The article presents comparison of two statistical methods of data prediction over transport datasets. Autoregressive integrated moving average and its seasonal modification—seasonal autoregressive integrated moving average—are often applied in timeseries data. In current article their effectiveness is assessed using transport data. The data are acquired from data surveillance traffic system of Technical University of Sofia, branch Plovdiv. The conducted experiment encompasses STL transformation, ADF and KPSS stationarity tests, analysis of ACF and PACF, and comparison of different ARIMA and SARIMA configurations. Comparative analysis of MAE, MAPE, and RMSE confirms that ARIMA outperforms SARIMA in current datasets.

This article describes the process of designing and developing an Android application to support students in a university environment by automating study schedule management, facilitating access to study materials, providing navigation to educational buildings, and sending notifications about upcoming classes. The main problem that current development addresses is the lack of a centralized system for timely notifications and difficulties in navigating university campuses and obtaining summaries of study material files. Using two multilingual machine learning (ML) models, the solution integrates an automated notification system using Firebase Cloud Messaging (FCM), which operates in real time and provides geospatial navigation to educational buildings. Two ML models for natural language processing are used to automatically generate short and meaningful text summaries, and they accept long articles or documents in different languages and create abstract summaries, which makes them suitable for easy absorption of academic/educational materials. The technology stack includes the Django REST Framework 3.10 for the server part, PostgreSQL 18 for database management, and Java SE 21 for the mobile application, with security guaranteed through JWT (JSON Web Token) authentication and TLS encryption 1.2. The result is a comprehensive application that provides students with personalized access to weekly schedules, information about classes and assigned classroom numbers, and access to learning materials that are trained with a model optimized to create short, informative summaries. This contributes to better organization, reducing absences and increasing the efficiency of the educational process.