In machine learning, two approaches outperform traditional algorithms: ensemble learning
and deep learning. The former refers to methods that integrate multiple base models in the …

With the advent of technological marvels like global digitization, the prediction of the stock
market has entered a technologically advanced era, revamping the old model of trading …

Ensemble learning techniques have achieved state-of-the-art performance in diverse
machine learning applications by combining the predictions from two or more base models …

The availability of digital technology in the hands of every citizenry worldwide makes an
available unprecedented massive amount of data. The capability to process these gigantic …

In stock market forecasting, the identification of critical features that affect the performance of
machine learning (ML) models is crucial to achieve accurate stock price predictions. Several …

The financial sector has greatly impacted the monetary well-being of consumers, traders,
and financial institutions. In the current era, artificial intelligence is redefining the limits of the …

To predict the class level of any classification problem, predictive models are used and
mostly a single predictive model is built to predict the class level of any classification …

Within the area of stock market prediction, forecasting price values or movements is one of
the most challenging issue. Because of this, the use of machine learning techniques in …

Abstract Social Customer Relationship Management (social CRM) has become one of the
central points for many companies seeking to improve their customer experience. It …

Evaluating and understanding the financial impacts of COVID-19 has emerged as an urgent
research agenda. Nevertheless, the impacts of government interventions on stock markets …