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Hybrid methods improve key variable prediction in process industry using small noisy datasets

Soft measurement based on data-driven models is widely used to predict key variables in process industry due to low cost and real-time capability. However, these models struggle with noisy datasets ...
Frontiers

Computational modelling and AI for Brain and Behaviour: integrating prediction with inference

Computational modelling, machine learning, and broader artificial (AI) intelligence approaches are now key approaches used to understanding and predicting ...
Phys.org

Scientists develop dynamic landslide prediction method using hydrological and machine learning data

Northwestern University and University of California, Los Angeles (UCLA) scientists have developed a new process-based framework that provides a more accurate and dynamic approach to landslide ...
ascopubs.org

Modeling Individual-Level Uncertainty From Missing Data in Multifactorial Breast Cancer Risk Prediction

Prognostic Significance of Isolated Tumor Cells and the Role of Immunohistochemistry in Nodal Evaluation in Breast Cancer: A SEER-Based Analysis and Reappraisal We used Monte Carlo simulation methods ...
ascopubs.org

Dynamic Mortality Risk Prediction in Myelodysplastic Syndromes Using Longitudinal Clinical Data

Patients with myelodysplastic syndromes (MDS) exhibit diverse disease trajectories necessitating different clinical approaches ranging from watch-and-wait strategies to hematopoietic stem cell ...