Non-Terrestrial Networks for Energy-Efficient Connectivity of Remote IoT Devices in the 6G Era: A Survey.

The Internet of Things (IoT) is gaining popularity and market share, driven by its ability to connect devices and systems that were previously siloed, enabling new applications and services in a cost-efficient manner. Thus, the IoT fuels societal transformation and enables groundbreaking innovations like autonomous transport, robotic assistance, and remote healthcare solutions. However, when considering the Internet of Remote Things (IoRT), which refers to the expansion of IoT in remote and geographically isolated areas where neither terrestrial nor cellular networks are available, internet connectivity becomes a challenging issue.

Socially Aware Heterogeneous Wireless Networks.

The development of smart cities has been the epicentre of many researchers' efforts during the past decade. One of the key requirements for smart city networks is mobility and this is the reason stable, reliable and high-quality wireless communications are needed in order to connect people and devices. Most research efforts so far, have used different kinds of wireless and sensor networks, making interoperability rather difficult to accomplish in smart cities.

The Wisconsin breast cancer problem: Diagnosis and TTR/DFS time prognosis using probabilistic and generalised regression information classifiers.

This study addresses the breast cancer diagnosis and prognosis problem by employing two neural network architectures with the Wisconsin diagnostic and prognostic breast cancer (WDBC/WPBC) datasets. A probabilistic approach is dedicated to solve the diagnosis problem, detecting malignancy among cases (instances) as derived from fine needle aspirate (FNA) tests, while the second architecture estimates the time interval that possibly contains the right endpoint of disease-free survival (DFS) of the patient. The accuracy of the neural classifiers reaches nearly 98% for the diagnosis and 93% for the prognosis problem, while the prognostic recurrence predictions were evaluated using survival analysis through the Kaplan-Meier approximation method.