Sensor data quality plays a vital role in Internet of Things (IoT) applications as they are
rendered useless if the data quality is bad. This systematic review aims to provide an …

Ensemble classifier refers to a group of individual classifiers that are cooperatively trained
on data set in a supervised classification problem. In this paper we present a review of …

Imbalanced problem occurs when the size of the positive class is much smaller than that of
the negative one. Positive class usually refers to the main interest of the classification task …

With the growth of IoT applications, sensor data quality has become increasingly important
to ensure the success of these data-driven applications. Sensor data riddled with errors are …

Correlation analysis is one of the fundamental mathematical tools for identifying
dependence between classes. However, the accuracy of the analysis could be jeopardized …

Time-series features are the characteristics of data periodically collected over time. The
calculation of time-series features helps in understanding the underlying patterns and …

Due to increasing traffic congestion, travel modeling has gained importance in the
development of transportion mode detection (TMD) strategies over the past decade …

With the construction and deployment of seafloor observatories around the world, massive
amounts of oceanographic measurement data were gathered and transmitted to data …

Smart sensors are an integral part of the Fourth Industrial Revolution and are widely used to
add safety measures to human–robot interaction applications. With the advancement of …

Analytics based on sensor data is of increasing interest for a variety of industries. In the
context of industrial manufacturing, the goal is very often to reduce downtime through …