Abstract: Tabular data is the most prevalent form of structured data, necessitating robust models for classification and regression tasks. Traditional models like eXtreme Gradient Boosting (XGBoost) ...

Abstract: Imbalanced data exist extensively in the real world, and the classification of imbalanced data is a hot topic in machine learning. In order to classify imbalanced data more effectively, an ...

ABSTRACT: Accurate prediction of malaria incidence is indispensable in helping policy makers and decision makers intervene before the onset of an outbreak and potentially save lives. Various ...

Unlock the power of your data with an effective data governance framework for security, compliance, and decision-making. Data governance frameworks are structured approaches to managing and utilizing ...

As organizations evolve, traditional data classification—typically designed for regulatory, finance or customer data—is being stretched to accommodate employee data. While classification processes and ...

In today's digital landscape, organizations face an unprecedented challenge: managing and protecting ever-growing volumes of data spread across multiple environments. As someone deeply involved in ...

Many academic institutions apply their data classification schemas in service of a range of institutional functions. At UW–Madison, for example, we use data classification in the following ways: With ...

In an era where sensitive data is a prime target for cyberattacks and compliance violations, effective data classification is the critical first step in safeguarding information. Recognizing the ...

PR1, W1, T51, F58, SL4, KL3, SM11. This is not a test to crack a code. But you will see a series of letter and number combinations while engaging with the Paralympics in Paris. At the Olympics, there ...

Twenty years ago, mortgage-backed securities and collateralized debt obligations were all the rage. These new financial products were, initially, a wonder: they helped put millions of people into ...