Quite frequent calls to emergency telephone services with false messages form a real
problem. They mainly come from unreliable speakers or are subject of multiple mutually …

This paper presents the use of relations between features, which describe a caller for
automatic recognition of abnormal cases in emergency telephone call systems. The …

This paper presents a series of experiments on the classification of emergency phone
conversation records using artificial neural networks (ANNs). Input data which were …

In this study, we present the process of designing machine learning models for the detection
of call center agent malpractices. Based on the features extracted from audio recordings of a …

The continuous expansion of cyberattacks and their high degree of professionalization
generates a context of extreme risk in all sectors of society, both public and private. In this …

Abnormal calls have the characteristics of multiple occurrence and concealment. It has
become the focus of the industry to identify abnormal calls by using machine learning …

Emergency call centers are often required to properly assess and prioritise emergency
situations pre-intervention, in order to provide the required assistance to the callers …

Communication over the phone has always been one of the most prone communication
channels as it just requires a phone number to get connected and audio to communicate …

In recent years, fraudulent methods are constantly updated, criminal information is more
hidden, and there is a problem of subjectivity of artificial feature design in traditional model …

The analysis of informal texts is essential in text mining applications, such as the
classification ones. The division of classes into informal and non-structured text is necessary …