohruhbek Ulug’bek o’g’li https://creativecommons.org/licenses/by/4.0
date: 2025-12-16
publication date: 2025-12-16
starting page: 62
ending page: 69

INTEGRATIVE MACHINE LEARNING AND NATURAL LANGUAGE PROCESSING FRAMEWORK FOR THE DETECTION AND CLASSIFICATION OF AI-GENERATED TEXTS IN ACADEMIC RESEARCH PUBLICATIONS
type: item
description: 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.
subject: Technical sciences
  topic: section
subject: AI-generated text detection
  topic: keywords
subject: academic integrity
  topic: keywords
subject: machine learning
  topic: keywords
subject: natural language processing
  topic: keywords
subject: BERT-based models
  topic: keywords
subject: Kaggle datasets
  topic: keywords
subject: ensemble classification
  topic: keywords
subject: Google Colab experiments
  topic: keywords
creator: Mukhriddin Mukhiddinov
creator: Kungratov Ilmurod Kuzibay ugli
creator: Mirzarahmatov Shahzodbek Ulugbekovich
creator: Sanakulov Otaniyoz Erjigit ugli
rights: Copyright (c) 2025 Mukhriddin Mukhiddinov, Kungratov Ilmurod Kuzibay ugli, Mirzarahmatov Shahzodbek Ulugbekovich, Sanakulov Otaniyoz Erjigit ugli https://creativecommons.org/licenses/by/4.0
date: 2025-12-15
publication date: 2025-12-15
starting page: 51
ending page: 61

QISHLOQ XO‘JALIGI MAHSULOTLARINI SAQLASHDA KO‘P BOSQICHLI ADAPTIV BOSHQARUV TIZIMI MODELI VA ALGORITMI
type: item
description: 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.
subject: Technical sciences
  topic: section
subject: adaptiv boshqaruv
  topic: keywords
subject: ko‘p bosqichli jarayon
  topic: keywords
subject: matematik model
  topic: keywords
subject: dekompozitsiya
  topic: keywords
subject: parametr identifikatsiyasi
  topic: keywords
subject: optimizatsiya
  topic: keywords
subject: qishloq xo‘jaligi mahsulotlari
  topic: keywords
subject: saqlash jarayoni
  topic: keywords
subject: mikroiqlim
  topic: keywords
subject: adekvatlik
  topic: keywords
creator: Pirova Rashida Qudratovna
rights: Copyright (c) 2025 Pirova Rashida Qudratovna https://creativecommons.org/licenses/by/4.0
date: 2025-12-15
publication date: 2025-12-15
starting page: 43
ending page: 50

YEYILISH DINAMIKASINI TADQIQ ETISHDA EMPERIK TENGLAMALARNI KELTIRIB CHIQARISHDA MAHSUS DASTURLARDAN FOYDALANISHNING ISTIQBOLLARI
type: item
description: 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.
subject: Technical sciences
  topic: section
subject: Tuproq
  topic: keywords
subject: resurs
  topic: keywords
subject: o‘qyoysimon panja
  topic: keywords
subject: abraziv yeyilish
  topic: keywords
subject: ishchi organ
  topic: keywords
subject: tuproq zinchligi
  topic: keywords
subject: tuproq qattiqligi va tuproq namligi
  topic: keywords
creator: Kuvandikov Yokub Tursunbayevich
creator: Kabilov Begzod Uktam o‘g‘li
rights: Copyright (c) 2025 Kuvandikov Yokub Tursunbayevich, Kabilov Begzod Uktam o‘g‘li https://creativecommons.org/licenses/by/4.0
date: 2025-12-12
publication date: 2025-12-12
starting page: 39
ending page: 42

TURKIY TILLAR GRAMMATIKASINING KOMPYUTERGA YO‘NALTIRILGAN FORMAL MODELLARI TAHLILI
type: item
description: 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.
subject: Technical sciences
  topic: section
subject: Statistical Models
  topic: keywords
subject: Neural Network Models
  topic: keywords
subject: Rule Based Grammars
  topic: keywords
subject: Hybrid NLP
  topic: keywords
creator: Norov Abdisait Muradovich
rights: Copyright (c) 2025 Norov Abdisait Muradovich https://creativecommons.org/licenses/by/4.0
date: 2025-12-12
publication date: 2025-12-12
starting page: 27
ending page: 31

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
type: item
description: 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.
subject: Technical sciences
  topic: section
subject: Game development
  topic: keywords
subject: artificial intelligence
  topic: keywords
subject: machine learning
  topic: keywords
subject: recommender systems
  topic: keywords
subject: monetization
  topic: keywords
subject: video games
  topic: keywords
subject: digital economy
  topic: keywords
subject: user experience
  topic: keywords
subject: data science
  topic: keywords
creator: Shvets Stanislav Fedorovich
rights: Copyright (c) 2025 Shvets Stanislav Fedorovich https://creativecommons.org/licenses/by/4.0
date: 2025-12-12
publication date: 2025-12-12
starting page: 19
ending page: 26

MODAL GRAPH TRANSFORMER FOR GROUP ENGAGEMENT IN ONLINE MULTIPARTY CONVERSATIONS
type: item
description: 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.
subject: Technical sciences
  topic: section
subject: hop-level positional encoding
  topic: keywords
subject: Graph Transformer
  topic: keywords
subject: group engagement
  topic: keywords
subject: multimodal learning
  topic: keywords
subject: social signal processing
  topic: keywords
subject: collaborative learning
  topic: keywords
creator: Pirimqulova Zilola Avaz qizi
creator: Hojiyev Sunatullo Nasridin o‘g‘li
creator: Xo‘jamqulov Abdulaziz Xazrat o‘g‘li
rights: 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
date: 2025-12-12
publication date: 2025-12-12
starting page: 12
ending page: 18

SSRE-ID: KO‘P OBYETLAR KUZATUVLARIDA TO‘LIQ O‘Z-O‘ZINI O‘QITISHGA ASOSLANGAN QAYTA IDENTIFIKATSIYALASH
type: item
description: 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
subject: Technical sciences
  topic: section
subject: Ko‘p obyektli kuzatuv(MOT)
  topic: keywords
subject: Self-supervised learning
  topic: keywords
subject: reID
  topic: keywords
creator: Pirimqulova Zilola Avaz qizi
creator: Hojiyev Sunatullo Nasridin o‘g‘li
creator: Xo‘jamqulov Abdulaziz Xazrat o‘g‘li
rights: 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
date: 2025-12-09
publication date: 2025-12-09
starting page: 1
ending page: 11

COMPARATIVE ANALYSIS OF PHYSICAL DEVELOPMENT IN CHILDREN ENGAGED IN SPORTS AND PHYSICAL EDUCATION
type: item
description: 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
subject: Technical sciences
  topic: section
subject: physical development
  topic: keywords
subject: youth sports
  topic: keywords
subject: health
  topic: keywords
subject: education
  topic: keywords
subject: children
  topic: keywords
subject: rhythmic gymnastics
  topic: keywords
subject: swimming
  topic: keywords
creator: Usmankhodjaeva Adibakhon Amirsaidovna
creator: Rakhmatova Fotima Utkirovna
rights: Copyright (c) 2025 Usmankhodjaeva Adibakhon Amirsaidovna, Rakhmatova Fotima Utkirovna https://creativecommons.org/licenses/by/4.0
date: 2025-10-28
publication date: 2025-10-28
starting page: 290
ending page: 296

TARMOQ MA’LUMOTLARIGA INTELLEKTUAL ISHLOV BERISH MODELLARI VA USULLARI
type: item
description: 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.
subject: Technical sciences
  topic: section
subject: tarmoq ma’lumotlari
  topic: keywords
subject: intellektual ishlov berish
  topic: keywords
subject: sun’iy intellekt
  topic: keywords
subject: mashinali o‘qitish
  topic: keywords
subject: ma’lumotlar mayningi
  topic: keywords
subject: graf neyron tarmoqlari
  topic: keywords
subject: anomaliya aniqlash
  topic: keywords
creator: Mo‘minov Bahodir Boltayevich
creator: Husanov Sherzod Abdimonnonovich
rights: Copyright (c) 2025 Mo‘minov Bahodir Boltayevich, Husanov Sherzod Abdimonnonovich https://creativecommons.org/licenses/by/4.0
date: 2025-06-28
publication date: 2025-06-28
starting page: 280
ending page: 288

O‘ZBEK TILIDAGI GAPLARNI PERIFRAZ QILISH AXBOROT TIZIMINING FUNKSIONAL JARAYONLARINI LOYIHALASHTIRISH
type: item
description: 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.
subject: Technical sciences
  topic: section
subject: biznes-jarayon
  topic: keywords
subject: modellashtirish
  topic: keywords
subject: perifraz
  topic: keywords
subject: axborot tizimi
  topic: keywords
subject: semantik analizator
  topic: keywords
creator: Axmedova Xusniya Xusanovna
rights: Copyright (c) 2025 Axmedova Xusniya Xusanovna https://creativecommons.org/licenses/by/4.0
date: 2025-10-28
publication date: 2025-10-28
starting page: 283
ending page: 289

KO‘P FAZALI SUYUQLIKLARNI GIDRAVLIK TRANSPORTI UCHUN OQUVCHANLIKNI BAHOLASHNING YANGI USULI
type: item
description: Sanoat ishlab chiqarish jarayonida hosil bo‘ladigan chiqindilarni qayta ishlash, tashish va utilizatsiya qilish masalasi bugungi kunda eng dolzarb muammolardan biri hisoblanadi. Ko’pgina yirik korxonalarda hosil bo‘ladigan chiqindilarni samarali boshqarish ishlab chiqarishning barqarorligi, atrof-muhitni muhofaza qilish hamda iqtisodiy samaradorlik bilan bevosita bog‘liqdir. Ushbu chiqindilar ko‘pincha yuqori konsentratsiyali pulpa shaklida bo‘lib, ularning oquvchanlik xossalari tashish jarayonining samaradorligiga va energiya sarfiga katta ta’sir ko‘rsatadi.Ushbu maqolada suv miqdori kamaytirilganda sanoat chiqindisini tashish masalasi va yaroqlilik muddatiga qanday tasir ko’rsatishi o’rganilgan.
subject: Technical sciences
  topic: section
subject: sanoat chiqindilari
  topic: keywords
subject: pulpa
  topic: keywords
subject: konsentratsiya
  topic: keywords
subject: reologik xossalar
  topic: keywords
subject: oqimchanlik
  topic: keywords
subject: yoyilish radiusi
  topic: keywords
subject: kesik konussimon idish
  topic: keywords
subject: eksperimental metodika
  topic: keywords
subject: gidravlik transport
  topic: keywords
subject: sanoat chiqindilarini utilizatsiya qilish
  topic: keywords
creator: Mirzoyev Akmal Ahadovich
creator: Ahmadov Ilhom Aktam o’g’li
creator: Ahmadova E’zoza Fazliddin qizi
creator: Axmedova Dilnoza Aktam qizi
rights: Copyright (c) 2025 Mirzoyev Akmal Ahadovich, Ahmadov Ilhom Aktam o’g’li, Ahmadova E’zoza Fazliddin qizi, Axmedova Dilnoza Aktam qizi https://creativecommons.org/licenses/by/4.0
date: 2025-10-28
publication date: 2025-10-28
starting page: 278
ending page: 282

COMPARISON OF THE EXACT AND ASYMPTOTIC SOLUTIONS TO THE PROBLEM OF OSCILLATORY FLOW OF A VISCOUS FLUID IN A CYLINDRICAL PIPE
type: item
description: This article explores the problems of oscillatory flow of a viscous incompressible fluid in a cylindrical pipe with a given harmonic oscillation are solvedaverage velocity across the pipe cross-sectionThe transfer function and amplitude-phase frequency characteristics were determined. Using these functions, the influence of oscillation frequency on the ratio of shear stress to the cross-sectional average velocity was determined. By comparing the exact solution of the problem with the asymptotic solution, new asymptotic formulas are proposed that are of great importance in the study of the oscillatory flow of a viscoelastic fluid in a cylindrical pipe.
subject: Technical sciences
  topic: section
subject: Viscous fluid
  topic: keywords
subject: transfer function
  topic: keywords
subject: oscillatory flow
  topic: keywords
subject: asymptotic formula
  topic: keywords
subject: pulsating flow
  topic: keywords
subject: cylindrical pipe
  topic: keywords
creator: Kuralbay Navruzov
creator: Akmal Mirzoev
creator: Ilhom Ahmadov
rights: Copyright (c) 2025 Kuralbay Navruzov, Akmal Mirzoev, Ilhom Ahmadov https://creativecommons.org/licenses/by/4.0
date: 2025-10-28
publication date: 2025-10-28
starting page: 272
ending page: 277

USING ARTIFICIAL INTELLIGENCE TO CALCULATE SOLAR COLLECTOR PARAMETERS
type: item
description: This study explores the use of Artificial Intelligence to predict and optimize key parameters of solar collectors, such as thermal efficiency and heat transfer rate. Traditional analytical methods are often limited by nonlinear environmental factors, while AI techniques like Artificial Neural Networks and Genetic Algorithms offer more accurate and adaptive modeling. Results show that AI-based models provide high prediction accuracy and can effectively optimize collector performance under varying conditions, improving the overall efficiency of solar energy systems.
subject: Technical sciences
  topic: section
subject: Artificial Intelligence
  topic: keywords
subject: Machine Learning
  topic: keywords
subject: Solar Energy
  topic: keywords
subject: Solar Collector
  topic: keywords
subject: Parameter Estimation
  topic: keywords
subject: Energy Efficiency
  topic: keywords
subject: Thermal Performance
  topic: keywords
subject: Neural Networks
  topic: keywords
subject: Predictive Modeling
  topic: keywords
subject: Optimization
  topic: keywords
subject: Data-driven Analysis
  topic: keywords
subject: Heat Transfer
  topic: keywords
subject: Computational Modeling
  topic: keywords
subject: Smart Energy Systems
  topic: keywords
subject: Renewable Energy Systems.
  topic: keywords
creator: Ibrokhimov Abdulfatto Raximjon ugli
creator: Isakov Botirjon Umarovich
creator: Muminov Bahodir Boltayevich
rights: Copyright (c) 2025 Ibrokhimov Abdulfatto Raximjon ugli, Isakov Botirjon Umarovich, Muminov Bahodir Boltayevich https://creativecommons.org/licenses/by/4.0
date: 2025-10-28
publication date: 2025-10-28
starting page: 265
ending page: 271

KORXONA SAVDO MA’LUMOTLARINI INTELLEKTUAL TAHLIL QILISH UCHUN MA’LUMOTLARNI TO‘PLASH, QAYTA ISHLASH VA DASTLABKI ISHLOV BERISH
type: item
description: Ushbu maqolada korxonalar faoliyatida savdo ma’lumotlarini intellektual tahlil qilishning nazariy va amaliy jihatlari yoritilgan. Tadqiqotda ma’lumotlar bazasini tashkil etish va modellashtirishning zamonaviy yondashuvlari, shu jumladan ETL (Extract, Transform, Load) jarayonlari, ma’lumotlar ombori (Data Warehouse) arxitekturasi hamda OLAP texnologiyalarining qo‘llanilishi tahlil qilingan. Shuningdek, maqolada Mean usuli asosida savdo ma’lumotlarini statistik qayta ishlash va Z-Score normallashtirish natijalari keltirilgan. Ma’lumotlar tahlili korxona savdo samaradorligini oshirish, resurslardan oqilona foydalanish va boshqaruv qarorlarini ilmiy asosda qabul qilish imkonini beruvchi muhim bosqich sifatida baholangan. Tadqiqot natijalari shuni ko‘rsatadiki, ma’lumotlar omborini to‘g‘ri modellashtirish va intellektual tahlil metodlarini qo‘llash orqali korxonalarda Business Intelligence (BI) tizimlarini samarali yo‘lga qo‘yish mumkin.
subject: Technical sciences
  topic: section
subject: ma’lumotlar bazasi
  topic: keywords
subject: intellektual tahlil
  topic: keywords
subject: ma’lumotlar ombori
  topic: keywords
subject: ETL
  topic: keywords
subject: OLAP
  topic: keywords
subject: Data Warehouse
  topic: keywords
subject: CRM
  topic: keywords
subject: ERP
  topic: keywords
subject: normallashtirish
  topic: keywords
subject: Mean usuli
  topic: keywords
subject: Z-Score
  topic: keywords
subject: modellashtirish
  topic: keywords
subject: business intelligence
  topic: keywords
subject: savdo tizimlari
  topic: keywords
creator: Raximov Nodir Odilovich
creator: Baydullayev Ruslan Tuylibayevich
creator: Baydullayeva Dildora Tuylibayevna
rights: Copyright (c) 2025 Raximov Nodir Odilovich, Baydullayev Ruslan Tuylibayevich, Baydullayeva Dildora Tuylibayevna https://creativecommons.org/licenses/by/4.0
date: 2025-10-28
publication date: 2025-10-28
starting page: 258
ending page: 264

GLOBALASHUV JARAYONIDA IQTISODIY STATISTIK TAHLILLASHDA SUN'IY INTELLEKTNING TUTGAN O’RNI VA AHAMIYATI
type: item
description: Ushbu maqola sun'iy intellektning iqtisodiy statistika sohasidagi transformatsiya jarayonini o'rganadi, mashinani o'rganish (machine learning), katta ma'lumotlar tahlili, ma'lumotlarni to'plash, qayta ishlash, tahlil qilish va prognozlashning an'anaviy yondashuvlarini qanday qayta shakllantirayotganini yoritilgan. Tadqiqot su’niy intelektning keng va xilma-xil ma'lumotlar to'plamini boshqarish, iqtisodiy bashoratlarning aniqligini oshirish , siyosatchilar va biznes egalari uchun dalillarga asoslangan qarorlar qabul qilishga yordam beradi. Bundan tashqari, sun'iy intellekt statistik natijalarning vizualizatsiyasi va aloqasini kuchaytiradi, bu esa o’z navbatida murakkab iqtisodiy tushunchalarni kengroq auditoriya uchun qulayroq qiladi. Maqolada, shuningdek, ma'lumotlarning maxfiyligi bilan bog'liq muammolar, algoritmik bog’liqlik, shaffof va izohlanadigan modellarga bo'lgan ehtiyoj kabi muhim muammolar ko'rib chiqiladi. Ushbu to’siqlarga qaramay, maqoladasu’niy intellekt iqtisodiy statistikani rivojlantirish uchun ulkan salohiyatni taqdim etadi, degan xulosaga keladi, bu raqamli iqtisodiyot davrida o'z vaqtida, ishonchli va dinamik iqtisodiy monitoringga yo'l ochib beradi.
subject: Technical sciences
  topic: section
subject: Su’niy intellekt
  topic: keywords
subject: ekonomik statistika
  topic: keywords
subject: ma’lumot tahlili
  topic: keywords
subject: bashorat
  topic: keywords
subject: big data
  topic: keywords
subject: neyron tarmoqlar
  topic: keywords
subject: mashinani o'rganish
  topic: keywords
creator: Hayitbayev Husniddin Farhodovich
rights: Copyright (c) 2025 Hayitbayev Husniddin Farhodovich https://creativecommons.org/licenses/by/4.0
date: 2025-08-28
publication date: 2025-08-28
starting page: 285
ending page: 289

NARXLARNI MONOPOLIYAGA QARSHI TARTIBGA SOLISHNING BOZOR INSTITUTSIONALIZATSIYASINING MAKROIQTISODIY USTUVORLIKLARI
type: item
description: Maqolada monopoliyaga qarshi davlat tomonidan narxlarni tartibga solishning bozor institutsionalizatsiyasining makroiqtisodiy ustuvorliklari o‘rganilgan. Monopoliyaga qarshi tartibga solishning makroiqtisodiy ustunvorliklarining tahlili amalga oshirilgan. Narxlarni monopoliyaga qarshi tartibga solish mexanizmini takomillashtirish yo‘llari bo‘yicha xulosa va takliflar ishlab chiqilgan.
subject: Technical sciences
  topic: section
subject: monopoliya
  topic: keywords
subject: davlat
  topic: keywords
subject: narxlarni tartibga solish
  topic: keywords
subject: bozor institutsionalizatsiyasi jarayoni
  topic: keywords
subject: milliy iqtisodiyot
  topic: keywords
subject: iqtisodiy tizim rag‘batlantirish
  topic: keywords
subject: kadrlar salohiyati
  topic: keywords
subject: xorijiy tajriba
  topic: keywords
subject: kadrlar tayyorlash tizimi
  topic: keywords
creator: Mingboyev Asqar Qandaharovich
rights: Copyright (c) 2025 Mingboyev Asqar Qandaharovich https://creativecommons.org/licenses/by/4.0
date: 2025-10-28
publication date: 2025-10-28
starting page: 252
ending page: 257

JAHON VALYUTA BOZORINI SUN’IY INTELLEKT VOSITALARI BILAN BOSHQARISH VA TAHLIL QILISH
type: item
description: Jahon valyuta bozori – bu global iqtisodiyotning yuragi hisoblanadi. Bu bozorda kunu tun faoliyat yuritib, har kuni trillionlab dollar qiymatdagi valyutalar almashinuvi amalga oshiriladi. Bozorning murakkabligi va o‘zgaruvchanligini tahlil qilish va boshqarish juda murakkab. Shu sababli, so‘nggi yillarda sun’iy intellekt (SI) vositalari va ularning algoritmik tahlil imkoniyatlari valyuta bozorini boshqarishda yangi davrni boshlab berdi. Ushbu maqolada jahondagi valyuta bozorini SI yordamida boshqarish va tahlil qilish imkoniyatlari, afzalliklari, amaliy qo‘llanilishi hamda uni qo’llash jarayonidagi yuzaga keladigan qiyinchiliklar haqida bayon etilgan.
subject: Technical sciences
  topic: section
subject: Jahon valyuta bozori
  topic: keywords
subject: Forex savdo platformalari
  topic: keywords
subject: Kriptovalyutalar
  topic: keywords
creator: Hayitbayev Husniddin Farhodovich
rights: Copyright (c) 2025 Hayitbayev Husniddin Farhodovich https://creativecommons.org/licenses/by/4.0
date: 2025-10-28
publication date: 2025-10-28
starting page: 248
ending page: 251

OBJECT DETECTION SYSTEMS FOR DETERMINING CAR MODELS AND BODY COLOR: COMPREHENSIVE COMPARATIVE ANALYSIS AND PRACTICAL INTEGRATION BASED ON REAL IMAGES UNDER UZBEKISTAN CONDITIONS
type: item
description: This paper presents a practical solution for real-time recognition of vehicle model and body color on real-world CCTV streams in Uzbekistan. The dataset was annotated in CVAT and cleaned; to ensure fair evaluation, we applied a camera-wise train/val/test split (preventing cross-camera leakage). On the same dataset, we fine-tuned YOLOv8-l, DETR (ResNet-101), and CenterNet, and analyzed the quality–speed trade-off from a deployment perspective. Using detector-produced bounding boxes as ROIs, we integrated a fast color module into the pipeline based on a combined LAB+HSV approach. For inference threshold selection, we employed a practical method grounded in the F1–Confidence curve. The system operates robustly with multi-camera RTSP streams, supports auto-reconnect and resource monitoring (CPU/RAM/IO and, when available, GPU), and is ready for ONNX/TensorRT export. The results demonstrate practical applicability in traffic monitoring, security, and intelligent video surveillance systems.
subject: Technical sciences
  topic: section
subject: CCTV
  topic: keywords
subject: object detection
  topic: keywords
subject: vehicle model
  topic: keywords
subject: body color estimation
  topic: keywords
subject: YOLOv8-l
  topic: keywords
subject: DETR (ResNet-101)
  topic: keywords
subject: CenterNet
  topic: keywords
subject: CVAT
  topic: keywords
subject: COCO/YOLO
  topic: keywords
subject: LAB
  topic: keywords
subject: HSV
  topic: keywords
subject: F1–Confidence
  topic: keywords
subject: real time
  topic: keywords
subject: multi-camera
  topic: keywords
subject: ONNX
  topic: keywords
subject: TensorRT
  topic: keywords
creator: Davronov Rifkat Rakhimovich
creator: Misirov Farxod Abdulla ugli
rights: Copyright (c) 2025 Davronov Rifkat Rakhimovich, Misirov Farhod Abdulla ugli https://creativecommons.org/licenses/by/4.0
date: 2025-10-28
publication date: 2025-10-28
starting page: 240
ending page: 247

OLORA+: A HYBRID APPROACH TO LOW-RANK ADAPTATION
type: item
description: This paper introduces and analyzes OLoRA+, a novel parameter-efficient fine-tuning method that enhances Low-Rank Adaptation (LoRA) by combining the orthonormal initialization of OLoRA with the differential learning rate optimization of LoRA+. The study compared the performance of OLoRA+ against the standard OLoRA baseline on the TinyLlama-1.1B-Chat-v1.0 model. Using a subset of the alpaca dataset, a collection of 52K instruction-following demonstrations, with 1000 samples for training and 500 for testing, model performance was assessed using evaluation loss, BLEU, and ROUGE metrics. The obtained results show that OLoRA+ consistently outperforms the OLoRA baseline across all considered metrics. Additionally, the study reveals that OLoRA+ is effective with learning rate ratios both greater and less than one, uncovering a novel trade-off between "Refinement" and "Exploration" learning strategies. This confirms OLoRA+'s potential as a more versatile and powerful approach for LLM adaptation under limited resource conditions.
subject: Technical sciences
  topic: section
subject: NLP
  topic: keywords
subject: PEFT
  topic: keywords
subject: LLM
  topic: keywords
subject: LoRA
  topic: keywords
subject: OLoRA
  topic: keywords
subject: LoRA+
  topic: keywords
creator: Davronov Rifkat Rakhimovich
creator: Kushmuratov Samariddin Ibodulla ugli
rights: Copyright (c) 2025 Davronov Rifkat Rakhimovich, Kushmuratov Samariddin Ibodulla ugli https://creativecommons.org/licenses/by/4.0
date: 2025-10-28
publication date: 2025-10-28
starting page: 233
ending page: 239

NUMERICAL MODELING OF THE PROCESS OF TRANSFER AND DIFFUSION OF AEROSOLIC PARTICLES IN THE ATMOSPHERE
type: item
description: The work developed a mathematical model and a numerical algorithm for monitoring and forecasting the process of the spread of harmful substances in the atmosphere, taking into account the change in the movement of particles in the atmosphere. A detailed analysis of the developed mathematical models and numerical algorithms related to the processes of transport and diffusion of harmful substances in the atmosphere was carried out.
subject: Technical sciences
  topic: section
subject: Mathematical model
  topic: keywords
subject: concentration
  topic: keywords
subject: atmosphere
  topic: keywords
subject: joint equations
  topic: keywords
subject: transfer and diffusion processes
  topic: keywords
subject: hydromechanics law
  topic: keywords
subject: high-order approximation methods
  topic: keywords
creator: Shafiev Tursun Rustamovich
creator: Nazarov Shakhzod Erkinovich
creator: Bobozhonova Madina Abduakhat qizi
rights: Copyright (c) 2025 Shafiev Tursun Rustamovich, Nazarov Shakhzod Erkinovich, Bobozhonova Madina Abduakhat qizi https://creativecommons.org/licenses/by/4.0
date: 2025-10-28
publication date: 2025-10-28
starting page: 227
ending page: 232

O‘PKA KT (KOMPYUTER TOMOGRAFIYASI) TASVIRLARIDA SUN’IY INTELLEKT ASOSIDA SEGMENTATSIYA ALGORITMLARINI ISHLAB CHIQISH
type: item
description: Ushbu maqolada O‘pka kompyuter tomografiyasi (KT) tasvirlarida sun’iy intellekt texnologiyalari asosidagi segmentatsiya algoritmini ishlab chiqarishni ishlab chiqish masalasini ko'rib chiqamiz. O‘pka to‘g’irlash chegaralarini aniqlashtirish, Patologik o‘zgarishlarni erta bosqichda aniqlash va diagnostika jarayonini aniqligini oshirish. Maqolada chuqur o‘rganish (Deep Learning) usullaridan biri - U-Net konvolyutsion neyron manbasi asosida ishlab chiqarilgan va o'qitilgan. Maqolada LIDC-IDRI ochiq ma’lumotlar to‘plamiga KT tasvirlaridan foydalanildi. Modellashtirish IoU (Intersection over Union) va Dice koeffitsienti mezonlari yordamida baholandi. Natijalar ko‘rsatgichlari, taklif qilingan model O‘pka to‘qimasi segmentatsiyasini yuqori aniqlik bilan va kasallik o‘choqlarini avtomatik aniqlash jarayonini soddalashtirish va tibbiy tasvirlarni tahlil qilishda inson omillarini hamda klinik diagnostika tizimlarida amaliy qo‘llab-quvvatlash mumkin.
subject: Technical sciences
  topic: section
subject: O‘pka KT tasvirlari
  topic: keywords
subject: segmentatsiya
  topic: keywords
subject: sun’iy intellekt
  topic: keywords
subject: chuqur o‘rganish
  topic: keywords
subject: U-Net
  topic: keywords
subject: kompyuter tomografiyasi
  topic: keywords
subject: neyron tarmoq
  topic: keywords
subject: diagnostika
  topic: keywords
creator: Saparov Saidqul Xo‘jamurotovich
creator: Adilova Dinora Ablaqul qizi
rights: Copyright (c) 2025 Saparov Saidqul Xo‘jamurotovich, Adilova Dinora Ablaqul qizi https://creativecommons.org/licenses/by/4.0
date: 2025-10-27
publication date: 2025-10-27
starting page: 219
ending page: 226

DEEP LEARNING APPROACHES IN CLASSIFICATION OF ECG SIGNALS FOR CARDIOVASCULAR DISEASE DETECTION
type: item
description: Cardiovascular diseases (CVDs) remain the leading cause of mortality worldwide, emphasizing the critical need for early detection and accurate diagnosis. Electrocardiography (ECG) provides a noninvasive and cost-effective means of assessing cardiac health; however, manual interpretation is time-consuming and prone to human error. Recent advances in deep learning have shown great promise in automating ECG signal analysis by leveraging powerful feature extraction and classification capabilities. This study investigates multiple deep learning architectures, including convolutional neural networks (CNNs), hybrid CNN–LSTM models, and Transformer-based approaches, applied to benchmark ECG datasets. A comprehensive workflow is developed, consisting of data acquisition, preprocessing, beat segmentation, deep learning-based feature extraction, classification, and performance evaluation. Comparative experiments demonstrate that while CNNs effectively capture morphological features, the CNN–LSTM hybrid yields enhanced performance by modeling temporal dependencies. The Transformer-based model achieves the highest accuracy, sensitivity, and specificity, highlighting its ability to capture long-range dependencies in ECG signals. The results confirm the potential of advanced deep learning frameworks in supporting reliable, automated CVD detection. Future directions include cross-dataset validation, multimodal integration, and optimization for deployment on wearable and embedded platforms.
subject: Technical sciences
  topic: section
subject: Electrocardiogram (ECG)
  topic: keywords
subject: cardiovascular disease detection
  topic: keywords
subject: deep learning
  topic: keywords
subject: convolutional neural networks (CNN)
  topic: keywords
subject: long short-term memory (LSTM)
  topic: keywords
subject: Transformer models
  topic: keywords
subject: signal processing
  topic: keywords
subject: biomedical engineering
  topic: keywords
subject: wearable healthcare systems
  topic: keywords
creator: Dushanov Begmamat Berdimurodovich
creator: Mamatov Narzillo Solidjonovich
rights: Copyright (c) 2025 Dushanov Begmamat Berdimurodovich, Mamatov Narzillo Solidjonovich https://creativecommons.org/licenses/by/4.0
date: 2025-10-26
publication date: 2025-10-26
starting page: 208
ending page: 218

INNOVATSION RAQAMLI MUHITDA SAVDO KORXONALARINING AXBOROT TIZIMINI RIVOJLANTIRISH YO‘LLARI
type: item
description: Ushbu maqolada innovatsion raqamli muhitda savdo korxonalarining axborot tizimini rivojlantirish yo‘llari masalasi keng qamrovda o‘rganiladi. Tadqiqotda savdo korxonalarida raqamli texnologiyalarni joriy etish, ma’lumotlar bazasi va axborot oqimlarini samarali boshqarish, biznes jarayonlarni avtomatlashtirish hamda raqobatbardoshlikni oshirish imkoniyatlari tahlil qilinadi. Milliy va xorijiy tajribalar asosida savdo korxonalarida axborot tizimining iqtisodiy samaradorligi, uning boshqaruv qarorlarini qabul qilishdagi roli va innovatsion yechimlari ko‘rib chiqiladi. Natijalarga ko‘ra, kompleks model savdo korxonalarining faoliyatini raqamlashtirish, xarajatlarni kamaytirish va iste’molchilarga xizmat ko‘rsatish sifatini oshirishda muhim ahamiyat kasb etishi aniqlangan. Maqola yakunida O‘zbekistonda savdo korxonalari uchun axborot tizimini rivojlantirishning ustuvor yo‘nalishlari bo‘yicha takliflar ishlab chiqilgan.
subject: Technical sciences
  topic: section
subject: raqamli iqtisodiyot
  topic: keywords
subject: savdo korxonasi
  topic: keywords
subject: axborot tizimi
  topic: keywords
subject: kompleks model
  topic: keywords
subject: biznes jarayonlar
  topic: keywords
subject: avtomatlashtirish
  topic: keywords
subject: raqamli transformatsiya
  topic: keywords
creator: Boltayev Alisher Ataboy o‘g‘li
rights: Copyright (c) 2025 Boltayev Alisher Ataboy o‘g‘li https://creativecommons.org/licenses/by/4.0
date: 2025-10-26
publication date: 2025-10-26
starting page: 204
ending page: 207

G‘OVAK MUHITLARDA IKKI QATLAMLI GAZ FILTRATSIYA JARAYONIDA BIR O’LCHOVLI MASALANI SONLI MODELLASHTIRISH
type: item
description: Maqolada mahsuldor qatlamlarda gaz qazib olishni ko’paytirish maqsadida ikki qatlamli o‘zaro dinamik bog‘langan gaz filtratsiya jarayonini matematik modellashtirish va hisoblash algoritmi keltirilgan. Modellashtirish jarayonida qatlam bosimi hamda o‘tkazuvchanlik koeffitsientlarining fazoviy va vaqtga bog‘liq o‘zgaruvchanligi inobatga olingan. Chegaraviy masalani sonli modellashtirishda chekli-ayirmalar usuli, kvazichiziqli usul va Kramer usullaridan foydalanilgan. Shuningdek, samarali sonli usullar va algoritmlar asosida dasturiy ta’minot ishlab chiqilgan.
subject: Technical sciences
  topic: section
subject: Matematik model
  topic: keywords
subject: algoritm
  topic: keywords
subject: dastur
  topic: keywords
subject: sonli usullar
  topic: keywords
subject: ko‘p qatlamli
  topic: keywords
subject: dinamik aloqa
  topic: keywords
subject: sust o‘tkazuvchan qatlam
  topic: keywords
subject: chekli ayirmalar usuli
  topic: keywords
subject: Kramer usuli
  topic: keywords
creator: Eshqarayeva Narxol G‘uzarovna
creator: Xujamurotova Mashxura G‘ayrat qizi
rights: Copyright (c) 2025 Eshqarayeva Narxol G‘uzarovna, Xujamurotova Mashxura G‘ayrat qizi https://creativecommons.org/licenses/by/4.0
date: 2025-10-24
publication date: 2025-10-24
starting page: 198
ending page: 203

MODERN APPROACHES TO FORECASTING FOR BREAST CANCER DIAGNOSIS BASED ON DATA MINING
type: item
description: The article examines modern approaches to prediction and classification in breast cancer diagnosis based on data mining techniques. It describes the most commonly used datasets and modalities, stages of preprocessing and feature extraction, classical and deep learning models, validation and evaluation methods, issues of interpretability, clinical validation, regulatory considerations, and ethical aspects. Recommendations for practical implementation and directions for further research are provided.
subject: Technical sciences
  topic: section
subject: Машинное зрение
  topic: keywords
subject: классификация
  topic: keywords
subject: сортировка
  topic: keywords
subject: зерно
  topic: keywords
subject: анализ и обработка изображений
  topic: keywords
subject: реальное время
  topic: keywords
creator: Mukhamedieva Dildora Kabilovna
creator: Doshanova Malika Yuldashovna
creator: Shaazizova Madina Eldarovna
rights: Copyright (c) 2025 Mukhamedieva Dildora Kabilovna, Doshanova Malika Yuldashovna, Shaazizova Madina Eldarovna https://creativecommons.org/licenses/by/4.0
date: 2025-10-23
publication date: 2025-10-23
starting page: 119
ending page: 129

KVANT HISOBLASH ASOSIDA FAKTORIZATSIYA MASALASINI YECHISHDA SHOR ALGORITMIDAN FOYDALANISH
type: item
description: Mazkur maqolada kvant hisoblash nazariyasining Piter Shor tomonidan 1994-yilda ishlab chiqilgan Shor algoritmi tahlil qilinadi. Ushbu algoritm butun sonlarni faktorlash masalasini polinomial vaqt ichida yechimini topish imkonini beruvchi kvant yondashuvni taqdim etadi. Maqolada Shor algoritmining kvant va klassik qismlari batafsil yoritilgan, kvant Furye transformatsiyasi yordamida funksiyaning davrini topish jarayoni va undan foydalanib faktorizatsiyani amalga oshirish bosqichlari tushuntirilgan. Asosiy e’tibor algoritmning amaliy qo‘llanilishiga, xususan qishloq xo‘jaligi sohasidagi ma’lumotlarni shifrlash va identifikatsiya qilish jarayonlarida foydalanish imkoniyatlariga qaratilgan. Maqolada Shor algoritmi yordamida shifrlangan urug‘ turlarining kodlari faktorizatsiya qilinib, ularning asl identifikatorlari aniqlanadi. Shu orqali kvant algoritmlarni agroinformatika, raqamli urug‘ identifikatsiyasi, va ma’lumotlarni xavfsiz saqlash tizimlarida qo‘llash istiqbollari asoslab berilgan.
subject: Technical sciences
  topic: section
subject: Shor algoritmi
  topic: keywords
subject: kvant hisoblash
  topic: keywords
subject: faktorizatsiya
  topic: keywords
subject: kvant Furye transformatsiyasi
  topic: keywords
subject: kriptografiya
  topic: keywords
subject: qishloq xo‘jaligi
  topic: keywords
subject: agroinformatika
  topic: keywords
subject: zanjirli kasrlar metodi
  topic: keywords
creator: Raxmonova Munisaxon Rashodovna
rights: Copyright (c) 2025 https://creativecommons.org/licenses/by/4.0
date: 2025-10-23
publication date: 2025-10-23
starting page: 192
ending page: 197

PROCESSING ALGORITHM INCORPORATING COMPLEX DEPENDENCIES IN INPUT DATA
type: item
description: In this article, an algorithm (QKV) was developed that takes into account many factors in predicting the level of cardiovascular diseases, in particular, taking into account the relationships between factors. With the help of machine learning, the accuracy of the model has been increased, taking into account the complex interconnection of the data included in the MLP model. In addition, in the process of training the dataset processed through the QKV algorithm to the MLP model, it was possible to quickly achieve the minimum value of the Loss function and find the optimal weight coefficient.
subject: Technical sciences
  topic: section
subject: Artificial intelligence
  topic: keywords
subject: MLP model
  topic: keywords
subject: QKV algorithm
  topic: keywords
subject: cardiovascular diseases
  topic: keywords
creator: Saidov A.D.
rights: Copyright (c) 2025 Saidov A.D. https://creativecommons.org/licenses/by/4.0
date: 2025-10-23
publication date: 2025-10-23
starting page: 187
ending page: 191

MODEL-BASED ASSESSMENT OF ENERGY AND RELIABILITY EFFICIENCY IN WIRELESS SENSOR NODES
type: item
description: Wireless sensor networks (WSN) are increasingly used in various control and management applications, where the efficiency of sensor nodes plays a decisive role in the overall operation of the network. This work presents a model-based assessment approach for assessing the energy efficiency and reliability of wireless sensor nodes. The proposed models analyze the relationship between transmission energy, node operation time, and network reliability under various operating conditions. Simulation-based estimates show how energy constraints and packet loss levels affect the stable operation of sensor nodes. The study compares the effectiveness of several existing energy models and provides recommendations for optimizing the behavior of nodes in energy-restricted environments.
subject: Technical sciences
  topic: section
subject: WSN
  topic: keywords
subject: energy efficiency
  topic: keywords
subject: reliability
  topic: keywords
subject: performance modeling
  topic: keywords
subject: node lifetime
  topic: keywords
creator: Khayrullaev Alisher Fayzulla ugli
rights: Copyright (c) 2025 Khayrullaev Alisher Fayzulla ugli https://creativecommons.org/licenses/by/4.0
date: 2025-10-23
publication date: 2025-10-23
starting page: 181
ending page: 186

HYBRID AI FOR GASTRIC DIAGNOSTICS: MULTI-MODAL FUSION AND INTERPRETABILITY FOR EARLY ULCER AND CANCER DETECTION
type: item
description: This study investigates the development of hybrid Artificial Intelligence (AI) models for the early and accurate diagnosis of gastric ulcer and gastric cancer. Traditional diagnostic methods such as endoscopy and biopsy, though widely used, face limitations including inter-observer variability, sampling errors, and delays in diagnosis. To address these challenges, two novel hybrid AI architectures are proposed: the Deep Feature-Attention SVM (DFA-SVM) and the Multi-Modal Fuzzy-Fusion Network (M2F2-Net). These models integrate multi-modal data sources, including endoscopic images, histopathology slides, and structured clinical records, while employing attention mechanisms, fuzzy logic, and interpretable AI approaches to enhance diagnostic performance. Experimental evaluation using the conceptual GIMIC-12K dataset demonstrates that M2F2-Net achieves superior performance, particularly in early gastric cancer detection, by significantly reducing false negatives and improving sensitivity. The findings highlight the transformative potential of hybrid AI in improving patient outcomes, optimizing healthcare efficiency, and supporting explainable decision-making in clinical practice.
subject: Technical sciences
  topic: section
subject: Hybrid Artificial Intelligence
  topic: keywords
subject: Gastric Cancer
  topic: keywords
subject: Gastric Ulcer
  topic: keywords
subject: Multi-Modal Fusion
  topic: keywords
subject: Deep Learning
  topic: keywords
subject: Explainable AI (XAI)
  topic: keywords
subject: Diagnostic Imaging
  topic: keywords
subject: Fuzzy Logic
  topic: keywords
subject: Early Detection
  topic: keywords
creator: Iskandarova Sayyora Nurmamatovna
creator: Abdurashidova Kamola Turg’unbayevna
creator: Omonov Sanjarbek G‘anisher o’g’li
creator: Sabitova Nazokat Qobuljon qizi
rights: Copyright (c) 2025 Iskandarova Sayyora Nurmamatovna, Abdurashidova Kamola Turg’unbayevna, Omonov Sanjarbek G‘anisher o’g’li, Sabitova Nazokat Qobuljon qizi https://creativecommons.org/licenses/by/4.0
date: 2025-10-23
publication date: 2025-10-23
starting page: 167
ending page: 180