This research investigates the potential advantages of using artificial intelligence (AI) to
drive ensemble machine learning (ML) for enhancing cost strategies and maximizing profits …

The classification of imbalanced datasets is a prominent task in text mining and machine
learning. The number of samples in each class is not uniformly distributed; one class …

Recent advances in time series forecasting and anomaly detection have been attributed to
the growing popularity of deep learning approaches. Traditional methods, such as rule …

Privacy violations concern many social networking sites users. Here, we seek to understand
how it might affect SNS use reduction. In Study 1, we untangle a mechanism through which …

Many oversampling methods applied to imbalanced data generate samples according to
local density distribution of minority samples. However, samples generated by these …

Anomaly detection in time series data is essential for fraud detection and intrusion
monitoring applications. However, it poses challenges due to data complexity and high …

Inflation growth in Indonesia and other countries impacts the currency value and investors'
purchasing power, particularly in the transportation sector. This research explores the impact …

One of the prominent challenges encountered in real-world data is an imbalance,
characterized by unequal distribution of observations across different target classes, which …

Green practices are social practices that aim to harmonize the relations between people and
the natural environment. They may involve minimizing the use of resources and the …

Text categorization remains a formidable challenge in information retrieval, requiring
effective strategies, especially when applied to low-resource languages such as Italian. This …