Abstract: The probit regression model is a model used to analyze the relationship between categorical response variables, with predictive variables that are numerical, categorical, or the combination ...

HIV and tuberculosis (TB) remain closely linked public health threats in sub-Saharan Africa, with South Africa bearing the highest burden of both diseases. In KwaZulu-Natal, where HIV prevalence peaks ...

A great job, the model's performance has been tested through univariate fine-tuning in the openltm . Our dataset has exogenous variables, which have been proven to be effective in predicting the ...

A new kind of large language model, developed by researchers at the Allen Institute for AI (Ai2), makes it possible to control how training data is used even after a model has been built.

Solving optimization problems is challenging for existing digital computers and even for future quantum hardware. The practical importance of diverse problems, from healthcare to financial ...

Abstract: The article considers the problem of forecasting the volume of seasonal logistics transportation of fruits and vegetables using time series based on the seasonal autoregressive integrated ...

One of the biggest issues with large language models (LLMs) is working with your own data. They may have been trained on terabytes of text from across the internet, but that only provides them with a ...

The SAMR framework of technology integration—substitution, augmentation, modification, redefinition—has been around since 2010. Other models have been developed in that time, but I find the SAMR model ...

Conclusions: We developed a machine learning model for delirium prediction in ICU patients using routinely measured variables, including physiological waveforms. Our study demonstrates the potential ...