To make the test automation process go faster, we needed to harness innovation. So how do we do this? So the first strategy was to figure out how we could leverage AI to speed up the test development cycle. The second strategy involved developing a test automation care package.

This paper presents a cloud-edge digital twin frame- work designed to enhance battery lifecycle management within electric vehicles, contributing to sustainable transportation and advanced battery system engineering. The architecture integrates a static state-of-health (SOH) model trained offline with a dynamically retrained state-of-charge (SOC) model updated peri- odically via cloud-based machine learning.

This paper introduces a data-driven anomaly detection framework designed to enhance the safety and reliability of lithium-ion battery packs deployed in large-scale electric transport systems. Leveraging principal component analysis (PCA) and cumulative sum (CUSUM) control charts, the method monitors mean-based residuals of voltage and temperature across cell groups to detect early-stage faults such as internal short circuits, sensor failures, and thermal irregularities.

The rapid development arena of Artificial Intelligence (AI), especially from Large Language Models (LLMs), has prompted a drastic change in the way data engineering is practised. This paper provides an in-depth overview of AI-powdered data engineering, and discusses how modern AI techniques are re-shaping the conventional development, profiling/optimisation, and maintenance process of the data pipeline. We then study recent paradigms in so-called pipeline automation with LLMs, the growing use of machine learning to optimise database query execution, and ponder over risks and governance that should be considered when allowing AI-driven data flows.

This paper discusses the potential contribution of Artificial Intelligence (AI) to Cybersecurity: How can it be useful for securing healthcare patient data? ML, NLP, AD(shell 2017) for defending cyber threats AI techniques such as machine learning and natural language processing (NLP), anomaly detection are the best helpful in detecting and protecting from cyber threats. [1] [8] The work evaluates these technologies for their suitability in the context of real-time threat discovery, automated incident response and data confidentiality protection.