Mathematical Models for the Design of GRID Systems to Solve Resource-Intensive Problems

VV Tynchenko, VS Tynchenko, VA Nelyub… - Mathematics, 2024‏ - mdpi.com
Artificial neural networks are successfully used to solve a wide variety of scientific and
technical problems. The purpose of the study is to increase the efficiency of distributed …

Using machine learning techniques to simulate network intrusion detection

V Kukartsev, K Kravtsov, O Stefanenko… - 2024 International …, 2024‏ - ieeexplore.ieee.org
This article explores the application of machine learning methods for modeling intrusion
detection systems. We present the research findings, including the construction of a model …

Machine Learning for Predicting and Optimizing Physicochemical Properties of Deep Eutectic Solvents: Review and Perspectives

FJ López-Flores, C Ramírez-Márquez… - Industrial & …, 2024‏ - ACS Publications
This review explores the application of machine learning in predicting and optimizing the
key physicochemical properties of deep eutectic solvents, including CO2 solubility, density …

Deep learning model for precise prediction and design of low-melting point phthalonitrile monomers

R Lu, Y Han, J Hu, D Xu, Z Zhong, H Zhou… - Chemical Engineering …, 2024‏ - Elsevier
Phthalonitrile is a widely applied resin due to its outstanding high-temperature resistance
and versatility. However, the high melting point of its monomer limits its range of …

Predicting Diffusion Coefficients in Nafion Membranes during the Soaking Process Using a Machine Learning Approach

I Malashin, D Daibagya, V Tynchenko, A Gantimurov… - Polymers, 2024‏ - mdpi.com
Nafion, a versatile polymer used in electrochemistry and membrane technologies, exhibits
complex behaviors in saline environments. This study explores Nafion membrane's IR …

[HTML][HTML] Prediction of Dielectric Constant in Series of Polymers by Quantitative Structure-Property Relationship (QSPR)

E Ascencio-Medina, S He, A Daghighi, K Iduoku… - …, 2024‏ - pmc.ncbi.nlm.nih.gov
This work is devoted to the investigation of dielectric permittivity which is influenced by
electronic, ionic, and dipolar polarization mechanisms, contributing to the material's capacity …

ML-based Forecasting of Temporal Dynamics in Luminescence Spectra of Ag2S Colloidal Quantum Dots

IP Malashin, DS Daibagya, VS Tynchenko… - IEEE …, 2024‏ - ieeexplore.ieee.org
The study delves into the temporal dynamics of luminescence in colloidal quantum dots,
utilizing time series forecasting techniques. Through an analysis of intensity measurements …

Predictive modelling of post-monsoon groundwater quality in Telangana using machine learning techniques

J Olentsova, V Kukartsev, V Orlov… - BIO Web of …, 2024‏ - bio-conferences.org
Groundwater quality is vital for public health, agriculture, and industry, especially in regions
like Telangana, India. This study analyses and predicts post-monsoon 2020 groundwater …

Designing a system of step-by-step quality control

R Alena, K Vladislav, G Anna, P Ivan… - … Conference on Smart …, 2024‏ - ieeexplore.ieee.org
In this article we will consider the main aspects of designing a system of step-by-step quality
control of the husking machine in the shop of veneer production and its impact on improving …

Machine learning approaches for water potability prediction: Addressing class imbalance with SMOTE

E Stepanova, V Orlov, V Kukartsev… - BIO Web of …, 2024‏ - bio-conferences.org
Ensuring access to safe drinking water is a fundamental public health priority. Traditional
methods for assessing water quality are laborintensive and require specialized equipment …