Energy and security are major challenges in a wireless sensor network, and they work
oppositely. As security complexity increases, battery drain will increase. Due to the limited …

Many daily applications are generating massive amount of data in the form of stream at an
ever higher speed, such as medical data, clicking stream, internet record and banking …

Multi-label learning has garnered much research interest due to its wide range of real-world
applications. Many multi-label learning methods have been proposed; however, few have …

Power quality events are usually associated with more than one disturbance and their
recognition is typically based on multilabel learning. In this study, we propose a new method …

With the increasing presence of online social networks (OSN), there is a growing interest in
accurately predicting user behaviors based on the data collected from OSN. Unlike …

The proximate analysis of coal is the umbrella term for the six indexes that include the
moisture, ash, volatile matter, fixed carbon, and sulphur contents and the heating value …

Learning from imbalanced data is a challenging task in the fields of machine learning and
data mining. As an effective and efficient solution, cost-sensitive learning has been widely …

The ensemble learning method is considered a meaningful yet challenging task. To
enhance the performance of binary classification and predictive analysis, this paper …

The indoor location-based services are high demand in the market, and precise location
estimation in multi-floor buildings has received significant attention in recent years. In these …

Multi-label learning is a popular area of machine learning research as it is widely applicable
to many real-world scenarios. In comparison with traditional binary and multi-classification …