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All Journal International Journal of Electrical and Computer Engineering Journal of Applied Data Sciences
Sakypbekova, Meruyert
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering

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Development of a digital twin of a network of heating systems for smart cities on the example of the city of Almaty Tyulepberdinova, Gulnur; Kunelbayev, Murat; Shiryayeva, Olga; Sakypbekova, Meruyert; Sarsenbayev, Nurlan; Bayandina, Gulmira
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 6: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i6.pp6656-6674

Abstract

In this paper, a digital twin of the network of heating systems for smart cities is developed using the example of the city of Almaty. The study used machine learning algorithms to estimate future thermal energy consumption and develop thermodynamic formulas. This work offers a thorough and in-depth analysis of thermal energy consumption. In addition, the paper identifies the relationship between thermal energy consumption and ambient temperature, and wind uncertainty in certain urban areas using machine learning methods to predict thermal energy consumption. Using both training and regression models, this interdependence is revealed. The obtained forecasts provide useful information for studying the structure of heat consumption in Almaty and reducing heat losses by reducing overheating in the zones of heating networks. In addition, the study analyzes high-resolution spatial data collected from 385 homes and 62 heat transfer circuits located throughout the city during the heating season. The study examines the degree of relationship between the ambient temperature and the amount of heat energy used in the areas of Astana. A minor impact of wind speed is also estimated. These discoveries allow us to use machine learning algorithms to find the location of hot spots and inefficient zones with high losses.
Development of a Smart Lung Health Monitoring System Using Sensors and Data Analytics for Early Disease Detection Tyulepberdinova, Gulnur; Kunelbayev, Murat; Amirkhanova, Gulshat; Sakypbekova, Meruyert; Adilzhanova, Saltanat; Tolepberdinova, Ardak
Journal of Applied Data Sciences Vol 6, No 4: December 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i4.857

Abstract

This study introduces a novel multimodal wearable sensor system for real-time monitoring and analysis of respiratory and cardiac activity. The primary objective is to facilitate the early detection of cardiopulmonary abnormalities by integrating electrical (ECG) and acoustic data. A total of 30 participants, aged 25 to 50 years, were involved in controlled breathing experiments, which included deep (1000 ml, 15 breaths/min), moderate (750 ml, 20 breaths/min), and shallow (500 ml, 30 breaths/min) breathing, as well as coughing simulations. Signal processing using a 7th-order polynomial approximation yielded the lowest modeling error at 6.8%, ensuring precise waveform reconstruction. The system demonstrated a clear differentiation of respiratory patterns via Area Under the Curve (AUC) metrics, with average AUC values increasing from 1200 µV·s during shallow breathing to 3200 µV·s during deep breathing. Further analysis of the first derivative of AUC values revealed a strong correlation (r = 0.89) between respiratory volume and ECG amplitude fluctuations, highlighting robust cardiorespiratory coupling. Notably, the system achieved a 92% accuracy in detecting abnormal breathing events, such as shallow breathing and coughing fits. By combining ECG-derived heart rate variability with respiratory data, the system offers a comprehensive assessment of cardiopulmonary interaction. The key contribution of this work lies in its real-time, continuous monitoring capability using a compact wearable form factor, which distinguishes it from existing single-modality systems. This approach represents a significant advancement in non-invasive health monitoring, with strong potential for application in clinical diagnostics and home-based tracking of chronic conditions, such as asthma, COPD, and cardiac dysregulation.
Co-Authors Adilzhanova, Saltanat Amirkhanova, Gulshat Bayandina, Gulmira Kunelbayev, Murat Sarsenbayev, Nurlan Shiryayeva, Olga Tolepberdinova, Ardak Tyulepberdinova, Gulnur