Context: Software defect prediction is crucial for prioritising quality assurance tasks,
however, there are still limitations to the use of defect models. For example, the outputs often …

Although the effectiveness of machine learning (ML) for machine diagnosis has been widely
established, the interpretation of the diagnosis outcomes is still an open issue. Machine …

This paper presents a comprehensive framework for expert-augmented supervised feature
selection, addressing pre-processing, in-processing, and post-processing aspects of …

The proliferation of computational advertising (CA) and other technological developments in
artificial intelligence have greatly expanded the types of data used in advertising research …

Today's algorithms already reached or even surpassed the task performance of humans in
various domains. Especially, Artificial Intelligence (AI) plays a central role for the interaction …

In the smart grid paradigm, precise electrical load forecasting (ELF) offers significant
advantages for enhancing grid reliability and informing energy planning decisions …

Type 1 diabetes mellitus (T1DM) is characterized by insulin deficiency and blood sugar
control issues. The state-of-the-art solution is the artificial pancreas (AP), which integrates …

Expert systems are being extensively used to make critical decisions involving emotional
analysis in affective computing. The evolution of deep learning algorithms has improved the …

Over the past decade, there has been a growing surge of interest in leveraging artificial
intelligence and machine learning models to address real‐world challenges within the field …

Purpose Machine learning (ML) techniques are increasingly important in enabling business-
to-business (B2B) companies to offer personalized services to business customers. On the …