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

This study proposes the use of Machine Learning ... by preprocessing the images to retain only relevant eye diagrams. A CNN is then applied as a feature extractor, with its attributes used as input ...

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 ...

We conduct a comparative analysis of the performance of LSTM and TextCNN classification ... widespread implementation across industries such as language translation, image recognition, driverless cars ...

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 this study, we have proposed and investigated seven different classifiers to efficiently classify the MRI brain tumor images into benign or malignant types. The deep learning (DL)-based neural ...

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 ...