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 ...

Data Normalization vs. Standardization is one of the most foundational yet often misunderstood topics in machine learning and ...

When everyone has access to the same AI models, the same AI-enabled tools, and the same vendor ecosystem, organizational context becomes the differentiator. Context is demonstrated execution: the ...

BACKGROUND: Mental stress-induced myocardial ischemia is often clinically silent and associated with increased cardiovascular risk, particularly in women. Conventional ECG-based detection is limited, ...

Machine learning for health data science, fuelled by proliferation of data and reduced computational costs, has garnered ...

Machine learning is an essential component of artificial intelligence. Whether it’s powering recommendation engines, fraud detection systems, self-driving cars, generative AI, or any of the countless ...

Researchers sought to determine an effective approach to predict postembolization fever in patients undergoing TACE.

The degradation is subtle but cumulative. Tools that release frequent updates while training on datasets polluted with synthetic content show it most clearly. We’re training AI on AI output and acting ...

1 School of Computing and Data Science, Wentworth Institute of Technology, Boston, USA. 2 Department of Computer Science and Quantitative Methods, Austin Peay State University, Clarksville, USA. 3 ...

In some ways, Java was the key language for machine learning and AI before Python stole its crown. Important pieces of the data science ecosystem, like Apache Spark, started out in the Java universe.

If you’re learning machine learning with Python, chances are you’ll come across Scikit-learn. Often described as “Machine Learning in Python,” Scikit-learn is one of the most widely used open-source ...