Traditional machine learning methods like Support Vector Machines, Random Forest, and gradient boosting have shown strong performance in classifying device behaviors and detecting botnet activity.

Two projects from the Arab World exemplify the potential of AI-driven approaches to disaster response and displacement monitoring.

M, a transformer-based AI trained on UK Biobank and Danish health data to predict and simulate lifetime trajectories for ...

AI in drug discovery Artificial intelligence is rapidly transforming the way new drugs and therapeutic targets are discovered ...

A recent study shows that 1 in 5 people use AI every day. From the chatbot helping you budget smarter to the recommendations ...

WiMi Hologram Cloud Inc. (NASDAQ: WiMi) ("WiMi" or the "Company"), a leading global Hologram Augmented Reality ("AR") Technology provider, today announced that they are actively exploring Scalable ...

Zapier reports that AI automation enhances traditional automation by combining intelligent technologies, improving efficiency ...

Artificial Intelligence (AI) has become a part of everyday life. It is visible in medical chatbots that guide patients and in generative tools that assist artists, writers, and developers. These ...

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 ...

This project applies feature extraction and machine learning to classify breast cancer (benign vs. malignant) using the Kaggle Breast Cancer Dataset. The goal is to achieve >90% accuracy, and the ...

Demographic bias gaps are closing in face recognition, but how training images are sourced is becoming the field’s biggest privacy fight.