Context: Advancements in machine learning (ML) lead to a shift from the traditional view of
software development, where algorithms are hard-coded by humans, to ML systems …

Context: In traditional software systems, Requirements Engineering (RE) activities are well-
established and researched. However, building Artificial Intelligence (AI) based software …

Recent efforts have incorporated large language models (LLMs) with external resources (eg,
the Internet) or internal control flows (eg, prompt chaining) for tasks requiring grounding or …

AI-based systems are software systems with functionalities enabled by at least one AI
component (eg, for image-, speech-recognition, and autonomous driving). AI-based systems …

The introduction of machine learning (ML) components in software projects has created the
need for software engineers to collaborate with data scientists and other specialists. While …

Over the past six decades, the computing systems field has experienced significant
transformations, profoundly impacting society with transformational developments, such as …

With the projected global surge in hydrogen demand, driven by increasing applications and
the imperative for low-emission hydrogen, the integration of machine learning (ML) across …

Artificial intelligence (AI) relies on data and algorithms. State-of-the-art (SOTA) AI smart
algorithms have been developed to improve the performance of AI-oriented structures …

Data science projects represent a greater challenge than software engineering for
organizations pursuing their adoption. The diverse stakeholders involved emphasize the …

This comprehensive overview explores the integration of machine learning (ML) in data
warehousing, focusing on optimization challenges, methodologies, results, and future …