Implementing machine learning technologies in manufacturing environment relies heavily on human expertise in terms of domain and machine learning knowledge. Yet, the required machine learning knowledge is often not available in manufacturing companies. A possible solution to overcome this competence gap and let domain experts with limited machine learning programming skills build viable applications are digital assistance systems that support the implementation. At the present, there is no comprehensive overview over corresponding assistance systems. Thus, within this study a systematic literature review based on the PRISMA-P process was conducted. Twenty-nine papers were identified and analyzed in depth regarding machine learning use case, required resources and research outlook. Six key findings as well as requirements for future developments are derived from the investigation. As such, the existing assistance systems basically focus on technical aspects whereas the integration of the users as well as validation in industrial environments lack behind. Future assistance systems should put more emphasis on the users and integrate them both in development and validation.
Background: Customer churn significantly impacts business revenues. Machine Learning (ML) and Deep Learning (DL) methods are increasingly adopted to predict churn, yet a systematic synthesis of recent advancements is lacking. Objectives: This systematic review evaluates ML and DL…
Modern artificial intelligence methods are increasingly used in power systems for renewable energy generation and electricity load forecasting. However, the limited interpretability of complex machine learning and deep learning models constrains their adoption in critical energy…
The rise of machine-learning applications in domains with critical end-user impact has led to a growing concern about the fairness of learned models, with the goal of avoiding biases that negatively impact specific demographic groups. Most existing bias-mitigation strategies adap…
Human Pose Estimation (HPE) is the task that aims to predict the location of human joints from images and videos. This task is used in many applications, such as sports analysis and surveillance systems. Recently, several studies have embraced deep learning to enhance the perform…
Quantum computing, with its foundational principles of superposition and entanglement, has the potential to provide significant quantum advantages, addressing challenges that classical computing may struggle to overcome. As data generation continues to grow exponentially and tech…
Alzheimer’s disease (AD) is the most prevalent neurodegenerative disorder worldwide, affecting approximately 56.9 million people in 2021 and projected to reach 152 million by 2050. Its defining pathological features, amyloid-beta plaques and neurofibrillary tangles, accumulate fo…