AIOps changes this by applying machine learning and advanced analytics. Consequently, the managed NOCs gain context, ...

In food drying applications, machine learning has demonstrated strong capability in predicting drying rates, moisture ...

Abstract: Accurate Short-Term Load Forecasting (STLF) is essential for effective operational planning, particularly for optimizing maintenance schedules, managing power generation capacity, and ...

Adnan and colleagues evaluated machine learning models’ ability to screen for Parkinson’s disease using self-recorded smile videos. 2. The models achieved high sensitivity and specificity among ...

Abstract: Credit card fraud detection presents a significant challenge due to the extreme class imbalance in transaction datasets. Traditional machine learning models struggle to achieve high recall ...

A research team has developed a new way to measure and predict how plant leaves scatter and reflect light, revealing that ...

For years, the artificial intelligence industry has followed a simple, brutal rule: bigger is better. We trained models on ...

Pharmaceutical Separation Science Session Day two of HPLC 2025 concluded with a session on pharmaceutical separations chaired ...

The progression of glaucoma was accurately predicted by machine learning models based on structural, functional and vascular ...

A newly developed artificial intelligence (AI) model is highly accurate in predicting blood loss in patients undergoing high-volume liposuction, reports a study in the January issue of Plastic and ...

The strong role of socioeconomic factors underscores the limits of purely spatial or technical solutions. While predictive models can identify where risk concentrates, addressing why it does so ...

Reverse Logistics, Artificial Intelligence, Circular Economy, Supply Chain Management, Sustainability, Machine Learning Share and Cite: Waditwar, P. (2026) De-Risking Returns: How AI Can Reinvent Big ...