The advancement of artificial intelligence (AI) and the study of neurobiological processes are deeply interlinked, as a ...

In this research, we present a Python implementation using ... in deep learning have presented a novel architecture known as the Vision Transformer (ViT). In fact, vision transformer has shown ...

Deep learning ... can use keywords to guide the robot, with the legged robot using a combination of thermal camera and RGB camera where thermal images were used for ROI extraction based on hotspots, ...

In preparation phase, we train a deep learning-based classifier to do the image classification task. In application phase, users may input the UML class diagram of a micro-architecture into ... We ...

Neural architecture search is an aspect of AutoML, along with feature engineering, transfer learning ... for example in image classification and language translation, have required considerable ...

We evaluated the classification (segment) performance by using more than 13,766 images and obtained 95.75% training and 95.23% validation accuracies without overfitting issues. This research aimed to ...

Using feature descriptors and time-frequency image with deep learning is a better alternative. Most voice pathology deep learning frameworks are based on binary classification model. Implementing ...

ANNs have inspired the creation of CNN, a Deep learning algorithm ... scale and r = 3 for RGB images). Normally, the CNN has the capability to perform image classification using raw images as direct ...