Nowadays, many real-world applications of our daily life generate massive volume of
streaming data at a higher speed than ever before, to name a few, Web clicking data …

Improving survey specifications are causing an exponential rise in pulsar candidate
numbers and data volumes. We study the candidate filters used to mitigate these problems …

We evaluate the performance of four different machine learning (ML) algorithms: an Artificial
Neural Network Multi-Layer Perceptron (ANN MLP), Adaboost, Gradient Boosting Classifier …

Featured Application Nowadays, many applications and disciplines work on the basis of
stream data. Common examples are the IoT sector (eg, sensor data analysis), or video …

Classifiers trained on data sets possessing an imbalanced class distribution are known to
exhibit poor generalisation performance. This is known as the imbalanced learning problem …

It is an active topic to investigate the schemes based on machine learning (ML) methods for
detecting pulsars as the data volume growing exponentially in modern surveys. To improve …

Searches for pulsars during the past fifty years, have been characterised by two problems
making their discovery difficult: i) an increasing volume of data to be searched, and ii) an …

ABSTRACT A typical characteristic of the pulsar candidate classification task is the class
imbalance between true pulsars and false candidates. This imbalance has negative effects …

The survival rate for sudden cardiac arrest (SCA) is low, and patients with long-term risks of
SCA are not adequately alerted. Understanding SCA's characteristics will be key to …

Pulsar candidate sifting is an essential process for discovering new pulsars. It aims to search
for the most promising pulsar candidates from an all-sky survey, such as the High Time …