Machines are rapidly gaining the ability to perceive, interpret and interact with the visual world in ways that were once ...

People have worried that computers will take their jobs for at least a decade, but those fears have felt more realistic than ...

Researchers have unveiled the All-Topographic Neural Network (All-TNN), a new AI vision model type that mimics human brain ...

Further, three deep learning architectures viz., Convolutional Neural Network (CNN), Deep features from pretrained network classified using Support Vector Machine (SVM) classifier and Transfer ...

This paper describes our new deep learning system based on a comparison between GRU and CNN. Initially we start with the first system which uses Convolutional Neural Network (CNN) which we will ...

The rise in manipulated visual evidence poses a significant threat to judicial systems, potentially leading to incorrect judgments, wrongful convictions, or acquittals of guilty parties. Such outcomes ...

Benefiting from a large amount of unallocated bandwidth, millimeter-wave (mmWave) communications have been regarded as one of the most promising technologies. To overcome high pathloss of mmWave ...

Applications like disaster management, urban planning, and environmental monitoring rely on satellite image categorization. This project develops a machine learning pipeline using MobileNetV2, a CNN ...

This paper conducts a comprehensive analysis of Convolutional Neural Network (CNN)-based deep learning models, focusing on the impact of transfer learning on their accuracy. Multiple tailored CNN ...

This research gathers participants“ electroencephalographic (EEG) data to build classifiers that can decode users” mental states. Although deep learning models can incorporate domain-dependent feature ...