In recent years, many researchers focused on unsupervised learning for network anomaly
detection in edge devices to identify attacks. The deployment of the unsupervised …

Metal workpieces are indispensable in the manufacturing industry. Surface defects affect the
appearance and efficiency of a workpiece and reduce the safety of manufactured products …

Deploying deep neural network models on the resource constrained devices, eg, lost-cost
microcontrollers, is challenging because they are mostly limited in terms of memory footprint …

This study investigates the use of advanced computer vision techniques for assessing the
severity of Orientia tsutsugamushi bacterial infectivity. It uses fluorescent scrub typhus …

Abstract Since Tiny machine learning (TinyML) is a quickly evolving subject, it is crucial that
internet of things (IoT) devices be able to communicate with one another for the sake of …

Deep neural networks with state-of-the-art (SOTA) performance on medical imaging
datasets such as Medical Resonance Imaging (MRI), Ultrasound (US), and Computed …

In recent years, Artificial Intelligence (AI) has played an important role in our daily life.
Especially convolutional neural network (CNN) in medical image analysis has been getting …

While Deep Reinforcement Learning has been widely researched in medical imaging, the
training and deployment of these models usually require powerful GPUs. Since imaging …

The successful application of deep learning approaches in remote sensing image
classification requires large hyperspectral image (HSI) datasets to learn discriminative …

Prior attacks on graph neural networks have focused on graph poisoning and evasion,
neglecting the network's weights and biases. For convolutional neural networks, however …