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 …

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 …

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 …

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

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 …

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 …

Interpretability has recently gained attention in the field of machine learning, for it is crucial
when it comes to high-stakes decisions or troubleshooting. This abstract concept is hard to …

Unmet need for contraceptives is a public health issue globally that affects maternal and
child health. Reducing unmet need reduces the risk of abortion or childbearing by …

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

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