International Journal of Engineering Mathematics (Online)

IMPLEMENTATION OF HEALTH INSURANCE MECHANISMS THROUGH THE APPLICATION OF RESULTS-ORIENTED BUDGETING IN UZBEKISTAN

2025-03-22T17:23:06+00:00

This article analyzes the implementation of health insurance mechanisms through results-oriented budgeting, the procedure for financing the social sector in Uzbekistan based on foreign experience, and the expected results.

PRINCIPLES OF OPERATION AND EFFICIENCY OF BAYESIAN NETWORKS IN LOGIC SCHEMES USED IN ARTIFICIAL INTELLIGENCE

2025-03-04T13:25:23+00:00

This article analyzes the logic schemes used in artificial intelligence systems and the principles of Bayesian networks. Bayesian networks, based on
probability theory, allow for decision-making in complex systems and analysis of data under uncertainty. The article discusses the effectiveness of networks, their advantages in the modeling process, and areas of application. Information is also provided about the specific aspects of Bayesian networks and methods for optimizing them.

KINEMATICS OF A SELF-ROTATTING CUTTER AS A FACTOR OF INCREASING TOOL LIFE AND PROCESS PRODUCTIVITY

2025-01-17T07:57:28+00:00

Information is presented on the kinematics of a self-rotating cutter as a factor in increasing tool life and process productivity. According to it, it is possible to increase the productivity of the cutting process with existing tool materials if a cutting process scheme is created that allows reducing the temperature, speed and pressure in the cutting zone without reducing the cutting modes. This type of cutting is most effectively performed in cutters with a circular cutting edge when this edge rotates around its geometric axis. The results of theoretical studies are presented on the fact that in circular self-rotating cutters, the rotation of the cutting part is carried out by the moment of cutting forces relative to the geometric axis of the circular cutting edge.

Hybrid Neural Network Architecture for Real-Time Groundwater Monitoring Using Multi-Sensor Systems

2025-03-22T17:17:31+00:00

Groundwater quality monitoring is a critical component of environmental sustainability and public health, necessitating advanced systems that can provide accurate and continuous assessments. Existing methods, while effective for periodic sampling, often fail to meet the demands for real-time, large-scale data analysis, leaving gaps in understanding dynamic environmental changes. This study aims to develop a hybrid intelligent system that integrates multi-sensor data collection and advanced neural network architectures. The system is designed to enhance the real-time monitoring of groundwater quality, detect anomalies, and provide actionable insights through spatial-temporal mapping. The proposed system employs a combination of Long Short-Term
Memory (LSTM) networks, Convolutional Neural Networks (CNNs), and a multi-head attention mechanism to analyze temporal and spatial data. Data preprocessing techniques such as outlier detection, interpolation, and normalization ensure robustness against noise and missing values. The system
supports three learning modes: static supervision (offline training), online learning (operator-in-the-loop), and active learning (querying uncertain examples). Experimental evaluations were conducted using datasets collected from multiple monitoring wells over a six-month period. The hybrid neural network architecture achieved significant improvements in performance compared to standalone LSTM and CNN models. Metrics such as RMSE (0.10), precision (88.5%), and recall (90.2%) underscore the system’s ability to deliver accurate and reliable groundwater quality predictions. Visual outputs, including water quality maps and anomaly detection reports, demonstrate the system's capacity to identify critical trends and regions of concern effectively. The study highlights the efficacy of integrating advanced machine learning techniques with multi-sensor systems for real-time groundwater monitoring. The hybrid system’s scalability, adaptability, and robustness position it as a promising tool for environmental management. Future work will focus on incorporating additional data sources, such as meteorological and geological information, and enhancing the system’s online learning capabilities to further improve performance in dynamic and diverse environments.

GLOBALIZATION AND ITS IMPACT ON THE NATIONAL ECONOMY

2025-02-27T06:47:43+00:00

Globalization is an integral part of the modern world economy, facilitating the integration of national economies into international markets. This article explores the impact of globalization on national economies, focusing on international trade, foreign direct investment (FDI), and technological advancements.