This paper considers the process of developing a method to recognize the causes of plant growth deviations from normal using the advancements in artificial intelligence. The medicinal plant Aloe arborescens L. was chosen as the object of this research given that this plant had been for decades one of the best-selling new products in the world. Aloe arborescens L. is famous for its medicinal properties used in medicine, cosmetology, and even the food industry. Diagnosing the abnormalities in the plant development in a timely and accurate manner plays an important role in preventing the loss of crop production yields. The current study has built a method for recognizing the causes of abnormalities in the development of Aloe arborescens L. caused by a lack of watering or lighting, based on the use of transfer training of the VGG-16 convolutional neural network (United Kingdom). A given architecture is aimed at recognizing objects in images, which is the main reason for using it to achieve the goal set. The analysis of the quality metrics of the proposed image classification process by specified classes has revealed high recognition reliability (for a normally developing plant, 91 %; for a plant without proper watering, 89 %; and for a plant without proper lighting, 83 %). The analysis of the validity of test sample recognition has demonstrated a similar validity of the plant's classification to one of three classes: 92.6 %; 87.5 %; and 85.5 %, respectively. The results reported here make it possible to supplement the automated systems that control the mode parameters of hydroponic installations by the world's major producers with the main feedback on the deviation of the plant's development from the specified values

The article presents the development of an intelligent system capable of automatically generating web content based on a user's text query using a language model built on the GPT architecture. It discusses the objectives, methodology, and stages of designing and implementing a web application that synthesises the structure and visual design of websites based on a description given in natural language. A key component of the system is the integration of a neural network model via the OpenAI API. The architecture of the software solution, the principles of interaction between its modules, and the sequence of stages for generating and subsequently verifying HTML/CSS code are described. A comprehensive assessment of the functionality, performance, and practical applicability of the system has been carried out. The results of the experimental study demonstrate the high reliability, scalability, and potential for application of the developed tool in educational, professional, and commercial spheres.

The goal of this research is to create GridSense, a real-time digital platform that uses stream data processing and machine learning to forecast electricity consumption in Petropavlovsk city. The study tackles the issue of manual load planning in the North Kazakhstan regional power system, where dispatch services face considerable unpredictability due to significantly continental climate conditions, wind influence, heating demand, and noticeable daily consumption peaks. The primary goal was to develop an economically accessible, low-latency, and interpretable decision-support platform that could generate short-term forecasts up to 48 hours in advance. The work's methodological foundation consists of the creation of an event-driven software architecture based on Apache Kafka and system-structural modeling using Data Flow Diagram (DFD) and Integration Definition for Function Modeling (IDEF0). A Linear Regression model trained on climatic and temporal parameters, such as temperature, wind speed, humidity, present load, forecast horizon, and goal hour, is used by the forecasting module. A browser-based single page application, a Node.js and Express.js backend, a Kafka message broker, a Python machine learning consumer, a PostgreSQL database, and WebSocket real-time delivery are all integrated into the implemented platform. The model attained an operational forecasting accuracy of 88.8% with R² = 0.77 and Mean Absolute Error of 23.7 MW, according to experimental evaluation. With a mean response time of 8.9 ms, p95 latency of 12.1 ms, p99 latency of 18 ms, and no detected errors, load testing with Artillery showed robust system operation. The suggested approach serves as a foundation for intelligent energy planning systems in Kazakhstan and offers useful assistance for regional dispatch management.

This study examines multi-context educational data acquired from independent digital platforms and characterized by differences in the composition of entities, types of interactions, and data representation schemes. The task addressed relates to the lack of a unified model for the invariant representation of heterogeneously structured data, the assessment of their structural compatibility, the determination of the costs of mutual adaptation, and the possibility of comparative analysis of digital platforms while preserving the characteristics of the educational context. A parametrically typed invariant hypergraph model has been proposed, which represents educational data as typed hypergraphs, their semantic normalization into a single type space, as well as the calculation of indicators of structural invariance, adaptation, and integral utility. A parametric analysis of sensitivity to changes in the data structure and model weighting coefficients has been suggested. The novelty of this work is underscored by the following: – a unified parametric hypergraph model has been constructed that combines the invariant representation of educational data, a quantitative assessment of the structural compatibility, adaptation costs, and integral utility of digital educational platforms; – a parametric analysis has been proposed to examine the influence of structure and weighting factors on the integration and comparative analysis of platforms; – the model was experimentally tested on data from independent educational platforms, including over 3,100 educational records and 4,861 hyperedges, revealing differences in the structural compatibility of the studied contexts. The model supports decision making in the selection and design of digital educational ecosystems in the context of structurally heterogeneous data

The rapidity of the process in modern manufacturing implies a high speed of continuous monitoring, which often surpasses the potential of manual control. The study presents the adaptive, energy-efficient intelligent platform for monitoring defects with the use of Edge Artificial Intelligence (AI) architecture. In terms of objectives, special attention is paid to solving latent computational problems in real-time processing on the local edge device at "Raduga" LLP. At the beginning, the study performs a comparison of One-to-Many and One-to-One detection architectures. The findings show that the Non-Maximum Suppression (NMS) step decreases the speed of local graphics accelerators; accordingly, the system under development uses NMS-free neural network detectors. In order to standardize the process of defining the data flows and algorithms, the following standards for system-structural modeling are used: IDEF0, IDEF3 and DFD. Besides, the system includes a lightweight You Only Look Once (YOLO) 26 Nano detector and ByteTrack object tracking algorithm. Thanks to this software module, it becomes possible to overcome computational difficulties in post-processing of frames and to perform stable object tracking on a moving conveyor. Finally, operational tests show that the system performs with stable real-time speeds on the NVIDIA GeForce RTX 2050 GPU with FPS above 30 for the conveyor speed of 1.5 m/s..