Statistical analysis and advanced data analytics are essential to develop real-time data collection and analysis. Raw data is collected from a variety of sources such as the internet, sensor data, and radio frequency (RF) intercepts. The data is then filtered, processed, cleaned, and store-gathered into virtual cloud data repositories that house large volumes of structured and unstructured data.
Significant independent variables that show anomalies and hidden patterns in the data are investigated to help formulate innovative predictive insights. Next-level machine learning techniques (a key technology of artificial intelligence) are used to:
Natural Language Processing (NLP) holds significant promise in revolutionizing various aspects of aviation applications, particularly in the realm of anomaly detection. In the aviation industry, a wealth of textual data is generated daily through incident reports, pilot-Air Traffic Control (ATC) communications, maintenance logs, and more. NLP techniques empower aviation professionals to extract valuable insights from this vast pool of information, enhancing safety protocols and operational efficiency. By employing NLP algorithms, aviation stakeholders can derive meaningful patterns and contextual understanding from unstructured text, enabling them to proactively address potential anomalies and safety concerns.
Anomaly detection in aviation involves identifying deviations from normal operational behavior that may indicate safety risks or irregularities. NLP plays a crucial role in this process by providing a sophisticated means of analyzing textual data to uncover subtle nuances and irregularities that might go unnoticed through traditional methods. The semantic understanding provided by NLP models further refines anomaly detection by capturing the intricate details and context embedded in textual data, allowing for more accurate risk assessment and mitigation strategies. Overall, the integration of NLP in aviation applications not only streamlines data processing but also fortifies the industry's ability to proactively manage safety, ensuring a safer and more efficient air travel experience.
Concepts Beyond is leading innovation in using the latest NLP techniques and has developed models for:
Identify occurrence categories, phase of flight, hazards and other relevant information from safety report narratives using NLP with large language models (LLMs).
Classify transcripts of voice communication between the pilot and controller into various categories based on a taxonomy (Emergency and Safety, Clearances and Instructions, etc.) and extract named entities (Callsign, Altitude, Facility, etc.)
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