The classification of electrocardiogram (ECG) plays a crucial role in th...
The presence of domain shift in medical imaging is a common issue, which...
Physiological Signals are the most reliable form of signals for emotion
...
Domain Adaptation is a technique to address the lack of massive amounts ...
Interactive segmentation has recently attracted attention for specialize...
Neural networks often require large amounts of expert annotated data to
...
Electrocardiogram (ECG) is an authoritative source to diagnose and count...
ECG is an attractive option to assess stress in serious Virtual Reality ...
In this paper, we present a novel Image Fusion Model (IFM) for ECG heart...
Convolutional Neural Networks (CNNs) are successful deep learning models...
Over the last few decades, Lung Ultrasound (LUS) has been increasingly u...
Despite the continued successes of computationally efficient deep neural...
Reconstructing an indoor scene and generating a layout/floor plan in 3D ...
Traditionally, convolutional neural networks need large amounts of data
...
Convolutional Neural Network (CNN) provides leverage to extract and fuse...
COVID-19, due to its accelerated spread has brought in the need to use
a...
One of the major reasons for misclassification of multiplex actions duri...
This paper attempts at improving the accuracy of Human Action Recognitio...
Screening mammograms is the gold standard for detecting breast cancer ea...
Facial expressions of emotion are a major channel in our daily
communica...
Stress analysis and assessment of affective states of mind using ECG as ...
In current deep network architectures, deeper layers in networks tend to...
Multimodal fusion frameworks for Human Action Recognition (HAR) using de...
Transfer Function (TF) generation is a fundamental problem in Direct Vol...
Detection of Alzheimer's Disease (AD) from neuroimaging data such as MRI...
We propose a real-time human activity analysis system, where a user's
ac...