A study explores how AI and ML can improve early detection of neurological diseases, including Parkinson’s disease, ...

Background Adult-onset Still’s disease (AOSD) is a systemic autoinflammatory disorder lacking a gold-standard diagnostic ...

The emerging convergence of AI-first design principles and environmental consciousness is reshaping how we think about semiconductor development.

AI success depends on whether enterprise data is ready, reachable, and close enough to the workloads that need it. In this eSpeaks episode, Dell Technologies’ Vrashank Jain explains why fragmented ...

Abstract: This study examined methods for analyzing data with complex structures, extreme values, and NaN values using machine learning models. The techniques of removing NaN values and using KNN ...

SmartKNN is a nearest-neighbor–based learning method that belongs to the broader KNN family of algorithms.

The field of neuroimaging has undergone profound transformation in recent years, driven primarily by rapid advances in machine learning (ML), and especially deep learning (DL), techniques. These ...

Are Machine Learning (ML) algorithms superior to traditional econometric models for GDP nowcasting in a time series setting? Based on our evaluation of all models from both classes ever used in ...

ABSTRACT: The objective of this work is to determine the true owner of a land- public or private- in the region of Kumasi (Ghana). For this purpose, we applied different machine learning methods to ...

Postpartum depression (PPD) is a common and serious mental health complication after childbirth, with potential negative consequences for both the mother and her infant. This study aimed to develop an ...

If you rotate an image of a molecular structure, a human can tell the rotated image is still the same molecule, but a machine-learning model might think it is a new data point. In computer science ...