NeuSomatic is based on deep convolutional neural networks for accurate somatic mutation detection. With properly trained models, it can robustly perform across sequencing platforms, strategies, and ...

[and subsequent follow on work: Experimental Exploration of Compact Convolutional Neural Network Architectures for Non-temporal Real-time Fire Detection] "In this work we investigate the automatic ...

Abstract: Polycystic Ovary Syndrome (PCOS), a rampant endocrine anomaly in ladies, necessitates accurate and efficient diagnostic methods. This study presents a groundbreaking approach using a Custom ...

Deep learning shows promising results in extracting useful information from medical images. The proposed work applies a Convolutional Neural Network (CNN) on retinal images to extract features that ...

To address the difficulties in fusing multi-mode sensor data for complex industrial machinery, an adaptive deep coupling convolutional auto-encoder (ADCCAE) fusion method was proposed. First, the ...

The interplay between data symmetries and network architecture is key for efficient learning in neural networks. Convolutional neural networks perform well in image recognition by exploiting the ...

Drowsiness detection is a safety technology that can prevent accidents that are caused by drivers who fell asleep while driving. The objective of this intermediate Python project is to build a ...

Abstract: Defect detection on solid wood surface has two main problems: (1) the real-time performance of the available methods are poor despite good detection accuracy, and (2) the defect extraction ...

Deep learning techniques, and in particular Convolutional Neural Networks (CNNs), have led to significant progress in image processing. Since 2016, many applications for the automatic identification ...