Abstract: In this work, we utilize machine learning to build a diabetes risk prediction model which is uniquely suited to a dataset of only females. The dataset focuses on important health ...

This work includes two high performance recognizers. The SVM based recognizer has an accuracy of 90%. It first applies projection-based algorithm to the input image, then use a pre-trained SVM model ...

The “gut–skin axis” has been proposed to play an important role in the development and symptoms of atopic dermatitis. Therefore, we have constructed an interpretable machine learning framework to ...

Abstract: In recent times, studies about remote-sensing methods have focused on improving variables like sensing distance, sensitivity, and power consumption of available remote-sensing methods. The ...

The rise of artificial intelligence (AI) deep learning algorithms is helping to accelerate brain-computer interfaces (BCIs). Published in this month’s Nature Neuroscience is new research that shows ...

Nowadays, remotely sensed data has increased dramatically. Microwaves and optical images with different spatial and temporal resolutions are available and are used to monitor a variety of ...

Machine learning and deep learning have been widely embraced, and even more widely misunderstood. In this article, I’ll step back and explain both machine learning and deep learning in basic terms, ...