DTAI – 2024: “Digital transformation and artificial intelligence: problems, innovations and trends” I international scientific - practical conference (September 11, 2024 - Tashkent city)
FAST VOICE FILTERING IN A FEW STEPS USING VOICE CONVERSION AS A POSTPROCESSING MODULE ADAPTATION OF A SPEAKER FROM UZBEK TEXT TO SPEECH
Text-to-Speech (TTS) systems developed in recent years require hours of recorded speech data to generate high-fidelity human-like synthetic speech. Low resources or small amount of speech can lead to several problems in the development of TTs models, which makes it difficult to train TTS systems with limited resources. This paper proposes a new lowresource TTS method called Voice Filter that uses only one minute of the target speaker's speech. It applies Voice Conversion (VC) as a post-processing module added to an already existing high-quality TTS system, which marks a conceptual change in the current TTS paradigm by recasting the multi-frame TTS problem as a VC task. In addition, it has been proposed to use a TTS system with controlled duration to create a parallel speech corpus that facilitates the VC task. The results show that Voice Filter outperforms modern multi-frame speech synthesis methods based on objective and subjective metrics using only one minute of speech from a diverse set of sounds, and at the same time with the Uzbek TTS model. remains competitive. 25 times more data.
ANALYSIS OF FILTERS FOR PROCESSING VIDEO IMAGES
Every day the number of images taken by the camera is increasing. Many photos are taken outdoors, where weather conditions can affect the quality of the captured images. Degradation caused by atmospheric phenomena is difficult to see by the human eye, and also reduces recognition by computer vision algorithms. Therefore, the task of processing such images is the most important. This article is devoted to the processing of images distorted by fog or rain. All modern methods in this field are based on supervised learning. In addition, information is provided about some image processing methods and their quality, intensity levels, and pixels whose GT exceeds a specified threshold are considered noise.
The Modern trends in the application of intelligent systems in the management of economic objects
The article is devoted to the issues of improving the management processes of economic entities based on the use of artificial intelligence technologies. The work provides the general structure and components of an intelligent information system - a knowledge base, a decision-making mechanism and an intelligent interface. The knowledge base is characterized as a set of models, rules and data that allow generating and analyzing conclusions to find solutions to complex problems in a certain subject area.
ALGORITHM FOR CONSTRUCTING AND CONFIGURING PARAMETERS OF A MODEL FOR SEARCHING FOR TRACES OF ATTACKS IN AN INFORMATION SYSTEM
In the information system, it is not possible to constantly monitor the activity of users, that is, monitoring in real time mode is inconvenient. Therefore, it is important to form the actions of users in the system on the basis of parameters based on their role, and to create rules for searching for attack traces in the future detection of attacks, and to configure models and parameters for searching and detecting attack traces based on these rules.
THE ROLE AND APPLICATION OF ARTIFICIAL INTELLIGENCE IN IDENTIFYING THREATS TO INFORMATION SYSTEMS
In this article, In recent years, artificial intelligence has become a necessary technology to enhance the efforts of information security professionals. From a security point of view, it is important that artificial intelligence can identify and prioritize risks, immediately detect any malware on the network, respond to incidents and detect attacks in advance, and develop algorithms to detect and block botnets in computer networks.
COMPARATIVE ANALYSIS OF FACE RECOGNITION ALGORITHMS FOR AUTOPROCTORING: EIGENFACES, FISHERFACES, CNNS, AND YOLO
Auto proctoring, leveraging automated surveillance technologies, has emerged as a solution to monitor online examinations in educational settings. However, its efficacy and ethical implications remain subject to scrutiny. This scientific article presents a thorough investigation into the effectiveness and ethical considerations surrounding auto proctoring systems. Through a review of existing literature and empirical analysis, we aim to provide insights into the benefits, limitations, and ethical challenges associated with the widespread adoption of auto proctoring in educational assessment. Our findings underscore the need for a balanced approach that ensures both academic integrity and student privacy
PREDICTION OF EJECTION FRACTION OF LEFT VENTRICLE IN PATIENTS WITH TYPE 2 DIABETES MELLITUS ON EMPAGLIFLOZIN: A SIX-MONTH ASSESSMENT
Diabetes mellitus type 2 (T2DM) is a growing global health concern, often leading to cardiovascular complications, including left ventricular dysfunction. Predicting left ventricular ejection fraction (LVEF) can be crucial for early intervention and management. Empagliflozin, a sodium-glucose cotransporter 2 (SGLT2) inhibitor, has garnered attention for its efficacy in managing type 2 diabetes mellitus (T2DM) and its potential cardiovascular benefits. This study aims to identify significant predictors of LVEF in T2DM patients treated with Empagliflozin after six months of therapy. We aim to identify outcomes related to cardiac function and the predictive factors influencing LVEF changes during this treatment phase. We applied a systematic feature selection approach through generalized linear models (GLM) to build a predictive model based on a cohort of 130 patients.
DIGITAL TRANSFORMATION AND ARTIFICIAL INTELLIGENCE IN UZBEKISTAN: CHALLENGES, INNOVATIONS, AND FUTURE TRENDS
The digital transformation and integration of artificial intelligence (AI) are pivotal to the advancement of modern economies. Uzbekistan, a country in Central Asia, is making significant strides in these areas despite facing numerous challenges. This paper explores the current state of digital transformation and AI in Uzbekistan, identifies key challenges, highlights recent innovations, and discusses emerging trends. The findings suggest a growing emphasis on building digital infrastructure, fostering innovation, and creating a supportive regulatory environment, which are crucial for Uzbekistan’s economic and social development.
Digital transformation, artificial intelligence (ai), uzbekistan, digital infrastructure, e-government, fintech, smart cities, educational reforms, innovation ecosystems, data governance, regulatory framework, skill development, international collaboration, technology adoption, economic development.
APPLICATION OF THE ALGORITHM FOR ENRICHMENT THE KNOWLEDGE GRAPH WITH NUMERICAL PREDICATES IN DECISION-MAKING SUPPORT SYSTEM
In this paper, the theoretical and practical principles of creating a knowledge graph by forming a set of rules for expert systems are studied. At the same time, the method of enriching the graph made from the predicates created according to First Order Logic by numerical predicates was studied. as the object of the research, the classification problem of selecting crops for repeated cropping was taken, among which, using the set of real data collected in agriculture, test-experimental work was carried out on the algorithm mentioned above and the results were obtained. All results were presented in table and graph form.
THE ROLE OF HUMAN JOURNALISTS IN AN AI-DRIVEN MEDIA LANDSCAPE
As artificial intelligence (AI) continues to advance, its impact on the journalism industry is becoming increasingly evident. AI-powered tools are being used to automate tasks such as content creation, fact-checking, and distribution. However, while AI can enhance efficiency and productivity, it cannot fully replace the unique contributions of human journalists. Human journalists bring a level of critical thinking, creativity, and empathy that AI cannot replicate. They are able to interpret complex information, identify biases, and provide context to news stories. Additionally, human journalists can develop relationships with sources and build trust with audiences, which is essential for credible and reliable journalism. However, the role of human journalists is evolving in an AI-driven media landscape. Journalists must adapt to new technologies and develop skills such as data analysis, digital storytelling, and social media engagement. They must also be mindful of the ethical implications of using AI and ensure that it is used responsibly and transparently. So, human journalists will continue to play a vital role in the media industry, even as AI becomes more sophisticated. By combining their unique skills with the capabilities of AI, journalists can produce more accurate, informative, and engaging content that serves the public interest.
MODELING AND STORING DATA IN GRAPH DATABASES
This paper presents the study of graph databases and their application in the context of social networks. It describes a data model that represents users, communities, and the relationships between them as a graph, where nodes represent objects and edges represent their relationships. The data structure in graph databases is compared to relational databases, emphasizing the freedom and flexibility to create and modify relationships between nodes without strict restrictions. A method for storing graph data structures is discussed, including the possibility of storing them in SQL tables using JSON, and the use of specialized graph DBMSs such as Neo4j. The specifics of data storage in Neo4j are highlighted, including caching for improving read/write performance and optimizing graph traversal. The paper emphasizes the Cypher query language, which is specifically used in Neo4j to work with graph data. Example queries with explanations are provided, demonstrating the capabilities of the Cypher language for working with data in graph databases. The conclusion discusses the application areas of graph databases, including fraud detection and supply chain mapping, how graph databases provide flexible options for storing information, and highlights their wide range of applications in various fields.
Comparison of machine learning methods in bad loan predictions: analysis of micro finance data
Credit risk management is crucial for lending institutions to safeguard themselves against financial losses and maintain financial stability. Machine learning methods have been useful in analyzing borrower data and identifying bad loans that would be almost impossible for humans to detect. Client data from Microfinance Institution (MFI) in Uzbekistan have been used to build machine learning methods to predict loan delinquency. Their performances were evaluated based on five metrics: accuracy, sensitivity, specificity, negative (npv) and positive predictive value (ppv). The findings suggest that none of the machine learning used in the study methods have absolute advantage over the rest in all five-performance metrics. However, Extreme Gradient Boosting (XGBoost) produced the highest average performance compared to other methods.
THE ROLE OF ARTIFICIAL INTELLIGENCE-BASED SOLUTIONS IN ANTI-MONEY LAUNDERING COMPLIANCE
In this study, we will explore the role of artificial intelligence-based solutions in tackling Anti-Money Laundering efforts of commercial banks with due focus on efficiency enhancement in solving various tasks. Particular attention will be paid on the applicability of AI-based solutions in Anti-Money Laundering Compliance units of commercial banks in conditions of Uzbekistan. The study will reveal that the use of AI-based technologies in AML Compliance enhances efficiency leading to decrease of time and costs while processing customer transactions and increase overall performance of these units in commercial banks.
POSSIBILITIES OF APPLYING INTELLIGENT TECHNOLOGIES IN PRODUCTION AUTOMATION
This paper examines the potential for using intelligent technologies in technological processes. These technologies are widely used in electric power organizations to automate production management. The main objective of this study is to analyze the main development trends and study the Smart Grid concept, as well as determine the potential for its implementation based on the goals and needs of key stakeholders in different industries.
Intelligent technologies, operation, power supply, electronic meters, generation
ALGORITHMS FOR RECOGNITION OF HISTOLOGICAL IMAGES BASED ON THRESHOLD RULES
The article discusses various histological image recognition algorithms to improve the accuracy of pathology diagnostics. Particular attention is paid to the proposed threshold rule-based method, which demonstrates higher classification accuracy and training speed compared to popular machine learning methods such as SVM, Random Forest, and XGBoost. During the experiments, the proposed method showed high accuracy with minimal training and classification time. The results of a comparative analysis by key metrics are presented, confirming the effectiveness of the proposed approach for automating diagnostics in digital pathology.
THE INFLUENCE OF INVESTMENTS ON INNOVATIVE ACTIVITIES IN ENTERPRISES ON THE PROFITABILITY OF ASSETS
The article analyzes the impact of investments in innovative activities on the profitability of the company's assets. The article examines the mechanisms of this effect, provides statistical analysis data, and develops recommendations for managing investments in innovation to increase the profitability of assets.
Innovations, Investments, Return on assets, Innovative activity, Financial management.
THE MAIN COMPONENTS OF "SMART FARM" FARMS WITH ADVANCED TECHNOLOGY
This article examines the impact of smart farming technologies on livestock management, focusing on the implementation of an automated management information system. By leveraging IoT devices, sensors, and RFID systems, the system streamlines farm operations, reduces costs, and enhances product quality. Using the IDEF0 methodology, we model farm processes and design a robust communication infrastructure and databases to support real-time decisionmaking. The findings highlight significant improvements in efficiency, animal welfare, and overall productivity, demonstrating the critical role of smart technologies in the future of sustainable livestock farming.
ARCHITECTURE AND PRIORITY ISSUES OF INTELLIGENT MILKING SYSTEM ON THE FARM
The dairy industry relies heavily on accurate milk measurement for productivity and quality control. Traditional manual methods are error-prone and labor-intensive. This paper discusses the development of smart milking systems that use advanced sensors and computational devices to measure milk parameters such as volume, flow, fat content, and pH in real time. These systems not only improve accuracy and efficiency but also enhance herd management, animal health, and farm profitability. Additionally, they aid in early disease detection and ensure milk safety. The paper explores the design, sensor selection, and integration of these systems, and proposes an intelligent milking system architecture.
APPLICATION OF THE RANDOM FOREST ALGORITHM FOR EARLY DETECTION OF LAMENESS IN DAIRY COWS
This study explores the application of pedometers as a tool for the early detection of lameness in dairy cattle. By continuously monitoring cattle activity through pedometer data, including step count, distance traveled, and other physical activity parameters, we aim to develop a machine learning-based system capable of identifying early signs of lameness. The research highlights the advantages of using pedometers attached to the legs of cattle, which offer more accurate data collection compared to other wearable devices.
MAIN CHALLENGES RELATED TO ARTIFICIAL INTELLIGENCE IN THE FIELDS OF INFORMATION TECHNOLOGY
The change of generations of computing technology is equivalent to another scientific and technological revolution. With the advent of new generations of computers, communication facilities, multiprocessor systems and the development of Internet technologies, it will be possible not only to solve fundamentally new problems in all areas of science and technology, but also to significantly expand the possibilities for their implementation. Solving previously traditional problems at a new, qualitative level, the qualitative level of solving problems, first of all, involves providing the necessary and sufficient intellectual support. Intellectualization of information and computing systems means not only the use of new-generation tools, but also the use of a new generation of mathematical, algorithmic and software tools. Information and computing systems with intellectual support are usually used to solve complex problems where logical information processing is superior to computational processing. Examples of such problems are: understanding and synthesizing texts in natural language, understanding and synthesizing speech, analyzing visual information, controlling robots, analyzing situations and making decisions.
Artificial intelligence, management information systems, database, development of yield assessment program, Risk management
IMPORTANCE OF DIGITAL TECHNOLOGIES IN MEASUREMENT OF KIDNEY FUNCTION
Non‐adherence to medications is a critical challenge in the management of people with chronic kidney disease (CKD). This review explores the complexities of adherence in this population, the unique barriers and enablers of good adherence behaviours, and the role of emerging digital health technologies in bridging the gap between evidence‐based treatment plans and the real‐world standard of care.
chronic kidney disease (CKD), digital health, eHealth, medication adherence
Bulutli hisoblash - bu mahalliy serverlar yoki shaxsiy kompyuterlarda emas, balki Internet tarmog'ida joylashgan tarmoq serverlarida katta hajmdagi ma'lumotlarni saralash, saqlash va qayta ishlash uchun talab bo'yicha xizmatdir. Tarmoqdagi ilova va bulut xizmatlarini ishga tushirish orqali foydalanuvchiga virtual resurs taqdim etiladi. Ushbu hujjatni namoyish qilish uchun Hadoop va MapReduce infratuzilmasiga asoslangan dastur amalga oshirildi. Ushbu ilova turli parametrlar bo'yicha sinovdan o'tgan. Taklif etilgan usulning samaradorligi eksperimental natijalarda ko'rsatilgan. Ushbu usul boshqa zamonaviy hisoblash usullariga nisbatan sezilarli yaxshilanishni anglatadi.
Revolutionizing Classrooms: The Role of AI in Personalized Learning and Student Engagement
This paper explores the transformative impact of artificial intelligence (AI) on education, specifically focusing on personalized learning and student engagement. By analyzing recent advancements, practical applications, and challenges, it aims to illustrate how AI can revolutionize classroom experiences. This study employs a mixed-methods approach, including literature review, case studies, and expert interviews, to provide a comprehensive overview of AI's role in modern education.
REVOLUTIONIZING MEDICAL IMAGING: THE ROLE OF AI AND DEEP LEARNING IN DIAGNOSIS AND TREATMENT
The integration of Artificial Intelligence (AI) into medical imaging has revolutionized diagnostic practices, offering the potential for enhanced accuracy, speed, and reduction of errors in clinical decision-making. This article explores the key applications of AI in medical imaging, such as image segmentation, classification, object detection, and image generation, highlighting advanced techniques like U-Net, ResNet, YOLO, and Generative Adversarial Networks (GANs). Despite its transformative potential, AI in medical imaging faces significant challenges, including data privacy concerns, the need for large annotated datasets, model interpretability, and the risk of overfitting. Furthermore, current AI models are limited by potential biases in training data and difficulties in generalizing across diverse populations and imaging modalities. Addressing these challenges is essential to ensure that AI can be effectively and ethically integrated into healthcare, ultimately improving patient outcomes and advancing the field of medical diagnostics.
SEMI-STRUCTURED DECISIONS MAKING ON THE BASIS FUZZY MEASURES
The task selection, i.e. quality assessment of alternatives analyzed objects (information - communication systems, technical - technological objects, varieties of agricultural crops, etc.) and a selection of the best alternative in many cases solved in conditions of information, procedural and functional, parametric and criteria uncertainties of various types. The article considers the fuzzy-set approach to the construction of models of description and evaluation of alternatives, as well as problem solving semi-structured decision making based on fuzzy measures and fuzzy integral
PARALLELISM AND SUPERPOSITION: REASONS FOR THE SUPERIORITY OF QUBIT OVER CLASSICAL BIT
The future of computing is greatly influenced by the revolutionary characteristics of quantum bits (qubits) and quantum superposition, which offer significant advantages over classical computing. This paper explores the superiority of qubits compared to classical bits, delving into the principles of quantum superposition and its mathematical representation. Additionally, it examines the differences between Shor's algorithm and classical algorithms. Qubits, with their ability to exist in multiple states simultaneously due to quantum superposition, dramatically enhance computational efficiency. This paper provides an in-depth analysis of the mathematical aspects of qubits, highlights the advantages of quantum computing through algorithms like Shor's, and compares quantum and classical algorithms, discussing the capabilities and limitations of quantum computing.
EVALUATING THE PERFORMANCE OF CONVOLUTIONAL NEURAL NETWORKS AND HYBRID CNN-SVM MODELS FOR SYMBOL RECOGNITION IN COMPLEX DATASETS
In this paper, we propose an algorithm for identifying symbols in various contexts using intelligent data analysis methods. With the increasing need for automated systems to process symbolic data from sources such as images, texts, or audio, we explore several state-of-the-art techniques, including machine learning models, pattern recognition, and feature extraction. The proposed method improves symbol identification accuracy by integrating supervised learning with advanced feature engineering. Our results show a significant enhancement in symbol recognition rates compared to traditional approaches.
CREATION OF INTELLIGENCE SYSTEMS RELATED TO INFORMATION-LIBRARY ACTIVITIES
In this article, the need for adaptive learning platforms for students of higher education institutions is analyzed, the issues of creating an information model and functional structure of an adaptive learning platform that provides electronic literature, taking into account the indicators of students' mastery, are researched.
adaptive educational platforms, electronic library, intelligent systems, programming technologies, database, search systems.
OFFICIAL SOCIAL RELATIONS IN UNIVERSITY GRADUATES’ ADAPTATION TO THE LABOR MARKET
The selection of students’ informal (friendly) social connections as a significant determinant influencing their adaptation to the labor market is justified by the challenges of combining the concepts of social capital and network analysis; the reasons for the insufficient efficiency of interaction between the labor market and the higher education institution in the context of a multi-level system of professional training are systematized; three categories of elements have been identified as contributing to the establishment of students’ social networks, as well as the usual structure of these networks when they are studying together. The factors that influence choosing between pursuing a master’s degree and starting a career after earning a bachelor’s degree have been recognized as components of the process of labor market adaption;
EMPLOYMENT OF UNIVERSITY GRADUATES IN THE LABOR MARKET
The article notes that the modern labor market is characterized by the accelerated dynamics of transformational changes associated with the ongoing processes of adaptation of the economy to global challenges on a national and global scale, in the conditions of digitalization of the economy, the rapid development of new technologies, including in the interests of ensuring import substitution, the transformation process is accelerating professional competencies necessary for the implementation of the labor process,in connection with which the requirements of employers for applicants for available vacancies are constantly changing, which contributes to increased competition in the labor market, in which university graduates occupy far from leading positions, employment is becoming one of the acute problems of the modern labor market, characterized by the development of contradictory trends, on the one hand, employers report a shortage of qualified personnel, and, on the other hand, there is unemployment among university graduates who cannot find a job for several years after graduation.