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Multi-Modal Detection of Alzheimer's Disease from Speech and Text
Reliable detection of the prodromal stages of Alzheimer's disease (AD) remains difficult even today because, unlike other neurocognitive impairments, there is no definitive diagnosis of AD in vivo.
A Deep Learning Framework for COVID Outbreak Prediction
In this paper, we propose a comparative analysis of deep learning models to forecast the COVID-19 outbreak as an alternative to statistical models.
Self-Attention Dense Depth Estimation Network for Unrectified Video Sequences
The dense depth estimation of a 3D scene has numerous applications, mainly in robotics and surveillance.
Monocular Depth Estimators: Vulnerabilities and Attacks
It has been shown in recent works that these deep neural networks are highly vulnerable to adversarial samples for tasks like classification, detection and segmentation.
Intelligent Residential Energy Management System using Deep Reinforcement Learning
The rising demand for electricity and its essential nature in today's world calls for intelligent home energy management (HEM) systems that can reduce energy usage.
