This research uses AI-powered markerless motion capture to preserve Indigenous cultural dances as digital archives. By recording thousands of movement data points, it safeguards intangible cultural heritage for future generations. The work aims to extend this technology globally, ensuring every culture has the tools to preserve its unique traditions.

Using machine learning and Hidden Markov Models, this research analyzes the authorship of disputed New Testament letters. The results show that stylistic differences reflect the Apostle Paul’s versatile writing styles rather than forgery, demonstrating how modern computational tools can help recover long-standing historical truths.