Scientific Journal Portal (Institute of Bioresources) — NDU
About Project
Electronic journal management system engineered for the Bioresources Institute.
Manages the end-to-end editorial workflow from manuscript submission to peer review and digital publishing. Complies with international OJS standards and scientific indexing criteria, featuring automated daily cloud backups for high data availability.
Key Features:
Other Projects
Distant Learning Management System (LMS) — NDU
Design, development, and deployment of a unified Distance Education Management System (LMS) for Nakhchivan State University faculty and students. Fully integrated with the Student Information Management System (SIMS / TMİS) via Single Sign-On (SSO). The platform handles live online lectures via LiveKit, course material distribution, attendance tracking, video archiving, and academic management. Officially protected by registered State Patent and Copyright Certificates. Key Features: • Full integration with SIMS (TMİS); • Single Sign-On (SSO) authentication; • Dedicated student and lecturer control panels; • LiveKit-driven live online class orchestration & management; • Course material library and video archiving; • Secure data exchange & high-throughput database architecture.
Webinar Portal — NDU
Architected and developed the University Webinar System to manage online academic conferences, lectures, and departmental webinars across all university faculties. Provides real-time video broadcasting, session registration, live moderation, and automated video archiving. Key Features: • Live webinar broadcasting & streaming; • Webinar registration & video archiving; • Faculty and department level administration; • Granular user role & permission management; • Centralized video storage library.
Comparative Analysis of Computational Efficiency of Machine Learning Models and Interpretation of Sensor Importance in IoT-Based Smart Library Environments
Scientific research paper published in Scientific Works of Nakhchivan State University (2026, Vol 2(135), pp. 206-217). Evaluates sensor feature importance and computational efficiency across 7 machine learning models (KNN, Random Forest, Decision Tree, SVM, XGBoost, Naive Bayes). Key Highlights: • Comparative evaluation of 7 machine learning algorithms; • Assessment of IoT sensor features on computational complexity; • Environmental parameter optimization models.