Corporate default predictions play an essential role in each sector of the economy, as
highlighted by the global financial crisis and the increase in credit risk. This study reviews …

Abstract Artificial Intelligence (AI) and Machine Learning (ML) are currently hot topics in
industry and business practice, while management-oriented research disciplines seem …

Previous studies of bankruptcy prediction in imbalanced datasets analyze either the loss of
prediction due to data imbalance issues or treatment methods for dealing with this issue …

The availability of data in massive collections in recent past not only has enabled data-
driven decision-making, but also has created new questions that cannot be addressed …

The Twitter messaging service has become a platform for customers and news consumers to
express sentiments. Accurately capturing these sentiments has been challenging for …

Nowadays, big data plays a substantial part in information knowledge analysis,
manipulation, and forecasting. Analyzing and extracting knowledge from such big datasets …

Breast cancer is one of the leading causes of death among women worldwide. Accurate and
early detection of breast cancer can ensure long-term surviving for the patients. However …

We examine whether corporate bankruptcy predictions can be improved by utilizing the
recurrent neural network (RNN) and long short-term memory (LSTM) algorithms, which can …

In small business credit risk assessment, the default and nondefault classes are highly
imbalanced. To overcome this problem, this study proposes an extended ensemble …

With the help of machine learning (ML) techniques, the possible errors made by the
pathologists and physicians, such as those caused by inexperience, fatigue, stress and so …