The report discusses the creation of scientific and educational information search systems based on the integration of modules based on artificial intelligence methods. The urgency of the problem is dictated by the need to increase the efficiency of information search in databases to solve scientific, educational and technological problems.

This article shows that in the context of Industry 4.0. digital technologies in higher education contribute to improving the quality, detail and visibility of the educational process, allow building individual learning paths for each student, provide new opportunities for improving the qualifications of the university’s teaching staff, which ultimately serves as a guarantee of training highly qualified specialists for industries and sectors of the economy.

 The article is devoted to the problem of unequal access to distance education for evening students, due to employment, domestic conditions, and technical limitations. The use of intelligent systems is proposed as a tool for the personalization of the educational process, adaptation of learning materials, and the organization of flexible communication between students and teachers. The article presents examples of possible solutions, the architecture of intelligent support, and the potential effect of implementation at the university level.

The use of artificial intelligence technologies in teaching mathematics and probability theory improves the quality of education, making it more accessible and engaging for students. The application of a personalized approach, interactive methods, and gamification contributes to the development of analytical thinking and independent learning skills.

The article is devoted to modem problems in the methodology of teaching the subject of physics. Examples from the history of the development of physics teaching methodology are presented. The role of physics in the educational process of schools, universities, and other educational institutions is defined. Modem trends in teaching methodologies arc described. Regularities in the development of physics teaching methodology are provided. Comparisons of the study of theoretical material and the conduct of laboratory work arc given. Regularities in the development of physics teaching methodology are indicated.

The paper presents a mathematical model and the results of numerical calculations designed to analyze and predict the distribution of pollutants in the atmospheric surface layer. The model accounts for the dynamics of pollutant concentration reduction due to their natural decomposition and photochemical transformation, the influence of changes in wind patterns and terrain topography, and variations in diffusion and turbulent mixing coefficients depending on the stability of atmospheric stratification. High accuracy and stability of results arc achieved through the use of a semi-implicit finite-difference scheme and the method of lines for solving the specified problems.

The digitalization of healthcare demands automated analysis of dermoscopic images. These images are crucial for the early detection of skin lesions, particularly melanoma. This paper discusses the main challenges in image processing non-standard formats, dataset imbalance, and feature ambiguity and presents a sequence diagram designed for an automated analysis system based on these challenges.

This paper examines the significance of interactive platforms in fostering students’ creative potential. The study analyzes the impact of these platforms on educational processes and identifies strategies to stimulate creative thought through their utilization. Additionally, it provides practical guidance on how to promote creativity in educational settings through the utilization of contemporary digital platforms.

The article examines the models of managing an organization, provides the concepts of a business model and an operating model. It shows the role and place of Al systems in decision-making in the operational activities of an organization.

This article analyzes the processes of analyzing and classifying textual data, considers the types of textual data, namely structured, unstructured and semi-structured data, and presents their characteristics. In addition, special attention is paid to the existing opportunities and problems in processing textual data in the Uzbek language. In particular, the achievements and shortcomings of analyzing textual data in the Uzbek language using the example of the "Tahrirchi" system are presented.

This article shows how digital technologies play an important role in developing critical thinking skills among future elementary school teachers. It also mentions the need to use innovative teaching methods to eliminate problems that arise in the process.

Artificial intelligence (Al) models arc creating numerous opportunities for the intelligent analysis of medical images. However, the effective performance of these models requires large and high-quality labeled datasets. Since collecting such data in the medical field is challenging and costly, data augmentation methods play a crucial role.

This article analyzes the web server architecture, hardware, and software for monitoring and controlling well water levels, based on the ESP32 microcontroller. Furthermore, it explores the potential of using loT (Internet of Things) technologies to enhance real-time monitoring capabilities on the ESP32 platform. The article provides a thorough analysis of methods and techniques for groundwater observation and control. Experimental results regarding the technical parameters of the developed equipment and the performance speed of the software are presented. This system has potential for wide application in efficient water resource management, ecological monitoring, and the agricultural sector.

The article is devoted to the study of the impact of digital transformation and artificial intelligence on the formation of sustainable ecosystems in higher education. The paper discusses theoretical and practical aspects of the implementation of advanced digital technologies such as cloud services, big data, artificial intelligence and machine learning, which provide new opportunities for optimizing the educational process and management of educational institutions.

This article is dedicated to the application of modern educational technologies aimed at improving the quality of education and enhancing its educational significance in higher education institutions. The role, effectiveness, and challenges of implementing these technologies within educational institutions are analyzed. The article provides recommendations and suggestions for enriching the higher education system with innovative technologies, improving teaching methods, and enhancing their educational functions. This article aims to provide practical recommendations for organizing pedagogical processes in higher education more efficiently and effectively.

This article provides a comprehensive analysis of the needs of state organizations for modem graduates and the requirements for them in the conditions of the Republic of Uzbekistan. It also discusses cooperation with higher education institutions in the public sector, the practice system, and educational strategies that are appropriate for the labor market. Through the analysis, proposals arc made to improve the quality of education and improve the personnel training system.

This work analyzes the historical development, operating principles, and modem approaches of X-ray imaging technologies. It mainly considers technologies ranging from traditional X-ray imaging methods to advanced systems integrated with artificial intelligence. The work also analyzes the advantages and disadvantages of X-ray systems, which will serve as an important source of information for medical professionals and researchers.

The article provides methodological recommendations on the use of the concept of artificial intelligence and its essence in education when teaching robotics to schoolchildren. During the lesson, the concept of artificial intelligence and effective aspects of using its essence were studied.

This study is devoted to the analysis of existing systems for detecting human emotions through speech signals, mainly open and closed-source systems are studied and their principles of operation, technical characteristics, capabilities and areas of application are compared. The work also shows the characteristics of the operation and accuracy levels of systems for detecting human emotions through speech signals in different languages.

This article talks about the experience and effectiveness of the use of drones agricultural activities of economically developed countries of the world.

In order to organize efficient use of dry land, this article describes the application of water-saving technologies and methods of water use and irrigation in dry land.

This article analyzes the role of digital technologies and artificial intelligence in modern society, especially their application in science, education, and industry. The paper highlights practical uses of Al technologies, their impact on human activity, efficiency, and development prospects.

The article presents data on the need to use digital technologies in land management, including in the development of land management schemes, as well as on currently used digital technologies and software.

Today, due to the development of household appliances and intelligent systems, it is proposed to install renewable energy sources, backup sources, and a central energy supply network with flexible control to address the problems associated with increased demands on energy supply systems. For this purpose, this article develops a project of the application technology, scope and database for collecting and processing monitoring data based on data sources of hybrid energy supply sources and loT.

This article discusses the application of innovative pedagogical technologies in improving educational efficiency. The article analyzes the impact of innovative techniques such as interactive teaching, gamification, online learning, and personal approach on the educational process. Based on the results of the study, it was shown that an improvement in student motivation, academic performance and creative potential was achieved.

This article explores the importance of pedagogical technologies in teaching history. It is shown how effectively the process of historical cognition of students is carried out using innovative teaching methods, in particular, interactive games, group discussions and practical exercises. The role of parental and public participation in the educational process, increasing students’ interest in history, is discussed.

The purpose of this study is to analyze the important role that artificial intelligence (Al) plays in the educational process. The article explores how modem Al technologies can solve a number of problems in the field of education, such as barriers to access to educational materials, learning difficulties and a shortage of educational resources.

The article explores the role of intelligent systems and digital technologies in assessing and developing the export potential of Uzbekistan’s regions in the context of international integration. The current problems arc analysed and recommendations on effective implementation of digital tools for sustainable export growth are offered.

Artificial intelligence (Al) is becoming an integral part of everyday life, actively influencing many aspects of human activity. Al technologies are used to automate routine tasks, improve the quality of service and enhance convenience in various fields, such as medicine, education, transportation, finance and entertainment. For example, voice assistants, recommendation systems, smart homes and chatbots significantly simplify the performance of daily tasks. However, along with the benefits, Al raises questions related to ethics, privacy and data security. The impact of artificial intelligence on the labor market raises concerns about replacing human labor, while the rapid development of technology generates the need to adapt to new conditions. This topic emphasizes the importance of studying the benefits and risks associated with the implementation of Al, as well as developing strategics for its effective use to improve the quality of life and minimize possible threats. Keywords: Artificial intelligence (Al), machine learning, deep learning, automation, smart devices, robotics, data analysis, computer vision, chatbots, digital transformation, social networks and algorithms.

The article highlights foreign experience in land management, paying special attention to the problems and difficulties in land management. Described the main classifications of cadastral registration systems in foreign countries.

This article analyzes the possibilities of artificial intelligence technologies in digitization, systematization and analysis of scientific and historical heritage. Using the example of archival materials from the Institute of Physics and Technology, the methodology and software tools of the digital platform created on the basis of practical experience are considered. The article analyzes such areas as digitization of archival documents using OCR technologies, semantic analysis, automatic classification and graphing of scientific knowledge.

The article examines pedagogical approaches to spiritual and moral education for future teachers based on works by Sharaf Rashidov. It describes a functional model for such education and innovative methods for its implementation.

This paper presents the application of intuitionistic fuzzy sets and aggregation operators in solving multi-criteria decision-making problems. The study focuses on the use of the intuitionistic fuzzy Choquet integral operator, which effectively handles the interdependencies among criteria while aggregating linguistic evaluations expressed as intuitionistic fuzzy values.

This article analyzes the preprocessing of remote sensing images based on the discrete histogram model. The effectiveness, significance, quality, and uniqueness of the model arc highlighted. In addition, the practical importance of infrared channels in enhancing the efficiency of object recognition in remote sensing of the Earth is demonstrated.

This article covers the process of improving the quality of videos, optimizing and analyzing images. The article reviews modem technologies, in particular, methods based on artificial intelligence and machine learning, as well as approaches such as reducing noise in videos, image restoration, color correction and improving video compression.

This research work is devoted to the segmentation of X-ray images of the lungs, in which the idea of improving the traditional U-Net architecture with residual connections and adaptive training mechanisms is put forward, and this model is tested in experiments. As a result, it is noted that the model achieves high accuracy, Dice coefficient and IoU indicators.

The article considers neural network forecasting of the geomagnetic К - index with using neural networks. The construction of neural networks was carried out for a multilayer perceptron and a network of radial basis functions. Neural network models have a minimal error in short-term forecasting of the geomagnetic index К compared to statistical models.

The article explores approaches based on machine learning for the prevention of endocrine diseases in medicine, highlighting their advantages and prospects for application. The possibility of predicting early stages of diseases using data collection, analysis, and machine learning algorithms is examined.

This paper investigates the role of digital models and metadata-based systems in analyzing and optimizing technological processes in the machining industry. Using cylindrical plunge grinding as an example, modeled in the software environment, the study provides a step-by-step analysis of the dynamic system involved.

In the theory of thennoelasticity, problems are typically solved in terms of stress by introducing Airy’s stress functions. However, in this work, the problem of thennoelasticity is formulated and solved directly in terms of stresses in a spatial setting, without introducing any additional functions. The algorithm for the solution is also presented.

This article examines the role of speech signals in personal identification systems, as well as the processes of feature extraction and parameter formation. The biometric uniqueness of a speech signal is based on the distinctive phonetic and acoustic characteristics of each individual’s voice.

This study examines the surface composition and atomic distribution of the Si-Cu(100) system after implantation with Ch+ ions. For the first time, the distribution of atoms across the surface and within the surface layer of the Si/Cu(100) system has been analyzed following ion implantation. The findings demonstrate that by implanting pure Si/Cu with O2+ ions and applying heat, it is possible to obtain a SiCh nanofilm with a thickness of approximately 1.5-2 nm.

This study proposes a novel approach based on the Kohonen map for denoising speech signals. In this method, noisy clusters were identified using the Kohonen map based on speech frequency and energy characteristics, while the "Minimum Statistics Noise Estimation" method was used to estimate the noise level. This approach allowed for stable results even at high noise levels. As features, MFCC was used for low noise levels, while the Log-Mel spectrogram was employed for high noise levels. Experiments were conducted at various noise levels (1%, 5%, 10%, 15%, 20%, 25% white noise), and the results were evaluated using the PESQ (Perceptual Evaluation of Speech Quality) metric. The proposed approach demonstrated that combining an energy-based criterion with frequency characteristics for identifying noisy clusters significantly improves speech quality.

The purpose of this article is to develop a set of mathematical models for solving many issues related to the use of natural resources from an ecological and economic point of view. These models should describe specific aspects of the implementation of the system under study, taking into account various characteristics of ecological and economic systems. In this case, you will have to solve a large number of new issues. These questions arise in the modeling and analysis of ecological and economic systems.

This study comprehensively analyzes palmprint databases as a fundamental resource for biometric identification systems. It provides a detailed exploration of the database creation process, their technical specifications, and application areas. Existing databases such as CASIA Palmprint, NEC Palm Database, and PolyU, Multispectral have also been examined.During the research, issues related to quality, privacy concerns, standardization challenges, and technical limitations were identified, with recommendations proposed to address them.

This research focuses on analyzing the key concepts and characteristics of weakly formed processes, which are observed in medical, social, and technological systems. It discusses aspects such as uncertainty, multifactorial nature, and the complexity of formal modeling.

In this study, mathematical modeling was carried out to calculate the diffusion process of harmful substances in the atmosphere, taking into account diffusion coefficients such as wind speed, particle size, and temperature.

This study analyzes various types of the IDEF methodology, examining the most suitable methodologies for modeling speech-to-text (STT) and text-to-speech (TTS) conversion processes, as well as translation systems from Uzbek to Russian and English. Based on the research findings, IDEFO and IDEF3 were selected as the most optimal methodologies for STT and TTS processes, while IDEF IX was chosen for translation systems. Based on this model, a web application was developed for processing users’ speech and text data.

This paper analyzes the algorithms for processing classical images into quantum images, which is one of the important stages of quantum image processing. Quantum generation with 8,000 shots on IBM real-time computer and Aer simulator, presents the proposed products for efficient encoding of images into quantum format and optimization of qubit loading size. The research can contribute to the development of quantum image processing systems.

The paper presents numerical modeling of a three-dimensional wind velocity field in the atmosphere based on the Navicr-Stokes equations. A stable algorithm for solving the hydrodynamic problem using an implicit difference scheme and high-order approximation is developed. The model takes into account the spatiotemporal variability of air mass velocity in the u, V, and w directions, which allows for a more accurate description of the processes of pollutant transfer in the atmosphere.

This paper examines the problem of finding and analyzing the solution of a boundary value problem for a second-order singular differential equation using the spectral method based on Chebyshev polynomials.

This article examines the effectiveness of ensemble algorithms based on partial precedent principles for cancer stage classification. A weighted decision-making mechanism using linear convolution demonstrated high accuracy in classifying cancer types (C16, C17, C18, C44, C50, COO). A classification method based on Manhattan distance and threshold values was developed and compared with classical algorithms. The proposed ensemble algorithm improves classification accuracy to 94.7-96.2%, which is 3-10% higher than existing classical algorithms.

This research paper is devoted to the problem of classifying pathologies in renal computed tomography images using deep neural networks, where not only binary classification such as «normal/pathological» but also complex differential diagnosis issues between pathologies arc analyzed. The paper also proposes a combinatorial approach to classifying pathologies, in which it is shown that initially making a general diagnosis using a four-class classification model, and in doubtful cases, additional examination using binary or ternary models is acceptable for clinical practice.

The article is devoted to solving the problem of selecting precedent objects, which is an important aspect of multi-clustering in pattern recognition and classification processes. A new algorithm for selecting precedent objects, taking into account the space of nominal and numerical features, is proposed. This algorithm ensures accurate, robust, and transparent data classification, enhancing the effectiveness of analytical decision-making, especially in medical diagnostic processes.

This article analyzes methods of collecting palmpnnt data in the field of human biometric identification. It primarily compares two types of data acquisition methods - offline and online - highlighting their advantages and disadvantages. The offline method des-cribcs the technique of collecting data based on inked images, while the online method discusses the architecture of real-time operating devices, user interface, and optical system requirements.

The article Methods for extracting objects from images The main types of algorithms for segmenting objects in images are studied and information about their main advantages and disadvantages is provided. The options for choosing one or more of the methods, depending on the nature of the problem, are presented.

This article examines the role of algorithms in solving artificial intelligence (Al) problems and ways to increase their effectiveness. The main algorithmic approaches used in systems (Al) will be analyzed, as well as their advantages and limitations will be highlighted. The article uses advanced literature that will help to study the theoretical and practical aspects of artificial intelligence algorithms.

This abstract focuses on the development and application of algorithms for processing X-ray images of the human foot, which are crucial for diagnosing various foot conditions, including fractures, deformities, and joint diseases. The study examines various image processing techniques, including edge detection, segmentation, and feature extraction, to enhance the quality of X-ray images and improve diagnostic accuracy. Furthermore, the paper discusses the challenges of handling noise, distortion, and low contrast in X-ray images and presents methods for mitigating these issues while preserving critical details. Through the implementation of these algorithms, the study aims to enhance the efficiency of foot-related diagnoses and contribute to more effective clinical decision-making.

Nowadays, systems ensuring natural interaction between humans and machines are rapidly evolving. Among them, the task of identifying the user’s language holds particular importance. This article analyzes the problem of language identification (LID) based on speech signals, its application areas, challenges, and modem approaches. It compares traditional machine learning methods (GMM, SVM, i-vcctor) with deep neural network-based approaches (CNN, RNN, Transformer) for language recognition. Additionally, the paper discusses key evaluation metrics such as Accuracy, Precision, Fl-score, and Equal Error Rate (EER) for assessing system performance. Advanced methods for handling complex scenarios like code-switching and openset LID are reviewed, with a focus on practical perspectives for under-resourced languages like Uzbek. The results of the study provide a solid theoretical and practical foundation for developing multilingual interactive voice systems.

This paper examines modem algorithms used for the intelligent analysis of speech signals and their scientific and practical significance. The development of artificial intelligence and machine learning technologies has expanded the capabilities of automatic speech signal processing, feature extraction, and recognition. The study analyzes modeling processes based on advanced methods such as MFCC, CNN, and RNN. It also explores algorithms used for spccch-to-tcxt conversion, speaker identification, and context understanding. The results may be applied in intelligent voice interfaces, security systems, and linguistic applications.

Ҳар бир инсон хаёти давомида қайсидир йўналишда қандайдир даражада из қолдиради. Баъзилар билинар-билинмас из қолдирса, айрим катта ҳарфлар билан ёзилувчи инсонлар хаётда шундай из қолдирадики, ундан нафақат ушбу инсонни ёдлаш, балки, бир нсча авлод ёшлари учун ўрнак машъали ва тимсоли бўлиб қолади.

This article proposes a deep learning-based model for extracting key entities from texts and creating a knowledge base. The Long Short-Term Memory (LSTM) model is used for the Named Entity Recognition (NER) task. The data is preproccssed and converted into a digital format using tokenization and one-hot encoding. The model is trained and evaluated to extract various types of entities (e.g., person names, dates, and location names). Experimental results demonstrate the model’s effectiveness, and the impact of different parameters is analyzed.

This article is dedicated to the necessity of developing predictive models based on data for making quality decisions in management systems. Methods based on time scries in the process of data analysis and forecasting are presented. The importance of time series analysis in shaping management strategy, its impact on accuracy and efficiency, as well as the analysis of mathematical models and algorithms necessary to enhance the reliability of results, are discussed.