INNOVATIVE PROJECTS IN COLLABORATION WITH FACULTY, MASTER'S STUDENTS, STUDENTS AND PARTNERS

Project Title:

«UPTRACK» – AI trainer based on computer vision for individual workouts

Project goal:

The purpose of the project is to create an intelligent trainer that uses computer vision to automatically monitor the correctness of exercise performance and provide personalized real-time feedback to the user.

Problem and solution:

Problem: More than 60% of fitness equipment users (especially at home) train without supervision from specialists. This increases the risk of injuries, reduces motivation, and lowers training efficiency.
Solution: The lack of personal supervision during independent training often leads to reduced effectiveness and injuries. Existing market solutions are not integrated into the equipment itself and require additional devices or subscriptions.

Expected results:

  • Developed and implemented AI solution integrated into the fitness equipment line of Iron BARS LLP.
  • Prototype and serial model of a trainer with intelligent motion analysis function.
  • Improved safety and efficiency of workouts for end users.
  • Increased competitiveness of Iron BARS LLP products through digitalization and AI implementation.
  • Establishment of mass production of intelligent trainers and their market launch.
  • Potential for scaling the project to new types of equipment and areas (rehabilitation, sports, fitness for children and elderly, etc.).

Participants / Partner:

Professors, Master’s students, «Iron BARS» LLP

Project Title:

System of containers for separate collection of solid household waste

Project goal:

Formation of a modern model for separate waste collection and recycling, development of the green economy at the university

Problem and solution:

Problem: Lack of a separate waste collection system, low recycling of secondary raw materials, and high environmental burden.
Solution: Installation of containers, organization of sorting, holding eco-events, and cooperation with recycling companies.

Expected results:

Reduction of waste sent to landfills, increase in recycling by 20–30%, demonstration base for training, improvement of environmental culture

Participants / Partner:

ТОО «Чистый след»

Project Title:

Automated robot – laser engraving machine

Project goal:

Enhancing the efficiency and productivity of small businesses in the field of decor and textiles

Problem and solution:

Problem: Manual cutting of ornaments is time-consuming, has high cost, and limited scalability.
Solution: Implementation of an automated laser machine with high precision and speed, integrated with software for design customization.

Expected results:

  • Productivity increases 4–5 times
  • Cost price is reduced by 30–40
  • The product range expands
  • Opportunities to enter export markets appear
  • Local entrepreneurs receive support.

Participants / Partner:

Professors, Master’s students, Students

INNOVATIVE EDUCATIONAL CASES

Case name:

BilimALL AI – Integration of artificial intelligence technologies into natural sciences

Description of the problem / task:

In the modern education system, interdisciplinary connections play an important role (STEM learning). On the web portal, the system automatically determines interdisciplinary links (physics, biology, chemistry, geography, mathematics). Based on the identified interdisciplinary connections, the portal automatically generates slides. Upon request, the AI module provides content prepared in a logical scientific style. The platform includes official textbooks approved by the Minister of Science and Higher Education of the Republic of Kazakhstan

Expected result:

  • Quick access to learning materials saves time
  • Reduced time for obtaining scientifically grounded content
  • Digital transformation of educational material preparation
  • Reduction of methodological workload for teachers and editors by up to 40%
  • Reduction of time and labor costs for slide preparation by 50–70%

Case name:

DSR (Document Smart Route) – Implementation of machine learning in the electronic document management system

Description of the problem / task:

Modern organizations face the need to process and store large volumes of documents in digital format. Manual processing leads to time losses, errors, and delays in decision-making. The goal of the project is to develop an intelligent system that automates document classification and routing using machine learning methods, which will significantly accelerate processes within the organization.

Expected result:

  • Reduction of document processing time by up to 50%
  • Improved classification accuracy through adaptive learning
  • Optimization of document flow and data-driven management
  • Reduction of errors in document routing
  • Increased speed of managerial decision-making

Case name:

Intelligent Network Anomaly Detection System (iNADS)

Description of the problem / task:

Modern network security methods, primarily based on signature analysis, do not ensure timely detection of new or modified cyberattacks. This poses a threat to the information security of critical infrastructure and corporate networks. The iNADS system is designed to improve the accuracy and speed of network anomaly detection by combining the capabilities of artificial intelligence with IDS/IPS systems.

Expected result:

  • Increasing the level of network cybersecurity without additional costs for third-party solution licenses;
  • Reducing the number of incidents not detected by traditional IDS;
  • Reducing incident response time through automatic notifications.

Case name:

MapOil – visualization and analysis of oil data

Description of the problem / task:

A tool for analytics and reporting that provides up-to-date information on oil logistics. The information system enables tracking of oil flows during transportation, analysis of transportation routes, volume control, and recording of losses. For oil companies, loss accounting is important both economically and environmentally, as it helps minimize losses, ensure accuracy of balance calculations, and comply with regulatory requirements.

Expected result:

  • Reducing the time for collecting and analyzing logistics data by up to 40% 
  • decreasing technological losses through timely monitoring
  • increasing transparency and accuracy of reporting, and reducing incident response time by 20–30%.