Additionally, the effects of social media platform type, machine learning approach, and use of outcome measures in depression prediction models need attention. Analyzing social media texts for ...

Significant predictors were selected on the training set using recursive feature elimination methods, followed by prediction model development using 7 machine learning algorithms (logistic regression, ...

Two years after revamping its developer programs and pricing, X is expanding the closed beta of a pay-per-use plan for its API to more developers. The social network is accepting applications from ...

A simple Flask application that can serve predictions machine learning model. Reads a pickled sklearn model into memory when the Flask app is started and returns predictions through the /predict ...

A rotating cylinder with its side cut away to expose the core, showing patches of purple, blue, green, yellow, and orange that are dense in the middle and more diffuse toward the edges. This rotating ...

Department of Materials Science and Engineering, University of Delaware, Newark, Delaware 19716, United States Department of Chemistry, University of Pennsylvania, Philadelphia, Pennsylvania 19104, ...

Machine-learning models identify relationships in a data set (called the training data set) and use this training to perform operations on data that the model has not encountered before. This could ...

Researchers have found a way to make the chip design and manufacturing process much easier — by tapping into a hybrid blend of artificial intelligence and quantum computing. When you purchase through ...

The Department of Justice recently issued guidance encouraging federal agencies to use “artificial intelligence and machine translation to communicate with individuals who are limited English ...

Abstract: Crop diseases have a disproportionately large economic effect on farmers and threaten food security. Predictive Model for Crop Disease and Management System, which uses machine and deep ...