This article introduces LogicLSTM, a hybrid neuro-symbolic model obtained by logically
guiding a pretrained Long Short-Term Memory (LSTM) network with the support of a …

Numerous high-rise buildings in megacities create complex interference effects, significantly
impacting aerodynamic forces and leading to severe wind-induced disasters. While current …

This paper presents a novel anomaly detection methodology termed Statistical Aggregated
Anomaly Detection (SAAD). The SAAD approach integrates advanced statistical techniques …

A hot topic of Artificial Intelligence right now is image generation from prompts. DALL-E 2 is
one of the biggest names in this domain, as it allows people to create images from simple …

This paper provides a thorough analysis of the development of artificial intelligence (AI) in
the context of Industry 4.0 and the soon-to-be Industry 5.0. Important conclusions come from …

Detecting novel product defects whose classes have not been seen at all during training
time, is an important aspect of practical automated visual inspection in manufacturing …

In this paper, a deep neural network approach and a neuro-symbolic one are proposed for
classification and regression. The neuro-symbolic predictive models based on Logic Tensor …

This study introduces a novel methodology for finite element (FE) model updating using
displacement influence lines and a deep learning approach. Traditional structural health …

A novel hierarchical task framework is proposed for multi-damage quantification on plate
structures, and this framework is primarily divided into a damage quantity identification layer …

Being able to constantly retrain a model with new data, while not retaining the old one, and
still being able to successfully classify both is an interesting concept that has seen a surge in …