In the current age of the Fourth Industrial Revolution (4 IR or Industry 4.0), the digital world
has a wealth of data, such as Internet of Things (IoT) data, cybersecurity data, mobile data …

Artificial intelligence (AI) techniques have grown rapidly in recent years in the context of
computing with smart mobile phones that typically allows the devices to function in an …

Due to the increasing popularity of recent advanced features and context-awareness in
smart mobile phones, the contextual data relevant to users' diverse activities with their …

Huge amounts of data are circulating in the digital world in the era of the Industry 5.0
revolution. Machine learning is experiencing success in several sectors such as intelligent …

This paper formulates the problem of building a context-aware predictive model based on
user diverse behavioral activities with smartphones. In the area of machine learning and …

Smartphones are considered as one of the most essential and highly personal devices of
individuals in our current world. Due to the popularity of context-aware technology and …

This paper formulates the problem of a rule-based machine learning method to discover the
behavioral rules of individual smartphone users to provide context-aware intelligent …

Real-life mobile phone data may contain noisy instances, which is a fundamental issue for
building a prediction model with many potential negative consequences. The complexity of …

Mobile phones can record individual's daily behavioral data as a time-series. In this paper,
we present an effective time-series segmentation technique that extracts optimal time …

Today's information and communication devices provide always-on connectivity, instant
access to an endless repository of information, and represent the most direct point of contact …