International scientific journal "Modern Science and Research"

ISSN: 2181-3906;   OAV Guvohnoma №042359;   Impact factor (UIF-2022): 8.2
Ushbu jurnalda O'zbekiston va chet davlatlar olimlari ilmiy maqolalari chop etiladi.
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Jurnalning rasmiy tillari: o‘zbek, rus, ingliz
Jurnal telegram kanali: https://t.me/modernscience_research
Maqola muallifiga BEPUL qabul qilinganlik haqida tabriknoma, sertifikat, indekslanganligi haqida ma'lumotnoma va mualliflik guvohnomasi beriladi.
Jurnal har oyda nashr qilinadi.
Maqolalar yuborish uchun: @modernscience_research
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Articles Information letter

Oxirgi qushilgan maqolalar:



MOTEL VA KICHIK MEHMONXONA ARXITEKTURASI: O'ZBEKISTON TRANSPORT TARMOQLARI BILAN BOG'LIQ YANGI DIZAYN TENDENSIYALARI

Ushbu maqolada O‘zbekiston transport tarmoqlari bilan bog‘liq holda motel va kichik mehmonxonalarning yangi dizayn tendensiyalari tahlil qilinadi. Tadqiqot davomida transport infratuzilmasi, ekologik innovatsiyalar va milliy arxitektura elementlari asosida yangi loyihalarning istiqbollari o‘rganildi.


12.06.2025 Volume 4 Issue 6 View more Download
TERI QATLAMLARINING GISTOLOGIK TUZILISHI: EPIDERMIS, DERMIS VA GIPODERMIS

Ushbu maqolada inson tanasining eng katta organi — terining gistologik tuzilishi, uning asosiy qatlamlari (epidermis, dermis, gipodermis) va har bir qatlamning morfologik hamda funksional xususiyatlari ilmiy asosda tahlil qilinadi. Ayniqsa, terining himoya funksiyasi: fizik, mikrobiologik, immunologik va mexanik jihatlari chuqur yoritilgan. Maqola teri tuzilmasining mikroskopik ko‘rinishlari, ularning hujayraviy tarkibi va zamonaviy ilmiy yondashuvlar asosida o‘rganiladi. Mazkur tadqiqot gistologiya fani doirasida terining ko‘p funksiyali va ixtisoslashgan tuzilma ekanini ochib beradi hamda klinik amaliyotda uni to‘g‘ri baholash zarurligini ko‘rsatadi.


12.06.2025 Volume 4 Issue 6 View more Download
THE INFLUENCE OF NATIVE LANGUAGE ON SECOND LANGUAGE PRONUNCIATION

The relationship between a speaker’s native language (L1) and their pronunciation in a second language (L2) has long been recognized as a critical area of study within second language acquisition. Pronunciation is not merely about producing sounds correctly; it encompasses various phonological elements such as stress, rhythm, intonation, and syllable structure — all of which are deeply shaped by the learner's first language. This paper aims to explore the extent to which L1 interferes with or supports the acquisition of accurate L2 pronunciation. It investigates both segmental (individual sounds) and suprasegmental (prosodic features) aspects of speech, presenting evidence from various language groups to illustrate common patterns of transfer. Moreover, the study discusses how phonological habits from the native language often lead to a foreign accent and reduced intelligibility in the second language, even among otherwise proficient speakers. Emphasis is placed on practical strategies and pedagogical approaches that can be used to address L1-induced difficulties, such as contrastive analysis, phonetic training, and the use of technological tools for self-monitoring and feedback. The paper concludes that although native language influence is a natural and often unavoidable aspect of second language learning, its impact on pronunciation can be significantly minimized through targeted instruction and increased learner awareness.


12.06.2025 Volume 4 Issue 6 View more Download
МЕТОДИКА ПРЕПОДАВАНИЯ РУССКОГО ЯЗЫКА КАК ИНОСТРАННОГО

Освоение методики преподавания русского языка как иностранного становится важным условием и неотъемлемой частью подготовки студентов высших педагогических учреждений Узбекистана к будущей профессиональной деятельности. Цель статьи - подробно описать лингвистические и речевые аспекты обучения устному и письменному общению на русском языке как иностранном, как новом языке.


12.06.2025 Volume 4 Issue 6 View more Download
SELECTING AND JUSTIFYING DEEP LEARNING MODELS FOR EMOTION CLASSIFICATION

This paper focuses on the selection and justification of deep learning models for emotion classification tasks. It provides a comprehensive analysis of various neural network architectures, including Convolutional Neural Networks, Recurrent Neural Networks, Long Short-Term Memory networks, and Transformer models, assessing their performance in recognizing and classifying human emotions from multimodal data sources. The study examines the strengths and limitations of each model with respect to data type, training efficiency, computational complexity, and generalization capabilities. Furthermore, criteria for optimal model selection tailored to real-world emotion recognition applications are discussed. The findings contribute to enhancing the accuracy and robustness of emotion classification systems and offer valuable guidelines for researchers and practitioners developing advanced affective computing solutions.


12.06.2025 Volume 4 Issue 6 View more Download
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