4. Fault Detection Model Development using AI Faults using sensor data can be detected by artificial intelligence techniques such as machine learning and neural networks. These techniques involve the ...

Background Coronary artery disease (CAD) is linked to an increased risk of mild cognitive impairment (MCI). Effective and ...

The system provides a detailed report on classification and similarity between two invoices and has a drag and drop interface interface where user can edit their predictions and constantly improves ...

Credit: University of Toronto Formerly working at Google and often referred to as the ‘Godfather of Deep Learning,’ Dr. Geoffrey Hinton has made fundamental and transformative contributions to the ...

Tianjin Key Laboratory of Civil Structure Protection and Reinforcement, Tianjin Chengjian University, Tianjin 300384, P. R. China Tianjin Key Laboratory of Civil Structure Protection and Reinforcement ...

Abstract: We introduce a deep learning-based hybrid beamforming (HBF) strategy for millimeter-wave transmission systems, specifically addressing the challenges posed by phase noise of local ...

Despite the widespread success of neural networks, their susceptibility to adversarial examples remains a significant challenge. Adversarial training (AT) has emerged as an effective approach to ...

Merging with Fourier transforms and pupil aperture scanning causes difficulty in reconstructing high-resolution images by the commonly used deep neural ... network can provide better reconstruction ...

Deep neural networks are a type of deep learning, which is a type of machine learning. Deep neural networks are used in a variety of applications, including speech recognition, computer vision, and ...

This update follows the completion of a deep-penetrating TITAN geophysical survey and provides an overview of the Phase 2 drilling ... Document Analysis and Retrieval (SEDAR+) at www.sedarplus.ca ...