DEVELOPMENT OF AN ALGORITHM FOR DETECTING ANOMALIES IN THE AIR POLLUTION MONITORING SYSTEM
Abstract. In recent years, the problem of air pollution has become more and more acute, especially for industrial regions. The constant growth of environmental monitoring data requires not only their accumulation, but also effective intelligent processing. One of the key tasks is the timely detection of abnormal values that can indicate both real emissions of […]
MACHINE LEARNING FOR CLASSIFYING KAZAKHSTAN’S TERRITORIES BASED ON DEMOGRAPHIC DATA
Abstract. The article is devoted to the study of the application of machine learning algorithms for the classification of regions of Kazakhstan using demographic data for 2024. The study considers the Decision Tree, Random Forest and k-Nearest Neighbors (KNN) algorithms. They demonstrate high efficiency in solving this problem. Data preprocessing included the calculation of the […]
HARDWARE IMPLEMENTATION AND ANALYSIS OF MULTITHRESHOLD DECODING ALGORITHMS IN RADIO COMMUNICATION SYSTEMS
Abstract. Reliable data transmission in radio communication systems largely depends on the efficiency of error correction algorithms. For low-power and real-time radio systems, the use of decoding methods with reduced computational complexity and optimized hardware resource utilization is of particular importance. In this context, multi-threshold decoding algorithms represent a promising alternative to conventional iterative decoding […]
ANALYSING DEMAND FORECASTING MODELS BASED ON HISTORICAL DRUG CONSUMPTION DATA
Abstract. Forecasting drug demand plays a key role in ensuring sustainable supply, effective inventory management, and timely patient access to life-saving medicines. This article presents a study of ARIMA time series and exponential smoothing methods for predicting demand for an antihypertensive blood pressure drug. The research is aimed at developing and identifying a model that […]