In the last years, deep learning has dramatically improved the performances in a variety of
medical image analysis applications. Among different types of deep learning models …

In covering-based rough set theory, non-incremental approaches are time-consuming for
performing knowledge reduction of dynamic covering decision information systems when …

We present a modelling framework for the investigation of supervised learning in non-
stationary environments. Specifically, we model two example types of learning systems …

Today, many data are not any longer static but occur as dynamic data streams with high
velocity, variability and volume. This leads to new challenges to be addressed by novel or …

Background: AI-based solutions for automated Gleason grading have been developed to
assist pathologists to make rapid and quantitative assessments, but the generalization …

In many segmentation processes, we assign new cases according to a model that was built
on the basis of past cases. As long as the new cases are “similar enough” to the past cases …

Travel delays and bus overcrowding are some of the daily dissatisfactions of public
transportation users. These problems may be caused by bus bunching, an event that occurs …

The Internet of Medical Things (IoMT), combined with interconnected wearable devices and
medical-grade sensors, can play an essential role in healthcare evolution. By exploiting the …

Parallel incremental learning is an effective approach for rapidly processing large scale data
streams, where parallel and incremental learning are often treated as two separate …

В процессе создания и эксплуатации летательных аппаратов различных видов
значительное место занимает решение таких классов задач, как анализ поведения …