The COVID-19 pandemic has impelled the majority of schools and universities around the
world to switch to remote teaching. One of the greatest challenges in online education is …

Imbalanced response variable distribution is a common occurrence in data science. In fields
such as fraud detection, medical diagnostics, system intrusion detection and many others …

Identifying the relevant set of features in a dataset is an important part of data analytics.
Discarding significant variables or kee** irrelevant variables has significant effects on the …

Imbalanced class distribution is a common problem in a number of fields including medical
diagnostics, fraud detection, and others. It causes bias in classification algorithms leading to …

In this paper, we compare various approaches to stock price prediction using neural
networks. We analyze the performance fully connected, convolutional, and recurrent …

To achieve high-levels of autonomy, modern robots require the ability to detect and recover
from anomalies and failures with minimal human supervision. Multi-modal sensor signals …

Machine learning (ML) models in production pipelines are frequently retrained on the latest
partitions of large, continually-growing datasets. Due to engineering bugs, partitions in such …

Outlier detection (outlier and anomaly are used interchangeably in this review) in non-
independent and identically distributed (non-IID) data refers to identifying unusual or …

In the internet era, network-based services and connected devices are growing with many
users, thus it became an increase in the number of cyberattacks. Distributed Denial of …

Stock price prediction has been the focus of a large amount of research but an acceptable
solution has so far escaped academics. Recent advances in deep learning have motivated …