Speakers
Description
AI-driven methodologies are revolutionizing data indexing and analysis, offering substantial improvements in automating and enhancing data workflows across various sectors. Techniques such as machine learning, natural language processing, and deep learning hold significant potential to improve the efficiency and accuracy of monitoring systems, particularly in the context of large and complex data sets. Traditional methods often struggle with such data sets, leading to delays and inaccuracies in extracting actionable insights. AI can greatly enhance the processing of data from diverse sources, including sensor networks, seismic data, and On-Site Inspections, enabling faster and more accurate detection of nuclear test events and improving treaty verification. AI-driven techniques can optimize the performance evaluation and operational efficiency of the International Monitoring System, focusing on enhancing data availability, quality, and timeliness. By automating anomaly detection, predictive analytics, and decision support, AI can improve network resilience and coverage. This research focuses on the development of a Machine Learning model, designed to optimize data indexing and analysis for nuclear test monitoring, including advancements in data protection, cybersecurity, and interoperability. It also highlights the integration of emerging technologies for real time data analysis, improving feedback mechanisms, and supporting national needs, ultimately strengthening the global verification regime and facilitating the Comprehensive Nuclear-Test-Ban Treaty Organization's global implementation.
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