Many past machine learning approaches to microplastic detection have been criticised for relying on idealised datasets ...

The patient knowledge gap is considered to be a critical shortcoming for the medical community, particularly at a time when ...

Unlike conventional black-box AI models that flag anomalies without explanation, IFAT produces decision trees that map the ...

While the public focuses on model size or benchmark wins, the layer where actual decisions happen gets far less attention.

As customer expectations evolve, businesses are seeking more advanced AI solutions that can bridge the gap between automated ...

His work reflects how AI-native platforms address singular challenges in financial data handling, fraud detection, and risk ...

Kubit, the leading customer journey analytics platform, today announced the launch of Ask Kubit, a conversational AI ...

A novel image-based deep learning approach achieves high accuracy and interpretability, offering potential for clinical ...

It is important that organizations understand who trains their AI systems, what data was used and, just as importantly, what went into their algorithms’ recommendations. A high-quality explainable AI ...

Researchers now employ artificial intelligence (AI) models based on deep learning to make functional predictions about big datasets. While the concepts behind these networks are well established, ...

Explainable AI (XAI) is a field of AI that focusses on developing techniques to make AI models more understandable to humans.