In a recent paper, SFI Complexity Postdoctoral Fellow Yuanzhao Zhang and co-author William Gilpin show that a deceptively ...
As automation grows, artificial intelligence skills like programming, data analysis, and NLP continue to be in high demand ...
A recent study explored rapid evaporative ionization mass spectrometry (REIMS) as a high-throughput, real-time alternative. By analyzing metabolomic fingerprints from pig neck fat, REIMS was combined ...
This study aims to establish an interpretable disease classification model via machine learning and identify key features related to the disease to assist clinical disease diagnosis based on a ...
Abstract: This study addresses the lack of comprehensive evaluations of feature scaling by systematically assessing 12 techniques, including less common methods such as VAST and Pareto, in 14 machine ...
This study applied three models—random forest (RF), gradient boosting regression (GBR), and linear regression (LR)—to predict county-level LC mortality rates across the United States. Model ...
aDepartment of Thoracic Surgery and Oncology, The First Affiliated Hospital of Guangzhou Medical University, China State Key Laboratory of Respiratory Disease & National Clinical Research Center for ...
The goal of a machine learning binary classification problem is to predict a variable that has exactly two possible values. For example, you might want to predict the sex of a company employee (male = ...
Abstract: Predictive maintenance, utilising anomalous sound classification, demonstrates a strong potential to identify mechanical faults in industrial machinery. This research proposes a machine ...
ABSTRACT: Nowadays, understanding and predicting revenue trends is highly competitive, in the food and beverage industry. It can be difficult to determine which aspects of everyday operations have the ...
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