Predicting the success of marketing campaigns is a critical challenge in the fast-moving business world. This challenge requires advanced models that can help deal with complicated consumer behavior and tell us what actions to take. The goal of this study is to create and test a hybrid deep learning model that predicts campaign success using feature embeddings and dense neural networks. The hybrid model uses TabNet in its analysis to identify key features. The analysis identifies NumWebPurchases, MntGoldProds, Teenhome, Income, and Recency as some of the key predictors. Moreover, through which this knowledge is integrated into its learning frameworks. The hybrid model performed better with $91.24 \%$ accuracy, $89.76 \%$ precision, $89.32 \%$ recall, $89.54 \%$ F1-score compared to Multi-Layer Perceptron (MLP), TabNet and Deep Belief Networks (DBN). Presently, the model’s capability to reduce false negatives assists target differentiations in ways that present-day methods do not. Highlighting the relevance of examining feature importance, this exploration also gives marketers a chance to grasp consumer behavior and market trends which could be seen as beneficial.

Fraud or Fake financial transactions seriously impact digital payment systems, necessitating more advanced detection mechanisms to mitigate the associated risks. Fraud trends that are always changing have made the traditional methods used to identify fraud cases obsolete, such as rule-based fraud detection and machine learning models. Recent studies have shown that Graph Neural Networks (GNNs) can better capture the relationship between financial transactions, while transformers are effective at recognizing sequential fraud patterns. Yet, the existing models that incorporate both do not perform well in this manner. To fill this gap in existing research, we have created a new model for detecting fraudulent transactions, the Hybrid GNN-Transformer Fraud Detection Model. This uses graphbased learning along with deep sequential feature extraction to better distinguish frauds from genuine transactions. The hybrid model had better performance compared to single models such as autoencoders, GNNs, and LSTMs, getting an accuracy of $99 \%$, as well as a precision of.99 and a recall of 1.00 when it comes to detecting fraudulent transactions. Comparisons show that GNNs and LSTMs still, when combined with transformers, there is an improved ability in the identification ofare able to capture key transaction interdependencies on their own. Still, when combined with transformers, they have an improved ability to identify complicated fraud activities.

Air pollution is still a major problem in cities where pollutants, such as ozone (O3) and sulfur dioxide (SO2), can harm health and the environment. Thus, being able to forecast the Air Quality Index (AQI) can help make better-informed decisions and interventions. This study assesses traditional machine learning (ML), deep learning (DL), and hybrid models in AQI prediction using real-world data from New York City from 2014 to 2015. The research involved comparing various machine learning models such as Random Forest, XGBoost, and Support Vector Regression, as well as Long Short-Term Memory (LSTM) networks and hybrid models combining ML with DL techniques. Model performance was evaluated based on Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and the coefficient of determination ($\mathbf{R}^{2}$) values in multiple neighborhoods. The authors found that Random Forest and XGBoost performed better than stand-alone LSTM on both accuracy of forecasting and stability. A hybrid model with Random Forest and LSTM was also more robust than the stand-alone LSTM models in all neighborhoods.