Machine learning requires humans to manually label features while deep learning automatically learns features directly from raw data. ML uses traditional algorithms like decision tress, SVM, etc., ...

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 ...

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 ...

1 School of Health Management, Changchun University of Traditional Chinese Medicine, Changchun, China 2 School of Humanities and Management, Zhejiang Chinese Medical University, Hangzhou, China ...

Creative Commons (CC): This is a Creative Commons license. Attribution (BY): Credit must be given to the creator. Graph neural networks for crystal property prediction typically require precise atomic ...

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 ...

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 ...