Deep learning models have revolutionized numerous fields, yet their decision-making
processes often remain opaque, earning them the characterization of “black-box” models …

This systematic literature review employs the Preferred Reporting Items for Systematic
Reviews and Meta-Analyses (PRISMA) methodology to investigate recent applications of …

In recent years, black-box machine learning approaches have become a dominant modeling
paradigm for knowledge extraction in Remote Sensing. Despite the potential benefits of …

Digital twins (DT) are becoming popular methods to leverage auxiliary information for real-
time support. Digital Twins are closely related to the Dynamic Data driven Applications …

In recent years, black-box machine learning approaches have become a dominant modeling
paradigm for knowledge extraction in remote sensing. Despite the potential benefits of …

In radar electronic countermeasures, as the difference between jamming and targets
continues to decrease, traditional methods that are implemented based on classical features …

Many techniques have been developed for sensor and information fusion, machine and
deep learning, as well as data and machine analytics. Currently, many groups are exploring …

This paper considers the problem of aerial view object classification using co-registered
electro-optical (EO) and synthetic aperture radar (SAR) images. Both EO and SAR sensors …

There are many examples, methods, and processes showing the importance of sensor, data,
and information fusion. However, there is a need to determine the value added of …

Within the recent years, the concept of Digital Twins (DT) emerged to support digital
engineering physical-systems design in the processing and analysis of device components …