Workflow automation and performance improvement based on PostgreSQL

The development of an Automated Information System for Electronic Document Management and Communication (AIS EDiSZ) is aimed at optimizing the logging processes of enterprises. As part of this development, functionality is implemented that allows you to effectively manage documents, improve employee navigation and reduce equipment downtime. This functionality means various databases, an electronic map and various electronic charts. In addition, the creation of such a system lays the foundation for the future digitalization of the industry and opens up new opportunities for innovation. The result of the development stage will be a ready-made AIS ED&SZ project, which will be aimed at solving current problems of enterprises and increasing their efficiency.

Development of an automated information system for a logging company

Stepanova,

Kukartsev,

Kravtsov

et al. 2024

Application of statistical data analysis algorithms and determination of the most significant diagnostic factors

Rozhkova,

Kukartsev,

Kvesko

et al. 2024

The article examines the application of statistical data analysis algorithms in diagnostics and the identification of significant factors influencing observed phenomena. The use of statistical methods, such as multiple regression, logistic regression, and neural networks, is discussed. The study focuses on determining the most impactful factors, establishing relationships between variables, and evaluating the effectiveness of technologies and methods. The dataset, provided by Universidad Cardenal Herrera, CEU, Madrid, Spain, includes factors related to patients’ demographics, health conditions, and lifestyle. The analysis involves deductive analysis, correlation analysis, and Kohonen maps to identify relevant factors. Decision tree analysis is conducted using different combinations of factors, including average glucose levels and body mass index. The results show varying error rates for different methods and factor combinations. Overall, statistical data analysis demonstrates its potential for faster and more accurate identification of significant diagnostic factors.

Improving the efficiency of water use in agriculture by modelling the classification of groundwater quality

Rozhkova,

Kukartsev,

Kvesko

et al. 2024

This study investigates the potential of machine learning for classifying groundwater quality in Telangana, India, to optimize water resource utilization in agriculture. The study aims to develop and evaluate a decision tree model capable of accurately predicting groundwater quality based on chemical composition data. The objective is to identify key factors influencing water quality and provide insights for improving water management practices and enhancing agricultural productivity. The study utilizes a dataset of groundwater quality parameters collected over three years (2018-2020) and employs a decision tree algorithm for model development. The results demonstrate the effectiveness of the model, achieving an accuracy of 95.7%. The analysis highlights the significance of sodium content, dissolved salts ratio, total dissolved solids, and total water hardness as key factors influencing groundwater quality. This research underscores the potential of machine learning for enhancing water resource management in agriculture and suggests further exploration of temporal dynamics, predictive modeling, and broader geographic application to further refine and extend the model’s impact.