Text summarization plays a crucial role in natural language processing by condensing large volumes of textual information into concise and meaningful summaries. With the rapid growth of digital content, existing summarization approaches often struggle to balance contextual understanding and semantic relevance. This paper presents a PMI-driven BERT-...

By R. Ramesh, N. Subalakshmi, S. Selvarani, K. Kavitha, M. Jeyakarthic

Accurate prediction of stock market trends remains a challenging task due to high volatility, non-linearity, and the dynamic nature of financial time series data. Conventional statistical and machine learning typically do not provide consistent performance due to the fixed hyperparameter settings and the inability to adapt to a shifting market situ...

By N. Subalakshmi, M. Jeyakarthic, V. Mohanaselvam