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 ...
Medicine has always operated as an “evidence based” field, meaning that it generally pursues experimentation to gather ...
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 ...
Abstract: In online advertising, click fraud poses a significant challenge, draining budgets and threatening the industry’s integrity by redirecting funds away from legitimate advertisers. Despite ...
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 ...
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