Integrating AI and Iot with Prisma, NLP, LDA and Machine Learning for Data Quality and Extraction

The intersection of Internet of Things (IoT) and Artificial Intelligence (AI) significantly enhances data extraction and quality analysis in numerous disciplines. The present paper suggests a hybrid system that combines PRISMA, Natural Language Processing (NLP), Latent Dirichlet Allocation (LDA), and Machine Learning (ML) for handling the massive, high-dimensional datasets produced by IoT platforms. PRISMA provides controlled data collection, while NLP and LDA provide semantic discovery and topic modeling. Machine Learning algorithms evaluate data quality and enable appropriate, automated decision-making. The end-to-end integration is <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">9 1 %</tex> accurate and more accurate in the accuracy of individual processes' accuracy, recall, <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">F 1</tex>-score, and computation efficiency. Synergy not only reduces redundancy and optimizes data reliability but also enables scalable, real-time insights. The architecture effectively demonstrates its applicability in improving precision and resilience of datadriven applications, particularly healthcare, industrial automation, and intelligent spaces. It provides an avenue for developing smart, adaptive systems that efficiently handle diverse and unstructured data.