Hundreds of thousands of children in China have been separated from their parents. A Yale SOM study finds that a machine-learning approach could cut years off family reunification efforts by matching ...
A machine learning model trained on EEG data from patients recovering from strokes helps predict how new patients will regain ...
Supervised learning algorithms like Random Forests, XGBoost, and LSTMs dominate crypto trading by predicting price directions ...
Dengue and chikungunya, the two mosquito-borne diseases that frequently circulate at the same time, share the same Aedes ...
Researchers have developed a powerful machine learning framework that can accurately predict and optimize biochar production from algae, offering a ...
Passive sensing via wearable devices and smartphones, combined with machine learning (ML), enables objective, continuous, and noninvasive mental health monitoring. Objective: This study aimed to ...
The development of humans and other animals unfolds gradually over time, with cells taking on specific roles and functions ...
From fine-tuning open source models to building agentic frameworks on top of them, the open source world is ripe with ...
Abstract: Existing machine learning-based methods for series arc fault (SAF) identification still suffer from slow training speed when dealing with large-scale SAF datasets. For this reason, we ...
Abstract: Hunger is one of the global issues that needs attention, especially in developing countries. This study aims to compare which model can accurately predict the risk of hunger in a country ...
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