Genomic surveillance—the process of monitoring and sequencing pathogens—is one of the most important tools for detecting ...

Combinatorial optimization problems are often encountered in real-world applications, including logistics, scheduling and ...

Aims To develop prediction models for identifying cases with poor visual outcomes after surgery for primary rhegmatogenous ...

Support vector regression can predict numeric values effectively, and this article shows how to implement and train a kernel SVR model in C# using stochastic sub-gradient descent.

A hybrid machine learning and metaheuristic framework optimizes sludge, fly ash, slag, and gypsum mixtures to improve compressive strength. The approach enables sustainable sludge reuse, reduces waste ...

A recent study published in npj Materials Degradation introduces a two-stage machine learning (ML) framework that predicts the degradation of protective coatings under various environmental conditions ...

Abstract: Machine learning (ML) models were used to determine the moisture content (MC) for multiple grains and seeds after training on a large dataset obtained through several decades of research.

Researchers from Spain’s Valencia Polytechnic University have developed a novel method for forecasting the power generation of PV systems. Its novelty lies in developing a hyperparameter optimization ...

Abstract: Machine learning (ML) algorithms can accurately extract quantitative patterns from datasets without requiring prior knowledge, playing an increasingly crucial role in tasks such as ...

The rise of artificial intelligence (AI) deep learning algorithms is helping to accelerate brain-computer interfaces (BCIs). Published in this month’s Nature Neuroscience is new research that shows ...