Abstract: This study compares the relative utility of deep learning models as automated phenotypic classifiers, built with features of peripheral blood cell populations assayed with flow cytometry. We ...

Jesse Todd, CEO of EncompaaS, is a SaaS expert specializing in information management and risk mitigation for Fortune 500 companies. AI has the potential to transform how organizations operate, but ...

Background: The performance of a classification algorithm eventually reaches a point of diminishing returns, where the additional sample added does not improve the results. Thus, there is a need to ...

Classification of gas wells is an important part of optimizing development strategies and increasing the recovery. The original classification standard of gas wells in the Sulige gas field has weak ...

On 1,200 acres of cornfield in Indiana, Amazon is building one of the largest computers ever for work with Anthropic, an artificial intelligence start-up. On 1,200 acres of cornfield in Indiana, ...

Abstract: Imbalanced data remains a challenge in classification research and significantly influences classifier performance. The strategy that is widely used to address this issue is the data-level ...

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 ...

USU’s data classification system organizes data into three tiers based on the potential adverse impact of unauthorized access, use, or alteration. This system helps us manage risks associated with ...

For lithologic oil reservoirs, lithology identification plays a significant guiding role in exploration targeting, reservoir evaluation, well network adjustment and optimization, and the establishment ...

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 ...