Time series forecasting is fundamental for various use cases in different domains such as
energy systems and economics. Creating a forecasting model for a specific use case …

Medical datasets are usually imbalanced, where negative cases severely outnumber
positive cases. Therefore, it is essential to deal with this data skew problem when training …

With the widespread use of closed-circuit television (CCTV) surveillance systems in public
areas, crowd anomaly detection has become an increasingly critical aspect of the intelligent …

Electrical power grids are vulnerable to cyber attacks, as seen in Ukraine in 2015 and 2016.
However, existing attack detection methods are limited. Most of them are based on power …

Cyber security has become increasingly challenging due to the proliferation of the Internet of
things (IoT), where a massive number of tiny, smart devices push trillion bytes of data to the …

Lack of education is a major concern in underdeveloped countries because it leads to poor
human and economic development. The level of education in public institutions varies …

This paper delves into the transformative role of machine learning (ML) techniques in
revolutionizing the security of electric and flying vehicles (EnFVs). By exploring key domains …

Vehicular Ad-hoc network (VANET) is an imminent technology having both exciting
prospects and substantial challenges, especially in terms of security. Due to its distributed …

A decentralized power grid is a modern system that implements demand response without
requiring major infrastructure changes. In decentralization, the consumers regulate their …

Predictive maintenance in the car industry is an active field of research for machine learning
and anomaly detection. The capability of cars to produce time series data from sensors is …