Globally, there is a huge unmet need for effective treatments for neurodegenerative
diseases. The complexity of the molecular mechanisms underlying neuronal degeneration …

The leading approaches in Machine Learning are notoriously data-hungry. Unfortunately,
many application domains do not have access to big data because acquiring data involves a …

Recently, various sophisticated methods, including machine learning and artificial
intelligence, have been employed to examine health-related data. Medical professionals are …

Data currently generated in the field of nutrition are becoming increasingly complex and
high-dimensional, bringing with them new methods of data analysis. The characteristics of …

Graph clustering, which aims to divide nodes in the graph into several distinct clusters, is a
fundamental yet challenging task. Benefiting from the powerful representation capability of …

Human action recognition (HAR) has gained significant attention recently as it can be
adopted for a smart surveillance system in Multimedia. However, HAR is a challenging task …

Software Vulnerabilities (SVs) are increasing in complexity and scale, posing great security
risks to many software systems. Given the limited resources in practice, SV assessment and …

Background and Purpose: Stroke is reported as a consequence of severe acute respiratory
syndrome coronavirus-2 (SARS-CoV-2) infection in several reports. However, data are …

In this paper, an inventory of the landslide that occurred in Karahacılı at the end of 2019 was
created and the pre-landslide conditions of the region were evaluated with traditional …

As one of the earth's key ecosystems, rivers have been intensively studied and modelled
through the application of machine learning (ML). With the amount of large data available …