This article presents the proper organization of the supply chain to meet consumer demand, which is crucial for modern commercial enterprises involved in the sale of various products. Studies indicate that a company's success is linked to the satisfaction of its customers. To optimize the supply chain, this study will consider the use of artificial neural network models. The results of this model will seek a balance between demand and supply, helping determine the necessary quantity of goods to satisfy demand and prevent overproduction. By using this model, the company can fully meet the needs of its customers. Additionally, the company saves its resources and labor costs and reallocates them to other tasks. The model demonstrates the optimization of production and supply business processes, as well as an increase in efficiency.

This study proposes an interpretable hybrid deep learning framework for classifying attention deficit hyperactivity disorder (ADHD) using EEG signals recorded during cognitively demanding tasks. The core architecture integrates convolutional neural networks (CNNs), gated recurrent units (GRUs), and long short-term memory (LSTM) layers to jointly capture spatial and temporal dynamics. In addition to the final hybrid architecture, the CNN–GRU–LSTM model alone demonstrates excellent accuracy (99.63%) with minimal variance, making it a strong baseline for clinical applications. To evaluate the role of global attention mechanisms, transformer encoder models with two and three attention blocks, along with a spatiotemporal transformer employing 2D positional encoding, are benchmarked. A hybrid CNN–RNN–transformer model is introduced, combining convolutional, recurrent, and transformer-based modules into a unified architecture. To enhance interpretability, SHapley Additive exPlanations (SHAP) are employed to identify key EEG channels contributing to classification outcomes. Experimental evaluation using stratified five-fold cross-validation demonstrates that the proposed hybrid model achieves superior performance, with average accuracy exceeding 99.98%, F1-scores above 0.9999, and near-perfect AUC and Matthews correlation coefficients. In contrast, transformer-only models, despite high training accuracy, exhibit reduced generalization. SHAP-based analysis confirms the hybrid model’s clinical relevance. This work advances the development of transparent and reliable EEG-based tools for pediatric ADHD screening.

The article presents the development of a hybrid approach to quality control in additive manufacturing using the Fused Deposition Modeling (FDM) method, based on the integration of artificial intelligence techniques and statistical analysis. The study substantiates the need to shift from traditional quality control methods to intelligent monitoring systems capable of real-time defect detection and process analysis. The results of the analysis emphasize the effective application of techniques such as ANOVA, PCA, artificial intelligence algorithms, and image-based recognition systems in identifying irregularities and predicting potential issues in additive production. These technologies also play a vital role in fine-tuning critical FDM process parameters. The proposed integrated approach demonstrates considerable advantages in enhancing accuracy, process uniformity, and overall operational stability in 3D printing environments.

In the COVID-19 pandemic, the education system has faced unprecedented challenges requiring adaptation and implementation of new teaching methods and data analysis. This article discusses the use of ensemble machine learning models to predict student learning activity in distance education. The data on student engagement collected through the questionnaire is analyzed, and forecasting models are built based on these data. The study revealed that the combination of random forest (RF), k-nearest neighbor (KNN) algorithms and the gradient bootstrap method (XGBoost) demonstrates high accuracy and reliability of forecasts. The results of the study show that ensemble models can be effectively used to analyze educational data and make informed decisions in crisis situations. The research uses data from a survey conducted by the National Academy of Education named after I. Altynsarin in 2020 with the participation of 39,450 students from schools in the Republic of Kazakhstan.

The rapid growth of e-commerce has increased the demand for intelligent technologies capable of improving customer experience and optimizing online shopping processes. Among the most important technologies, conversational AI agents have become an importan

Opinion mining and sentiment analysis are aimed at extracting valuable necessary information from the raw text and determining the polarity of the text. Comparative opinion mining analyzes opinions that compare two or more entities based on a common feature. The purpose of comparative opinions mining is to extract comparative opinions from the text and identify the objects being compared. First step of identification comparative opinions is extraction comparative constructions. There are presently research papers on sentiment analysis and opinion mining in the Kazakh language. But we were unable to find any work on the comparative opinion mining. The aim of the study is to automate the extraction of comparative constructions in the Kazakh language. In this paper, a pattern based approach was implemented to extract comparative constructions in the Kazakh language.