Safety has always been a key focus for airport operations. ARFF Safety Management Software (SMS) is described as a formalized approach to managing safety by developing an organization-wide safety policy, developing formal methods of identifying hazards, analyzing and mitigating risk, developing methods for ensuring continuous safety improvement, and creating a monitoring and control framework. SMS also has a proactive approach, rather than a reactive approach, at its foundation.SMS is not a prescribed process but rather a framework.

Airport is divided into control points to monitor. Our software provides the foundation or framework for SMS. It outlines the methods and tools for achieving desired safety outcomes and details management’s responsibility and accountability for safety. It uses a set of standard processes to proactively identify hazards, analyze and assess potential risks, and design appropriate risk mitigation strategies. Airport is divided into control monitor the organization’s performance in meeting its current safety standards and objectives as well as contribute to continuous safety improvement. Consulting processes include information acquisition, analysis, system assessment, and deployment. The processes and procedures also help to create an environment where safety objectives can be achieved.

Machine learning, reorganized as a separate field, started to flourish in the 1990s. The field changed its goal from achieving artificial intelligence to tackling solvable problems of a practical nature. It shifted focus away from the symbolic approaches it had inherited from AI, and toward methods and models borrowed from statistics and probability theory.[19] It also benefited from the increasing availability of digitized information, and the possibility to distribute that via the Internet. Machine learning focuses on prediction, based on known properties learned from the training data. Machine learning employs data mining methods as “unsupervised learning” or as a preprocessing step to improve learner accuracy. Because of new computing technologies, machine learning today is not like machine learning of the past. It was born from pattern recognition and the theory that computers can learn without being programmed to perform specific tasks; researchers interested in artificial intelligence wanted to see if computers could learn from data.

The iterative aspect of machine learning is important because as models are exposed to new data, they are able to independently adapt. They learn from previous computations to produce reliable, repeatable decisions and results. It’s a science that’s not new – but one that has gained fresh momentum. ML can look at photos and recognize human faces, identify and label objects. In a sample POC at the Airport, EGR Machine learning COE identified “Pressure Gauge Indicators” with indicator pointing at High, Medium or Low. End Users can use it to add facial recognition as a layer of security to their app, sort through large collection of photos to tag and index them, and “build ‘smart’ solutions.