Article Per Year (5 Year)
p-Index From 2021 - 2026
0.444
P-Index
This Author published in this journals
All Journal Indonesian Journal of Electrical Engineering and Computer Science Journal of Applied Data Sciences
Mansurova, Madina
Unknown Affiliation
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
Investigation of linear models for control of water flow and temperature in a water supply system Asset, Askhat; Mansurova, Madina; Zhmud, Vadim; Kopesbaeva, Aksholpan; Dzheksenbaev, Nurbolat
Indonesian Journal of Electrical Engineering and Computer Science Vol 35, No 1: July 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v35.i1.pp113-123

Abstract

In some cases, the object model is a set of parallel models of the same general appearance, but with different parameters. The most common model is a model in the form of a serial connection of a first- or second-order filter and a delay link. An example is the water supply system of a large residential building or a group of houses. From the most general considerations, we can expect that such an object can be approximately described by a simpler model, replacing the sum of identical-looking models with different parameters with a single model of this type with averaged parameters, however, finding many parameters simply in the form of an average is, apparently, an unreasonable approach. It seems more reasonable to find the parameters by the approximating model by numerical optimization, in which the integral from the module or from the square of the deviation of the output signal of such a model from the output signal of the exact model is minimized when the test signal is applied. For linear models, the most reasonable test signal is a single step effect. This article tests this hypothesis and provides the results of this test.
Analyzing the Efficacy of Pose Recognition, YOLOv3, and Deep Learning Techniques for Human Activity Recognition Zhumasheva, Ainur; Mansurova, Madina; Amirkhanova, Gulshat; Tyulepberdinova, Gulnur
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.v6i4.797

Abstract

The global increase in life expectancy, driven by increased nutrition, healthcare, and living conditions, has resulted in a significant growth in the senior population, notably in Kazakhstan, where the number of people aged 60 and more currently exceeds 2.7 million. This demographic transition poses considerable public health problems, particularly the high prevalence and severity of falls in older persons. Falls are currently the second largest cause of unintentional mortality for more than 87% of the elderly, with 28-34% falling at least once per year. As the worldwide population of people aged 65 and more is predicted to exceed 1.5 billion by 2050, there is an urgent need for precise, real-time fall detection systems. This work uses standardized datasets to conduct a complete evaluation of three fall detection methodologies: posture recognition, YOLOv3-based detection, and deep learning. Deep learning models attained the best accuracy of 92.0% by utilizing their capacity to learn complex spatial-temporal information, but at the cost of increased computing burden and slower inference times (40 ms). YOLOv3 provided competitive accuracy (90.2%) and quicker processing (25 ms), making it suitable for real-time deployment, although with a larger false positive rate. Pose identification, while highly interpretable due to its emphasis on skeletal key points, performed less well in crowded or obscured settings. The findings highlight the possibility for combining the capabilities of each technique to create hybrid systems with adaptive, resource-efficient architectures. Future research should focus on sensor fusion and optimization methodologies to improve accuracy and scalability across a variety of scenarios.
Co-Authors Amirkhanova, Gulshat Asset, Askhat Dzheksenbaev, Nurbolat Kopesbaeva, Aksholpan Tyulepberdinova, Gulnur Zhmud, Vadim Zhumasheva, Ainur