Active learning (AL) attempts to maximize a model's performance gain while annotating the
fewest samples possible. Deep learning (DL) is greedy for data and requires a large amount …

Twitter is an active communication channel for the spreading of updated information in
emergency situations. Retrieving specific information related to infrastructure damage offers …

The ease of access to the internet has sparked a worldwide interest in SM in recent years.
The possibilities for the use of SM as a potential source in improving the management of …

Earthquakes can cause disastrous casualties and infinite economic loss worldwide. People
have tried various means to ease earthquake disasters. Machine learning (ML) is one …

Identifying tweets related to infrastructure damage during a crisis event is an important
problem. However, the unavailability of labeled data during the early stages of a crisis event …

With the increasing use of Twitter for encouraging users to instigate violent behavior with
hate and racial content, it becomes necessary to investigate the uniqueness in the dynamics …

Twitter is perhaps the social media more amenable for research. It requires only a few steps
to obtain information, and there are plenty of libraries that can help in this regard …

Supervised machine learning-based models are generally used for classifying tweets
related to crisis. A labelled tweet dataset is a major requirement for training the models …

ABSTRACT A surge of instant local information on social media serves as the first alarming
tone of need, supports, damage information, etc. during crisis. Identifying such signals …

Premature ventricular contractions (PVCs) are one of the most common cardiovascular
diseases with high risk to a large population of patients. It has been shown that supervised …