Supervised learning algorithms have shown limited use in the field of anomaly detection due
to the unpredictability and difficulty in acquiring abnormal samples. In recent years …

Since canonical machine learning algorithms assume that the dataset has equal number of
samples in each class, binary classification became a very challenging task to discriminate …

In pursuit of the perfect supervised NLP classifier, razor thin margins and low-resource test
sets can make modeling decisions difficult. Popular metrics such as Accuracy, Precision …

Android applications are developing rapidly across the mobile ecosystem, but Android
malware is also emerging in an endless stream. Many researchers have studied the …

In recent years, we have witnessed a rise in fake news, ie, provably false pieces of
information created with the intention of deception. The dissemination of this type of news …

In this study, we systematically investigate the impact of class imbalance on classification
performance of convolutional neural networks (CNNs) and compare frequently used …

Despite inherent ill-definition, anomaly detection is a research endeavour of great interest
within machine learning and visual scene understanding alike. Most commonly, anomaly …

In the recent pandemic, accurate and rapid testing of patients remained a critical task in the
diagnosis and control of COVID-19 disease spread in the healthcare industry. Because of …

Advances in nucleic acid sequencing technology have enabled expansion of our ability to
profile microbial diversity. These large datasets of taxonomic and functional diversity are key …

Label-free cell analysis is essential to personalized genomics, cancer diagnostics, and drug
development as it avoids adverse effects of staining reagents on cellular viability and cell …