Graph neural networks (GNN) have shown outstanding applications in fields where data is
essentially represented as graphs (eg, chemistry, biology, and recommendation systems). In …

The last few years have seen an increasing wave of attacks with serious economic and
privacy damages, which evinces the need for accurate Network Intrusion Detection Systems …

The goal of the state estimation (SE) algorithm is to estimate complex bus voltages as state
variables based on the available set of measurements in the power system. Because phasor …

Specific emitter identification (SEI) is significant in military communication scenarios,
cognitive radio, and self-organized networks. However, these methods only consider the …

The latest advances in Deeper Reinforcement Learning (DRL) have completely changed
how decision-making and automatic control issues are solved. The study community …

Telecommunication Networks (TNs) have become the most important infrastructure for data
communications over the last century. Operations and maintenance (O&M) is extremely …

The reinforcement learning-based routing, modulation, and spectrum assignment has been
regarded as an emerging paradigm for resource allocation in the elastic optical networks …

Nonlinear state estimation (SE), with the goal of estimating complex bus voltages based on
all types of measurements available in the power system, is usually solved using the …

Graph neural networks (GNN) have recently been proposed as a technique for accurate and
cost-efficient network modeling. As an example, the GNN-based model RouteNet has shown …

This PhD thesis thoroughly examines the utilization of deep learning techniques as a means
to advance the algorithms employed in the monitoring and optimization of electric power …