hybrid models

Development of Hybrid ARIMA-LSTM and Prophet-LSTM Models for Air Quality Forecasting in the Tangier Region

Accurately predicting tropospheric ozone (O$_3$) levels is essential for improving air quality in Tangier, given its significance as a major atmospheric pollutant.  In this study, we propose a hybrid forecasting approach that combines ARIMA-LSTM and Prophet-LSTM models to improve both the accuracy and robustness of ozone concentration predictions.  The method leverages the strengths of ARIMA and Prophet in capturing linear trends and seasonal variations, while leveraging the ability of LSTM networks to model nonlinear dynamics and long-term dependencies.  Comparative an

INFORMATION TECHNOLOGY FOR IDENTIFYING PROPAGANDA IN TIKTOK COMMENTS BASED ON NLP AND DEEP LEARNING

The article investigates the current scientific problem of automated detection of propaganda influence in short text comments of users of the TikTok social network, which operates in conditions of hybrid warfare and intensive disinformation campaigns. A hybrid model for detecting propaganda content has been developed, which integrates deep contextual representations of the text (transformer-based contextual representations) based on the BERT architecture with an additional vector of semiotic and structural features (number of emojis, repetition of symbols, use of caps lock).

Forecasting solar energy generation using deep learning models

The application of deep learning models for forecasting solar energy generation is considered.  An analysis and comparison of the efficiency of recurrent (LSTM, GRU), convolutional (CNN), and temporal convolutional networks (TCN) for forecasting time series of solar energy generation were conducted.  The possibility of improving forecasting accuracy by constructing a hybrid model combining ARIMA and CNN was explored.  The results of experiments for different EU countries are presented, and a comparison of models in terms of forecasting accuracy and computational efficiency is performed as w