Change points are abrupt variations in time series data. Such abrupt changes may represent
transitions that occur between states. Detection of change points is useful in modelling and …

The ubiquity of smartphones and the growth of computing resources, such as connectivity,
processing, portability, and power of sensing, have greatly changed people's lives. Today …

Abstract The consumer Internet of Things (IoT) has the potential to revolutionize consumer
experience. Because consumers can actively interact with smart objects, the traditional …

Getting a good feature representation of data is paramount for Human Activity Recognition
(HAR) using wearable sensors. An increasing number of feature learning approaches—in …

A smart home, which is an extension of a traditional home, is equipped with ubiquitous
sensors embedded in consumer appliances, connected via sensing technologies such as …

Recognition of human activities by analyzing smartphone data which is being collected via
accelerometer and gyroscopic sensors has been a critical area of research and it has been …

The study of natural and human-made processes often results in long sequences of
temporally-ordered values, aka time series (TS). Such processes often consist of multiple …

Identifying accident patterns is one of the most vital research foci of driving analysis.
Environmental or safety applications and the growing area of fleet management all benefit …

Logistic regression is by far the most widely used classifier in real-world applications. In this
paper, we benchmark the state-of-the-art active learning methods for logistic regression and …

Smart home technologies offer potential benefits for assisting clinicians by automating
health monitoring and well-being assessment. In this paper, we examine the actual benefits …