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All Journal International Journal of Electrical and Computer Engineering
Bapiyev, Ideyat
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Computer Science & IT Electrical & Electronics Engineering

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Predicting and detecting fires on multispectral images using machine learning methods Aitimov, Murat; Kaldarova, Mira; Kassymova, Akmaral; Makulov, Kaiyrbek; Muratkhan, Raikhan; Nurakynov, Serik; Sydyk, Nurmakhambet; Bapiyev, Ideyat
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 2: April 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i2.pp1842-1850

Abstract

In today's world, fire forecasting and early detection play a critical role in preventing disasters and minimizing damage to the environment and human settlements. The main goal of the study is the development and testing of machine learning algorithms for automated detection of the initial stages of fires based on the analysis of multispectral images. Within the framework of this study, the capabilities of three popular machine learning methods: extreme gradient boosting, logistic regression, and vanilla convolutional neural network (vanilla CNN), are considered in the task of processing and interpreting multispectral images to predict and detect fires. XGBoost, as a gradient-boosted decision tree algorithm, provides high processing speed and accuracy, logistic regression stands out for its simplicity and interpretability, while vanilla CNN uses the power of deep learning to analyze spatial and spectral data. The results of the study show that the integration of these methods into monitoring systems can significantly improve the efficiency of early fire detection, as well as help in predicting potential fires.
Development of a decision-making module in the field of real estate rental using machine learning methods Mukhanova, Ayagoz; Baitemirov, Madiyar; Ignatovich, Artyom; Bayegizova, Aigulim; Tanirbergenov, Adilbek; Tynykulova, Assemgul; Bapiyev, Ideyat; Mukhamedrakhimova, Galiya
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 5: October 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i5.pp5430-5442

Abstract

The research is aimed at developing a prototype of a decision support information system for managers of a company operating in the real estate rental industry. The system provides tools for data analysis, the use of mathematical models and expert knowledge to solve complex problems. The work analyzes the practical aspects of the design and use of decision support systems and formulates the requirements for the functionality of the system being developed. The Python programming language was used for implementation. The prototype includes machine learning models, expert systems, user interface and reports. Linear regression, data clustering density-based spatial clustering of applications with noise (DBSCAN) and backpropagation methods were implemented to train the classifying perceptron. The developed tool represents a significant contribution to the field of decision support, providing unique analysis and forecasting capabilities in the dynamic real estate rental environment. This prototype is an innovative solution that promotes effective management and strategic decision making in complex real estate business scenarios.
Co-Authors Aitimov, Murat Baitemirov, Madiyar Bayegizova, Aigulim Ignatovich, Artyom Kaldarova, Mira Kassymova, Akmaral Makulov, Kaiyrbek Mukhamedrakhimova, Galiya Mukhanova, Ayagoz Muratkhan, Raikhan Nurakynov, Serik Sydyk, Nurmakhambet Tanirbergenov, Adilbek Tynykulova, Assemgul