With the rise of social media, individuals face challenges in decision-making due to the abundance of options available. Recommender Systems (RSs) leverage Artificial Intelligence (AI) to provide users with personalized suggestions aligned with their preferences and interests. This study presents a systematic review of AI-based Recommender Systems, focusing on recent advancements and primary studies published between 2019 and 2024. While several review papers have addressed various aspects of RSs, the rapid evolution of AI techniques necessitates an updated review to capture the latest trends and innovations. We systematically gathered data from five major databases: IEEE, Springer, Science Direct, ACM, and Wiley. Through the PRISMA methodology, we selected 85 relevant studies. Our analysis addresses several key research questions: the types of datasets and data sources used, major application fields, prevalent machine learning and AI techniques, overall research productivity, and the limitations and future trends in AI-based RSs. Our findings indicate that advanced AI techniques, particularly those incorporating deep learning with multiple hidden layers and transformer models like BERT, significantly enhance the accuracy and effectiveness of Recommender Systems. Furthermore, we observed a trend towards integrating contextual and real-time data to improve recommendation relevance. Additionally, we discuss ethical considerations such as privacy, data security, bias, and transparency, emphasizing the need for responsible AI development to ensure fair and equitable recommendations. These insights can guide future research and development efforts in the field.

The world has to face health concerns due to huge spread of COVID. For this reason, the development of vaccine is the need of hour. The higher vaccine distribution, the higher the immunity against coronavirus. Therefore, there is a need to analyse the people’s sentiment for the vaccine campaign. Today, social media is the rich source of data where people share their opinions and experiences by their posts, comments or tweets. In this study, we have used the twitter data of vaccines of COVID and analysed them using methods of artificial intelligence and geo-spatial methods. We found the polarity of the tweets using the TextBlob() function and categorized them. Then, we designed the word clouds and classified the sentiments using the BERT model. We then performed the geo-coding and visualized the feature points over the world map. We found the correlation between the feature points geographically and then applied hotspot analysis and kernel density estimation to highlight the regions of positive, negative or neutral sentiments. We used precision, recall and F score to evaluate our model and compare our results with the state-of-the-art methods. The results showed that our model achieved 55% & 54% precision, 69% & 85% recall and 58% & 64% F score for positive class and negative class respectively. Thus, these sentimental and spatial analysis helps in world-wide pandemics by identify the people’s attitudes towards the vaccines.

In today’s world, security is the most prominent aspect which has been given higher priority. Despite the rapid growth and usage of digital devices, lucrative measurement of crimes in under-developing countries is still challenging. In this work, unstructural crime data (900 records) from the news archives of the previous eight years were extracted to predict the behavior of criminals’ networks and transform it into useful information using natural language processing (NLP). To estimate the next move of criminals in Pakistan, we performed hotspot-based spatial analysis. Later, this information is fed to two different classifiers for possible identification and prediction. We achieved the maximum accuracy of $92\%$ using K-Nearest Neighbor (KNN) and $62\%$ using the Random Forest algorithm. In terms of crimes, the results showed that the most prevalent crime events are robberies. Thus, the usage of digital information archives, spatial analysis, and machine learning techniques can open new ways of handling a peaceful and sustainable society in eradicating crimes for countries having paucity of financial resources.

COVID-19 has significantly impacted individuals, communities, and countries worldwide. These effects include health impacts, economics impacts, social impacts, educational, political and environmental impacts. The COVID-19 vaccine development was crucial for disease control and monitoring, yet the threat still looms large. Vaccine recommender systems can help the health practitioners in combating COVID-19 by providing the information and guidance on the benefits and risks of COVID-19 vaccines to individuals based on their preferences and medical history. In this paper, we have proposed sentiment analysis based recommender system for COVID-19 vaccines. We used Twitter data of 10,000 tweets about COVID-19 vaccines and applied pre-processing steps. We propose an ensemble of random forest with CT-BERT_CONVLayerFusion model, a novel algorithm, for classifying the tweets into seven different categories of sentiments. We also performed aspect-based review categorization which works on the queries given by a user. We compared the results of sentiment classification with the state-of-the-art with metrics including accuracy, recall, precision, and F1-score, and found out that our proposed approach outperformed all other state-of-the-art model by achieving maximum accuracy, recall, precision and F1-measure. Hence, such advanced methods can help somehow to fight COVID-19 as well as reducing the vaccine hesitancy by suggesting proper vaccines to patients based on the their specific concerns and questions.

This paper introduces CONV_GBERT, a hybrid deep learning framework for robust sentiment classification. The model integrates the bidirectional contextual understanding of BERT, the generative capabilities of GPT, and the local feature extraction strength of convolutional neural networks. By concatenating embeddings from BERT and GPT and passing them through a convolutional layer, CONV_GBERT captures both global semantic meaning and fine-grained sentiment patterns in text. To further enhance robustness and generalization, we incorporate Improved Fast Gradient Sign Method (IMP-FGSM) for adversarial training. IMP-FGSM introduces controlled perturbations to the input embeddings and iteratively refines them using gradient-based updates. This technique improves the model’s resistance to adversarial attacks while preserving valid token boundaries. Experiments on benchmark sentiment datasets demonstrate that CONV_GBERT significantly outperforms baseline models. It achieves an accuracy of 84.89%, precision of 83.67%, recall of 82.88%, and an F1 score of 83.37%. These results highlight the potential of combining multiple transformer-based architectures and adversarial training to advance sentiment analysis in complex, noisy, and dynamic textual environments.