APPLICATION OF ARTIFICIAL NEURAL NETWORKS IN SOCIETY

Аннотация

В статье анализируется применение искусственных нейронных сетей в обществе и их влияние на различные сферы. Искусственные нейронные сети — это технология, основанная на моделировании деятельности человеческого мозга, которая широко применяется при анализе сложных данных, принятии решений и прогнозировании. В обществе эта технология приводит к революционным изменениям в экономике, здравоохранении, образовании, транспорте, повседневной жизни и других сферах. С помощью искусственных нейронных сетей повышается эффективность производства, медицинская диагностика становится более точной, образование индивидуализируется, а транспортные системы работают более эффективно. Выпуск журнала №-26 Часть–6_ Май –2025 341 MODERN EDUCATION AND DEVELOPMENT В статье обсуждается положительное влияние искусственных нейронных сетей на общество и этические и социальные проблемы его развития. Ключевые слова: Искусственные нейронные сети, деятельность человеческого мозга, моделирование, технологии, принятие решений, прогнозирование, экономика, здравоохранение, образование, транспорт, повседневная жизнь, революционные изменения. Artificial neural networks (ANNs) are one of the main technologies of machine learning and artificial intelligence. These networks are based on modeling the human brain and are widely used in complex data processing, analysis, and decision-making. They consist of several layers of neurons and learn by analyzing incoming data. Currently, ANNs are used in many areas, including economics, healthcare, education, transportation, manufacturing, and everyday life. This article analyzes the application of ANNs in society and their social and economic impact in various areas. Principle of Operation of ANNs. As its name suggests, an ANN imitates the work of brain neurons. Each neuron receives input information, processes it, and ultimately transmits output information. The network learns through the connections between neurons, weights, and their changes. During the learning process, the network understands how the data is related and adapts to new information. The main components of artificial neural networks are as follows: Input layers – Data is fed into the network at this layer. Hidden layers – Data is processed at this layer and the network continues to learn. Output layers – Results or decisions are made at this layer. Applications of Artificial Neural Networks in Society.Economic sphere. Artificial neural networks are widely used in the economy to increase production efficiency, optimize resource utilization, and automate economic processes. They are used in companies and industrial enterprises in the following areas: Automated production: Artificial neural networks can be used to control and optimize production processes. For example, in factories, automatic production systems are operating using quality control, material inventory, and robotic systems. Выпуск журнала №-26 Часть–6_ Май –2025 342 MODERN EDUCATION AND DEVELOPMENT Forecasting: Artificial neural networks can be used to predict future economic developments, market demand forecasts, and prices. This helps companies optimize resources and make more accurate investment decisions. In finance and credit: Banks and financial institutions use artificial neural networks to make decisions on loan applications, detect fraud, and analyze investments. These systems help make decisions quickly and accurately, and also reduce credit risks. Healthcare. Artificial neural networks are widely used in healthcare to optimize medical diagnostics and treatment processes. In medical image processing and disease detection, SNTs play the following roles: Medical image analysis: Neural networks work effectively in analyzing X-ray images, MRI images, or ultrasound images. They help in early detection of complex diseases, such as oncology, heart disease, and other serious diseases. Drug development: Neural networks are also used in genomic analysis and the creation of new drugs. They enhance scientific potential in medical research and the development of new therapeutic approaches. Personalized treatment: Neural networks help in developing treatment plans based on genomic data, which are tailored to the individual characteristics of the patient. This allows for effective treatment for patients. Education. In the field of education, artificial neural networks help in individualizing the learning process. By creating customized learning systems for students, taking into account their interests and learning speed, the following results can be achieved with the help of SNTs: Personalized curricula: Neural networks allow students to analyze their learning style and create customized curricula for them. Helping teachers: Artificial neural networks help teachers monitor student changes and development, as well as identify which topics students are struggling with. Online learning platforms: Neural networks are used in online learning platforms to improve students' interactive experi