Large Language Models (LLMs) are revolutionizing the field of code development by
leveraging their deep understanding of code patterns, syntax, and semantics to assist …

Graph Neural Networks (GNNs) have become a powerful tool in order to learn from graph-
structured data. Their ability to capture complex relationships and dependencies within …

In this paper, the H∞ synchronization problem of switching inertial neural networks with
mixed time-varying delays is discussed. A parameterized system solution-based direct …

Microservices architecture is quickly replacing monolithic and multi-tier architectures as the
implementation choice for large-scale web applications as it allows independent …

A deep geological repository for radioactive waste, such as Andra's Cigéo project, requires
long-term (persistent) monitoring. To achieve this goal, data from a network of sensors are …

Multi-access Edge Computing (MEC) is an emerging and promising computing paradigm
that distributes computational resources closer to users at the network edge, effectively …

The shift from monolithic services to microservices brings modularity and elasticity, but
detecting faults and anomalies is challenging due to diverse data and evolving technology …

In past couple of years, cloud computing has emerged as one of the fastest growing
technologies across the globe. In order to keep pace with the advancements taking place in …

Structural health monitoring (SHM) systems currently utilize a combination of low-cost, low-
energy sensors and processing units to monitor the conditions of target facilities. However …

This paper proposes a framework for time series generation built to investigate anomaly
detection in cloud microservices. In the field of cloud computing, ensuring the reliability of …