Neural networks, once confined to the realm of science fiction, have emerged as powerful tools shaping our digital age. From driving cars to diagnosing diseases, their applications span diverse domains. However, one area where their impact is particularly promising yet underexplored is in global education systems. As nations grapple with the complexities of modernizing their educational approaches, neural networks offer a beacon of hope for personalized, efficient, and equitable learning experiences. In this article, we embark on a journey to uncover the potential of neural networks in revolutionizing education worldwide. By examining their applications, challenges, and future outlook, we aim to shed light on the transformative role they could play in shaping the future of learning.

Climate modeling and prediction play a crucial role in understanding and combating the effects of climate change. As the Earth's climate becomes increasingly complex and unpredictable, there is a growing need for advanced tools and technologies to accurately forecast future trends. One such innovative approach is the use of neural networks - a form of artificial intelligence that mimics the human brain's ability to learn and adapt. By harnessing the power of neural networks, researchers and scientists are exploring new possibilities for improving the accuracy and efficiency of climate modeling and prediction. This article will take into account the potential benefits of using neural networks in climate science, highlighting their capabilities, applications, challenges, and future directions.

Ushbu maqolada neyron tarmog‘i texnologiyalari va ularning foydalanish usullari haqida ko‘proq ma’lumot berilgan. Mualliflar algoritmlarni oshirish, genetik algoritmlardan foydalanish, minimal qoplamali daraxtalar va klasterlash algoritmlari haqida muhim ma’lumotlar ko‘rsatadilar. Elektron hujjat oqimini tasniflashda neyron tarmoqlaridan foydalanish vositalarini ishlab chiquvchilarni ko‘proq jalb qiladi. Neyron tarmog‘i matematik modellari va ularning ishlovchi prinsiplari to‘g‘ri ko‘rsatilgan. Ayniqsa, neyron tarmog‘ining kiritish va chiqish mexanikasi bo‘yicha formulalar va funksiyalar tasvirlangan. Ushbu ma’lumotlar neyron tarmoq texnologiyalarini o‘rganish va tadqiq qilish uchun foydali bo‘lishi mumkin.

This article discusses the study of neural networks and their types. Areas of application of neural networks, applications cover a wide range of areas

This article discusses the study of neural networks and their types. Areas of application of neural networks, applications cover a wide range of areas

Нейро—педагогик ёндашув билан инсонни тарбиялаш, ўқитиш фақат маълумотлар тўпламида фарқланади. Уни қуйидагича шакллантириш мумкин: табиий нейрон тармоқни ўқитиш—бу нейрон тармоқ ҳолати функцияларининг деярли бир хил мос келадиган ва маълум қийматлари билан маълум маълумотлар тўпламларини киритиш; таълим—бу билим олишга қаратилган аниқ маълумотлар тўпламларини тайёрлаш.

This research solves the task of forecasting and analyzing the dynamics of the urban population share using an artificial neural network method, taking the Samarkand region as an example. Based on the official data of the State Statistics Committee of the Republic of Uzbekistan, 25 significant factors affecting the degree of urbanization were identified. Then, an artificial neural network model was created. Using this model, forecasts of changes in the urban population share of the Samarkand region for 2023-2025 were made. The obtained results contain data that can be practically applied for managing and regulating urbanization processes.

Ушбу мақолада Нейрон тармоғи ёрдамида тиббий тасвир сифатини яхшилаш учун рақамли сигнални қайта ишлаш ва тасвирларни аниқлигини ошириш. Соғлиқни саклашда рақамли технология ва суъний интеллектдан унумли фойдаланиш, тиббиётимизнинг ривожланаётганлигидан дарак бериш. Мавжуд адабиётлар асосида мақола рақамли технология ёрдамида сигналларни қайта ишлашнинг назарий асосларни муҳокама этиш ва нейрон тармоғини қўллаш орқали тасвирларга ишлов бериш касалликни аниқлаш ва прогноз бериш.

Effective cybersecurity management requires significant automation to handle the complexity and volume of information involved. Traditional technologies with fixed implementations are often inadequate for combating security threats. Machine learning methods in AI offer a solution to this challenge. This paper presents an overview of various AI applications in cybersecurity and assesses their potential to strengthen defense mechanisms. Our review reveals that valuable AI tools are already in use for network protection and other cybersecurity areas through neural networks. However, some cybersecurity challenges can only be effectively addressed by employing AI strategies. For example, comprehensive data is essential for strategic decision-making, and the need for logical decision support remains a critical unresolved issue in cybersecurity.

This article explores the application of machine learning algorithms and big data analytics in predicting demand and customer behavior. With the increasing availability of vast amounts of data and advancements in machine learning techniques, organizations can leverage these tools to gain insights into customer preferences, anticipate demand patterns, and make data-driven decisions. The article discusses several commonly used machine learning algorithms, such as logistic regression, random forest, gradient boosting, support vector machines, neural networks, k-nearest neighbors, and naive Bayes, that have proven effective in customer behavior prediction tasks. Considerations for algorithm selection, including data availability, interpretability, scalability, and model complexity, are also discussed. Furthermore, the article highlights evaluation metrics commonly used to assess the performance of these algorithms, such as accuracy, precision, recall, F1 score, ROC curve, AUC, mean squared error, R-squared, lift, and mean average precision. By understanding and applying these techniques, organizations can gain a competitive advantage by accurately predicting demand and effectively targeting their customer base.

В данной статье рассмотрены теоретические и практические основы науки компьютерной лингвистики, её передовые методы и перспективы развития, а также вопросы, связанные с инновационными решениями в области искусственного интеллекта. В первой части статьи представлены определения лингвистики и компьютерной лингвистики, а также теоретические принципы, такие как математическое моделирование, формальные грамматики и аксиоматические подходы. Далее анализируются современные методы машинного перевода, применение нейронных сетей и технологий глубокого обучения в решении прикладных задач компьютерной лингвистики. Также освещаются направления интеграции искусственного интеллекта и компьютерной лингвистики, вопросы когнитивного моделирования и возможности применения в образовании и бизнесе.

Intelligent analysis of video data encompasses a variety of techniques, including machine learning, deep learning, computer vision, and real-time processing. These methods are designed to automate the detection, categorization, and enhancement of video content, ensuring seamless transmission and high-quality viewing experiences. For instance, deep learning models, such as convolutional neural networks (CNNs) and long short-term memory (LSTM) networks, are extensively used for content recognition and anomaly detection in video streams[1].

Humans can easily detect and identify objects present in an image. The human visual system is fast and accurate and can perform complex tasks like identifying multiple objects and detect obstacles with little conscious thought. For a long time, humans have been trying to make computers understand what is on the images. With the availability of large amounts of data, faster Graphics Processing Unit (GPU)s, and better algorithms, we can now easily train computers to detect and classify multiple objects within an image with high accuracy. The goal of this paper is to implement an object detection model suitable in terms of size and speed to run on an Android device and detect logos in real-time. The proposed approach is based on YOLOv2 (You Only Look Once) state-of-the-art, real-time object detection for logos and this project used the FlickrLogos-32 dataset. The experimental results show that we obtained a final accuracy of 82.3% and a speed of 35 fps (frames per second) on the NVidia GeForce GTX 1070.

Three-dimensional (3D) feature stabilization is a crucial aspect in various fields such as computer vision, robotics, and augmented reality. It is essential to maintain the stability of identified features across frames for accurate analysis and reliable performance. In this paper, we propose an Artificial Neural Network (ANN) model designed specifically for 3D feature stabilization tasks. Our model uses the inherent capacity of neural networks to learn complex patterns and relationships within sequential data to effectively stabilize 3D features across consecutive frames.

This investigation presents a brief yet thorough comparative assessment of FPGA (SoC)–based and GPU-based AI accelerators across applications that encompass edge devices to data center training environments. Primary performance parameters—latency, throughput, energy efficiency, programmability, and scalability—are thoroughly evaluated with a specific concentration on deep neural network inference and training. The research further highlights the significance of hardware/software co-design and high-level synthesis (HLS) in augmenting FPGA performance. Representative platforms, such as the one from Xilinx and Nvidia, are referenced to illustrate prevailing trends. Findings suggest that while GPUs excel in throughput and development simplicity, FPGAs exhibit reduced latency and enhanced energy efficiency in power-sensitive or real-time applications.

This article explores the role of machine translation (MT) tools in the context of language learning. As digital tools like Google Translate and DeepL become more advanced through neural network technology, they are increasingly used by language learners for assistance with vocabulary, grammar, and translation practice. This paper discusses the benefits, limitations, and pedagogical implications of integrating MT tools into language education.

Аннотация. Наша цель — разработать архитектуры, которые сохранят новаторскую производительность сверточных нейронных сетей (CNN) для ориентировочной локализации и в то же время будут легкими, компактными и подходящими для приложений с ограниченными вычислительными ресурсами.

This article provides an in-depth overview of the development of artificial intelligence (AI), its key technologies, and its significance in our lives. AI encompasses a collection of technologies designed to replicate certain human cognitive functions through computer systems. The article explores the history of AI, its current state, core methodologies, applications across various fields, and future potential. Specific examples highlight AI's impact in sectors such as healthcare, transportation, education, security, and social media. By analyzing the economic, social, and cultural influences of AI, the article emphasizes the critical role of this technology in shaping the future.

In the era of deep learning and large-scale artificial intelligence systems, the importance of efficient optimization algorithms has significantly increased. Neural networks, particularly those with deep and complex architectures, rely heavily on gradient-based iterative methods to update model parameters by minimizing a loss function. Among these methods, the Adam (Adaptive Moment Estimation) algorithm has emerged as a widely adopted solution due to its adaptive learning capability and robust convergence behavior. Originally introduced by D. Kingma and J. Ba in 2015, Adam integrates the advantages of both Stochastic Gradient Descent (SGD) and RMSprop algorithms, addressing several limitations of traditional approaches, such as fixed learning rates, slow convergence, oscillatory updates, and sensitivity to noisy gradients [1][2].

Ushbu raqamli davrda yuzni aniqlash tizimi deyarli barcha sohalarda muhim rol o'ynaydi. Yuz tanib olish biometrik usullardan biri hisoblanadi. U xavfsizlik, autentifikatsiya, identifikatsiyalash va ko‘plab afzalliklarga ega. Bundan tashqari, yuzni tanish tizimi ulardan biridir bugungi kunda universitetga kirishning eng qulay usullari. Vaqtning katta qismi kollejga ajratilgan ta'lim maqsadlari olimlarning davomatini olish vazifasiga sarflanadi. Talabalar kursida jonli ishtirok etishi uchun davomat tizimi talab qilinadi. Bu ko'pincha muammochunki bu o'qituvchilarning qimmatli vaqtini oladi, bu esa yanada samaraliroq ishlarga sarflanishi mumkin o'qitish va talabalar bilan muloqot qilish kabi. Ushbu maqola a ning batafsil bajarilishini taqdim etadi yuzni aniqlash va uning natijalari bilan qo'llab-quvvatlanadigan real vaqt rejimida qo'ng'iroq qilish tizimi. Ushbu maqola yuzni tanib olishdan foydalangan holda talabalarning davomatini avtomatik aniqlash tizimini taklif qildi muayyan sinfda qatnashayotgan olimlarning hisobini yuritadi. Talabaning yuzini tan olish uchun tizim birinchi navbatda kodning tasvirini juda katta ma'lumotlar bazasida havola sifatida olish va saqlash kerak. Sovg'a paytida fotosuratlar talabaning tan olinishi uchun yuz suratlarini oladi, shuning uchun kompyuter avtomatik ravishda yuzni aniqlaydi va rasmlarga mos keladigan talaba ismini aniqlaydi; va nihoyat, Excel fayli yuzni tanishni qo'llab-quvvatlash uchun davomat yozuvlari uchun eksport qilinadi natijalar. Tizim ichida yuzlarni aniqlash uchun oldindan o'rgatilgan CODESYS modeli qo'llaniladi fotosuratlar. Davomat tizimining ijobiy va salbiy tomonlarini o'rganish uchun so'rovnoma o'tkazildi kollej ta'limini boshqarish.

Tadqiqotda imzoni avtomatik tanib olish jarayonida qo‘llaniladigan algoritmlarning turlari va ularning samaradorlik darajasi tahlil qilinadi. Klassik yondashuvlar — Template Matching, Euclidean Distance, Dynamic Time Warping (DTW) — va zamonaviy metodlar — Support Vector Machine (SVM), Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM) — funksional va texnologik mezonlar asosida solishtirildi. Tadqiqot natijalari klassik metodlar oddiy strukturali tizimlarda samarali bo‘lsa-da, zamonaviy chuqur o‘rganish modellarining aniqlik darajasi, barqarorlik va moslashuvchanlikda ustunligini ko‘rsatdi.

Ushbu tezis zamonaviy web ilovalarida sun’iy intellekt texnologiyalarining qo’llanilishi, ular orqali erishilayotgan natijalar va kelajakdagi istiqbollarni o’rganishga qaratilgan. Web texnologiyalar va sun’iy intellekt integratsiyasi foydalanuvchi tajribasini yaxshilash, ma’lumotlarni qayta ishlash samaradorligini oshirish va biznes jarayonlarini avtomatlashtirish imkoniyatlarini taqdim etmoqda. Tezisda real vaqt rejimida ishlaydigan sun’iy intellektga asoslangan web ilovalarning asosiy komponentlari, arxitektura yechimlar va amaliy misollar ko’rib chiqiladi.

The article discusses the possibilities of using artificial intelligence technologies in training future historians. As you know, the most popular artificial intelligence chatbot currently is the ChatGPT application. Unfortunately, access to this service for users from Russia and Belarus is currently limited. However, there are many third-party applications free from such restrictions that use the ChatGPT API and solve similar problems. The article analyzes three of the most interesting and useful of these services: Phind.com - a search engine for researchers, Talkai.info - a service that provides access to ChatGPT in Russian, Explainlikeimfive.io - an application that explains complex concepts and concepts. Examples are given of the use of the above services in planning, organizing and conducting the educational process at the faculty that trains historians. It is shown that the rapid development of artificial intelligence technologies will lead to a radical change in approaches to organizing the educational process, its structure and content. According to some experts, by the end of 2026, ninety percent of the content on the Internet will be generated by artificial intelligence. This cannot but affect educational content posted on the Internet. In conclusion, it is noted that, unfortunately, solutions obtained using deep neural networks are often impossible to verify. This naturally reduces their value. That is why it is still difficult to judge how the widespread introduction of artificial intelligence technologies will affect the quality of specialist training.  

The article discusses the use of artificial intelligence in education, as well as solving problems of personalization of training and career guidance. The need to introduce artificial intelligence into the learning process, as well as technologies that are already in use, has been identified and analyzed. The possibilities of using artificial intelligence in personalizing learning are presented. Having analyzed the possibilities of using artificial intelligence, we came to the conclusion that there is a need to use and improve the technologies of neural networks and artificial intelligence in education.