The dynamicity of real-world systems poses a significant challenge to deployed predictive
machine learning (ML) models. Changes in the system on which the ML model has been …

Oil and gas industries are facing several challenges and issues in data processing and
handling. Large amount of data bank is generated with various techniques and processes …

With synergies among multiple energy sectors, integrated energy systems (IESs) have been
recognized lately as an effective approach to accommodate large-scale renewables and …

The rapid development of Artificial Intelligence (AI) technology has enabled the deployment
of various systems based on it. However, many current AI systems are found vulnerable to …

In low-and middle-income countries (LMICs), AI has been promoted as a potential means of
strengthening healthcare systems by a growing number of publications. We aimed to …

Electricity load forecasting has been attracting research and industry attention because of its
importance for energy management, infrastructure planning, and budgeting. In recent years …

The so-called “smartization” of manufacturing industries has been conceived as the fourth
industrial revolution or Industry 4.0, a paradigm shift propelled by the upsurge and …

When predictions support decisions they may influence the outcome they aim to predict. We
call such predictions performative; the prediction influences the target. Performativity is a …

In many applications of information systems learning algorithms have to act in dynamic
environments where data are collected in the form of transient data streams. Compared to …

Background Applications of artificial intelligence (AI) in health care have garnered much
attention in recent years, but the implementation issues posed by AI have not been …