Breiman, L. (2001) Random Forests. Machine Learning, 45, 5-32. - References - Scientific Research Publishing Home References Follow SCIRP Contact us customer@scirp.org +86 18163351462 (WhatsApp) ...

Abstract: The class imbalance issue has been a persistent problem in machine learning that hinders the accurate predictive analysis of data in many real-world applications. The class imbalance problem ...

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

The Titanic Survival Prediction project is a supervised machine learning classification project that predicts whether a passenger survived the Titanic disaster. The project uses passenger information ...

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Abstract: Heart failure is considered one of the leading cause of death around the world. The diagnosis of heart failure is a challenging task especially in under-developed and developing countries ...

Researchers developed a new model to predict the likelihood of critical illness in patients with connective-tissue disease-associated ILD.

Supervised machine learning improves predictions of compressive strength in industrial waste-modified concrete, supporting ...

摘要：骨骼残骸的性别估算是法医人类学(Forensic Anthropology)的关键环节，其中颅骨与骨盆是形态学上两性异形(Sexual Dimorphism)最显著的部位。传统多元统计方法如判别函数分析(Discriminant Function Ana 摘要：骨骼残骸的性别估算是法医人类学(Forensic Anthropology)的关键环节，其中颅骨与骨盆是形态学上两性异形(Sexua ...

As humans, our eyes take in two-dimensional images that our brains convert to three-dimensional experiences. This ability ...

Six machine learning algorithms—k-nearest neighbors, naive Bayes, multilayer perceptron, random forest, support vector machine, and Extreme Gradient Boosting (XGBoost)—were developed using 10-fold ...