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

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 …

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

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 …

Stroke requires immediate medical care. The WHO states stroke is the leading cause of
death and disability world-wide. Thus, early stroke detection is essential. Few studies …

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 …

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 …

Understanding users' satisfaction is fundamental for enhancing the effectiveness and
usability of e-learning platforms. The existing approaches for analyzing users' satisfaction …

The impact of language models in various applications like machine translation, speech
recognition, and chatbots has transformed text-based services. Despite being spoken by …

In this research, an innovative state space-based Transformer model is proposed to address
the challenges of complex system prediction tasks. By integrating state space theory, the …