Abstract: Precise forecasting of solar power output is crucial for integrating renewable energy into power networks, improving efficiency and dependability. This study assesses the efficacy of several ...

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

Machine learning is increasingly used in content-based recommender algorithms to guide journal submissions for academic articles. Objective: We sought to evaluate the performance of open-source ...

A privacy-preserving marketing framework applies homomorphic encryption to perform machine learning on encrypted ...

Medicine is rapidly evolving from statistical, evidence-based approaches to predictive, genotype-directed care, driven by ...

Abstract: As fake news spreads rapidly in social media, attempts to develop detection technology to automatically identify fake news are actively being developed, recently. However, most of them focus ...

Freshwater ecosystems worldwide have been suffering from declining oxygen levels—a trend known as deoxygenation—that ...

A new algorithm could drive breakthroughs in understanding cancer, Alzheimer's disease and other potentially fatal conditions ...

This study proposes a novel method for designing prosthetic heart valves (PHVs) by combining machine learning (ML) with optimization algorithms. This approach aims to overcome the limitations of ...

Six ML algorithms (Extreme Gradient Boosting [XGBoost], logistic regression (LR), Light Gradient Boosting Machine [LightGBM], random forest [RF], support vector machine [SVM], and k-nearest neighbor ...

Breast cancer diagnosis relies on imaging, yet conventional Doppler ultrasound possesses limitations in visualizing tumor microvasculature. This study aimed to compare Microvascular Flow imaging ...

But unlike most quants, I run a concentrated, fundamentals-based portfolio. More than 50% of my fund is invested in only eight companies, and they're the kinds of stocks that Peter Lynch and Charlie ...