Social media is a more common and powerful platform for communication to share views
about any topic or article, which consequently leads to unstructured toxic, and hateful …

In 2020 the world is facing unprecedented challenges due to COVID‐19. To address these
challenges, many digital tools are being explored and developed to contain the spread of …

Motivation In addition to substitution frequency data from protein sequence alignments,
many state-of-the-art methods for contact prediction rely on additional sources of …

In this paper, we demonstrate that image reconstruction can be expressed in terms of neural
networks. We show that filtered back-projection can be mapped identically onto a deep …

Deep convolutional neural network (DCNN) is an influential tool for solving various
problems in machine learning and computer vision. Recurrent connectivity is a very …

Machine learning methods incorporating deep neural networks have been the subject of
recent proposals for new hadronic resonance taggers. These methods require training on a …

Leading neuroimaging studies have pushed 3T MRI acquisition resolutions below 1.0 mm
for improved structure definition and morphometry. Yet, only few, time-intensive automated …

Purpose A supervised deep learning (DL) approach for frequency and phase correction
(FPC) of MRS data recently showed encouraging results, but obtaining transients with labels …

Numerous technical disciplines examine the information with a non-Euclidean dimension as
its fundamental structural basis. This geometrical information is common in several …

Microscopic histology image analysis is a cornerstone in early detection of breast cancer.
However these images are very large and manual analysis is error prone and very time …