Technical systems are becoming increasingly complex due to the increasing number of
components, functions, and involvement of different disciplines. In this regard, model-driven …

Most machine learning-based methods predict outcomes rather than understanding
causality. Machine learning methods have been proved to be efficient in finding correlations …

Abstract Conventional wisdom on Model-Driven Engineering suggests that metamodels are
crucial elements for modeling environments consisting of graphical editors, transformations …

Objective: This study aims to investigate the existing body of knowledge in the field of Model-
Driven Engineering MDE in support of AI (MDE4AI) to sharpen future research further and …

A growing demand is witnessed in both industry and academia for employing Deep
Learning (DL) in various domains to solve real-world problems. Deep reinforcement …

Automated Machine Learning is an area of research that has gained lots of research in the
past few years. To build a high qualitymodel for Machine learning we need technical experts …

Datasets are essential for training and evaluating machine learning (ML) models. However,
they are also at the root of many undesirable model behaviors, such as biased predictions …

This study introduces a novel framework for optimizing energy efficiency and computational
load in safety-critical robotic systems operating in nonlinear domains. Leveraging Graph …

This paper presents a novel meta learning framework for feature selection (FS) based on
fuzzy similarity. The proposed method aims to recommend the best FS method from four …

Reinforcement learning (RL) is an important class of machine learning techniques, in which
intelligent agents optimize their behavior by observing and evaluating the outcomes of their …