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 ...

The study of clustering and classification of uncertain data addresses the challenges posed by imprecise, noisy, or inherently probabilistic measurements common in many modern data acquisition systems ...

An effective data loss prevention (DLP) strategy is essential for protecting your organization's data, but without proper data classification, even the best DLP tools can fall short. Data ...

All college data are classified into levels of sensitivity to provide a basis for understanding and managing college data. Accurate classification provides the basis to apply an appropriate level of ...

When it comes to managing data, we need to know where it is – but we also need to know what it is. With the rise in regulatory controls, enterprises now pay more attention to data sovereignty, ...

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 ...

TEL AVIV, Israel--(BUSINESS WIRE)--In a revolutionary move, Flow has designed data classification powered by Large Language Models (LLMs). With a focus on unstructured data, this technology can ...

Information technology and data constitute valuable Connecticut College assets. The purpose of data classification is to identify college data and it’s sensitivity. In order to protect the security, ...

Here's a complete end-to-end demo of what Dr. James McCaffrey of Microsoft Research says is arguably the simplest possible classification technique. The goal of a machine learning classification ...

To ensure a common understanding, Harvard uses a 5-step scale for data sensitivity. The higher the number, the more sensitive the data is, and the stronger protections you need to take when accessing ...

BiLSTM, an ICD-11 automatic coding model using MC-BERT and label attention. Experiments on clinical records show 83.86% ...