Advancements in technology, particularly in Artificial Intelligence (AI), are transforming our ability to predict and manage natural disasters. This technology, with its ability to analyze vast ...

machine-learning-algorithms bayesian-network naive-bayes-classifier data-preparation svm-classifier decision-tree-algorithm knn-classification random-forest-classifier concept-learning Updated on Aug ...

A machine learning-based model can predict 30-day in-hospital mortality among patients with asthma in the ICU.

1. From 'Simulating Humans' to 'Data-Driven': The Ultimate Goal and Implementation Path of AI ...

The code uses the scikit-learn machine learning library to train a decision tree on a small dataset of body metrics (height, width, and shoe size) labeled male or female. Then we can predict the ...

Background Machine learning based on clinical characteristics has the potential to predict coronary CT angiography (CCTA) findings and help guide resource utilisation.Methods From the SCOT-HEART ...

With the advent of the computer science, the data volume that needed to be processed under many practical situations increases dramatically, challenging many traditional machine learning techniques.

What if plants could speak when they were thirsty? Agriculture, in essence, is a dialog among crops, soil and climate. Yet drought, the most insidious stressor, remains largely silent until its damage ...

The artificial intelligence method was used to optimize an early cancer detection test to ensure high sensitivity and specificity.

Engineered nanozymes and explainable machine learning enable sensitive bacterial detection across complex conditions. The system uses three distinct signals and delivers transparent, verifiable ...

The most successful institutions won't see artificial intelligence as merely an efficiency tool but as a responsibility to ...

1. Background and Significance of Public Opinion Monitoring ...