kaz29Вестник Казахстанско-Британского технического университетаHerald of the Kazakh-British Technical University1998-66882959-8109Казахстанско-Британский Технический Университет10.55452/1998-6688-2026-23-2-340-352kaz29-2917Research ArticleКОМПЬЮТЕРНЫЕ НАУКИCOMPUTER SCIENCEИСКУССТВЕННЫЙ ИНТЕЛЛЕКТ: НОВЫЙ БИОИНСПЕРИРОВАННЫЙ ПОДХОД В ИНЖЕНЕРНОМ ОБРАЗОВАНИИARTIFICIAL INTELLIGENCE: A NEW BIO-INSPIRED APPROACH IN ENGINEERING EDUCATIONhttps://orcid.org/0000-0003-1798-9161СамигулинаГ. А.SamigulinaG. A.

д.т.н., профессор.

Алматы

Dr.Tech.Sc., professor.

Almaty

kz.galinasamigulina@gmail.comhttps://orcid.org/0000-0002-5862-6415СамигулинаЗ. Н.SamigulinaZ. I.

PhD, доцент.

Алматы

PhD, Associate Professor.

Almaty

z.samigulina@kbtu.kzhttps://orcid.org/0009-0002-8168-6368БекешевД. Д.BekeshevD. D.

Магистр техн. наук.

Алматы

MSc degree.

Almaty

dauletb01@gmail.comhttps://orcid.org/0009-0002-7864-1900КучербаеваС.KucherbaevaS. Z.

Алматы

Almaty

stestephaniya1960@mail.ruhttps://orcid.org/0009-0001-5421-0666ЯрмухамедоваА. Б.YarmukhamedovaA. B.

Алматы

Almaty

only_alla@mail.ruИнститут информационных и вычислительных технологий, лаборатория «Интеллектуальные системы управления и прогнозирования»; Казахстанско-Британский технический университет, Школа информационных технологий и инженерииКазахстанInstitute of Information and Computing Technologies, «Intelligent Control and Forecasting Systems» Lab; Kazakh-British Technical University, School of Information Technology and EngineeringKazakhstanКГУ «Общеобразовательная школа №35» Управления образования г. АлматыКазахстанMunicipal State Institution «Comprehensive School No. 35» of the Almaty City Education DepartmentKazakhstan202627062026232340352Copyright © Самигулина Г.А., Самигулина З.Н., Бекешев Д.Д., Кучербаева С.Z., Ярмухамедова А.Б., 20262026Самигулина Г.А., Самигулина З.Н., Бекешев Д.Д., Кучербаева С., Ярмухамедова А.Б.Samigulina G.A., Samigulina Z.I., Bekeshev D.D., Kucherbaeva S.Z., Yarmukhamedova A.B.Данная работа распространяется под лицензией Creative Commons Attribution 4.0.This work is licensed under a Creative Commons Attribution 4.0 License.https://vestnik.kbtu.edu.kz/jour/article/view/2917

Разработана когнитивная технология дистанционного обучения техническим специальностям с использованием биоинспирированного подхода на основе унифицированной искусственной иммунной системы с нейроэндокринным регулированием гомеостаза при получении знаний. Для реализации интеллектуальной системы дистанционного образования по управлению сложными объектами промышленной автоматизации на основе предложенной технологии УИИС – НЭС разработана интегрированная онтологическая модель, состоящая из онтологических моделей иммунной, нейронной и эндокринной подсистем на основе которой создана база знаний для внедрения когнитивной технологии в учебный процесс. Разработаны классы, подклассы и рассмотрены их взаимодействия для двухэтапной обработки данных в виде решения задачи редукции неинформативных признаков и классификации на основе биоинспирированных алгоритмов. Созданы критерии оценки качества системы на основе метрик с разделением на взаимоисключающие классы на основе хороших и плохих результатов. По результатам анализа данных на основе технологии осуществляется прогноз уровня полученных профессиональных инженерных навыков, вероятности успешного освоения дисциплины и определения степени готовности обучающегося к решению сложных технических задач управления в виде цифрового профиля компетенций студента.

a bioinspired approach based on a unified artificial immune system featuring neuroendocrine regulation of homeostasis during knowledge acquisition. To implement an intelligent distance learning system for the control of complex industrial automation objects – based on the proposed UAIS–NES technology – an integrated ontological model has been developed. This model consists of ontological sub-models for the immune, neural, and endocrine subsystems, serving as the foundation for a knowledge base designed to integrate cognitive technology into the educational process. Classes and subclasses have been defined, and their interactions analyzed, to facilitate a two-stage data processing workflow: specifically, the reduction of non-informative features and subsequent classification, both executed using bio-inspired algorithms. System quality assessment criteria have been established based on metrics categorized into mutually exclusive classes, distinguishing between favorable and unfavorable outcomes. Based on the results of data analysis utilizing this technology, a forecast is generated regarding the level of acquired professional engineering skills, the probability of successfully mastering the academic discipline, and the student’s degree of readiness to tackle complex technical management challenges – all presented in the form of a digital student competency profile.

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The authors declare that there are no conflicts of interest present.