Nonalcoholic fatty liver disease (NAFLD) is the most common cause of chronic liver disease, and if it is accurately predicted ...
Optical computing has emerged as a powerful approach for high-speed and energy-efficient information processing. Diffractive ...
Artificial intelligence brings to classification a scalable, accurate alternative. Using natural language processing and ...
Introduction Application of artificial intelligence (AI) tools in the healthcare setting gains importance especially in the domain of disease diagnosis. Numerous studies have tried to explore AI in ...
Discover the power of predictive modeling to forecast future outcomes using regression, neural networks, and more for improved business strategies and risk management.
Abstract: Data privacy and heterogeneity among healthcare settings present fundamental challenges to machine learning (ML) brain tumor classification (BTC) model development based on local data. In ...
Abstract: Technology development has led to increased data generated in education, sparking interest in information extraction to support educational management through automated data analysis. Using ...
When AI models fail to meet expectations, the first instinct may be to blame the algorithm. But the real culprit is often the data—specifically, how it’s labeled. Better data annotation—more accurate, ...
Background: To establish a classification model for assisting the diagnosis of type 2 diabetes mellitus (T2DM) complicated with coronary heart disease (CHD). Methods: Patients with T2DM who underwent ...
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 ...
Some results have been hidden because they may be inaccessible to you
Show inaccessible results
Feedback