As profit season ramps up, companies like Orica and Worley are turning to AI and analysis to better communicate and offset ...

A mother's health during pregnancy, childbirth and the postpartum period is the foundation of lifelong well-being, directly influencing a child's development and long-term outcomes, yet most ...

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 ...

Abstract: In recent years, diabetes has become more prevalent due to unhealthy lifestyles, obesity, and other factors. Diabetes is a chronic and dangerous disease due to its major complications that ...

From fine-tuning open source models to building agentic frameworks on top of them, the open source world is ripe with ...

Accurately tracking atmospheric greenhouse gases requires not only fast predictions but also reliable estimates of uncertainty.

MIT researchers have identified significant examples of machine-learning model failure when those models are applied to data other than what they were trained on, raising questions about the need to ...

Machine learning can help predict whether people newly diagnosed with MS will experience disability worsening that occurs ...

Who is a data scientist? What does he do? What steps are involved in executing an end-to-end data science project? What roles are available in the industry? Will I need to be a good ...

It is instructive to acknowledge the complex interplay among AI, energy availability, and decarbonisation. While the AI ...

A collaborative approach to training AI models can yield better results, but it requires finding partners with data that complements your own.

Abstract: Diabetic retinopathy is a serious eye disease which can lead to vision defects in diabetic patients. Early detection is important for preventing vision loss. Automating the detection process ...