Despite global efforts to address climate change, carbon dioxide (CO₂) emissions are still on the rise. While carbon dioxide is essential for life on Earth, its increasing concentration due to human activities poses severe environmental and health risks. Therefore, accurately and efficiently predicting CO₂ emissions is essential. Hence, this research delves deeply into the prediction of CO₂ emissions by examining various deep learning models utilizing time series data to identify carbon dioxide levels in Oman. First, four important production materials of Oman (oil, gas, cement, and flaring), which have a great impact on CO₂ emissions, were selected. Then, the time series related to the release of CO₂ was collected from 1964 to 2022. After data collection, preprocessing was performed, in which outliers were removed and corrected, and data that had not been measured were completed using interpolation. Then, by dividing the data into two sections, education (1946–2004) and test (2022–2005) and creating scenarios, predictions were made. By creating four scenarios and modeling with two independent GRU and LSTM models and a hybrid LSTM-GRU model, annual carbon was predicted for Oman. The results were evaluated with three criteria: root mean square error (RMSE), mean absolute percentage error (MAPE), and correlation coefficient (r). The evaluations showed that the hybrid LSTM-GRU model with an error of 2.104 tons has the best performance compared to the rest of the models. By identifying key contributors to carbon footprints, these models can guide targeted interventions to reduce emissions. They can highlight the impact of industrial activities on per capita emissions, enabling policymakers to design more effective strategies. Therefore, in order to reduce pollution and increase the productivity of factories, using an advanced hybrid model, it is possible to identify the carbon footprint and make accurate predictions for different countries.

The exponential growth of digital content has made it increasingly difficult for users to understand the information, particularly in domains like political news. The study provides a framework for domain-specific multi-document political news summarization by using the transformer-based models that are fine-tuned on the synthetic dataset. The use of a generative Artificial Intelligence (AI) model, specifically the Bidirectional and Autoregressive Transformer (BART) model, for summarizing multi-document political news was explored in this paper. To improve the summarization process, real-world data was augmented, and a synthetic dataset was generated using the Adversarial Contextualized Text - Generative Adversarial Network (ACT-GAN) model to create a diverse and domain-specific training dataset. All four models, including BART, Pegasus, Text-To-Text Transfer Transformer (T5), and Seq2Seq, were fine-tuned and evaluated using a synthetic dataset generated via ACT-GAN and benchmarked using ROUGE metrics. BART performs the best in accuracy, recall, and F1-score, showing it can create clear and logical summaries while keeping the important context and structure intact. The results show that the transformer with the synthetic data helps to improve the quality of the political data summarization. In addition to addressing the challenges of extensive information, this framework also employs generative AI models in text summarization domains.

Rivers play a vital role in maintaining ecological balance. Accurate water quality predictions help manage pollution levels, protect aquatic life, and preserve biodiversity. Traditional water quality testing methods are often time-consuming and less accurate. Predictive models using machine learning and deep learning offer more precise and timely assessments, enabling proactive management strategies. This research aimed to create a dynamic system for predicting river water conditions by employing one standalone model and two hybrid models. The study analyzed four key parameters—pH, salinity, turbidity, and water temperature—across three rivers in Hong Kong, namely Kam Tin, Tung Chung, and Lam Tsuen. Data spanning 24 years (2000–2023) was utilized to forecast monthly levels of total dissolved solids (TDS). To analyze the collected data, it was partitioned into two sets: 80% for training and 20% for testing. The data was then utilized in various models, including a convolutional neural network (CNN), a long short-term memory (CNN-LSTM), a gate recurrent unit (CNN-LSTM-GRU), and a deep neural network (DNN). The performance of the models was assessed and compared using three metrics: root mean square error (RMSE), coefficient of determination (R²), and Nash-Sutcliffe (NSE). The findings indicated that the hybrid CNN-LSTM-GRU model outperformed the other two models in terms of accuracy and efficiency. This model has the potential to be applied to other rivers, enhancing the speed and effectiveness of water quality monitoring and control.