For inpatients with cirrhosis, a machine learning (ML) model using random forest (RF) analysis is superior for prediction of ...

Despite the AI hype, tools are proving valuable for leading-edge chip manufacturing. More aggressive feature scaling and ...

A machine learning model may help predict mortality for hospitalized patients with cirrhosis at the time of admission, ...

The natural protein universe is vast, and yet, going beyond and designing new proteins not observed in nature can yield new ...

The disease is known for its strong association with climatic variables, especially excessive rainfall, high humidity, and ...

Agricultural firms are uniquely exposed to risks that include volatile commodity prices, geopolitical tensions, and uneven ...

Sai Krishna's work in text mining has earned him well-deserved recognition within his organization. His development of an RShiny-based machine learning workbench was a game-changer, leading to ...

Proper handling of continuous variables is crucial in healthcare research, for example, within regression modelling for descriptive, explanatory, or predictive purposes. However, inadequate methods ...

The era of predictive modeling enhanced with machine learning and artificial intelligence (AI) to aid clinical ...

During this study foresee the loan knowledge by utilizing machine learning algorithms that area unit logistical regression. Loan prediction is an extremely basic life issue that every genuine bank ...

Machine Learning is being utilized by educational institutions to effectively manage different processes and transactions. Several colleges and departments are using predicting models to obtain ...

Artificial intelligence models used to detect depression on social media are often biased and methodologically flawed, according to a study led by Northeastern University computer science graduates.