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 ...

1 Department of Software Science, Tallinn University of Technology (TalTech), Tallinn, Estonia 2 LEARN! Research Institute, Vrije Universiteit Amsterdam, Amsterdam, Netherlands Project-based learning ...

Background: Depression affects more than 350 million people globally. Traditional diagnostic methods have limitations. Analyzing textual data from social media provides new insights into predicting ...

Washing machines aren’t what they used to be. Some new appliances have over a dozen cycles, and then there are a slew of other things to consider, including water temperature, spin speed, cycle ...

Join us to learn about how to use cutting edge GPU infrastructure to solve real world material discovery problems with AI and unsupervised machine learning. Our lab in the Department of Materials ...

Predict whether a college student will be placed after graduation based on various academic and personal factors using machine learning algorithms. machine_learning_project/ │ ├── data/ # Data files │ ...

In some ways, Java was the key language for machine learning and AI before Python stole its crown. Important pieces of the data science ecosystem, like Apache Spark, started out in the Java universe.

School of Artificial Intelligence and Data Science, Unversity of Science and Technology of China, Hefei 230026, P. R. China Suzhou Institute for Advanced Research, University of Science and Technology ...

If you’re learning machine learning with Python, chances are you’ll come across Scikit-learn. Often described as “Machine Learning in Python,” Scikit-learn is one of the most widely used open-source ...

Experiment tracking is an essential part of modern machine learning workflows. Whether you’re tweaking hyperparameters, monitoring training metrics, or collaborating with colleagues, it’s crucial to ...

Objective: This study aims to develop and validate a machine learning model that integrates dietary antioxidants to predict cardiovascular disease (CVD) risk in diabetic patients. By analyzing the ...