EC Clinical and Medical Case Reports

Non-Intrusive inspection (NII) using DJI Phantom 4 drones for video surveillance of dairy manufacturing plants operational activity and the utilization of computer vision, machine learning, NoSQL databases (e.g. MongoDB, HBase, and Cassandra) and the Hadoop Ecosystem with Spark Directed Acyclic Graph Execution Engine (DAG) to analyze ATP bioluminescent sensor micrographs from biofilms on a dairy manufacturing assembly lines is a powerful threat detection technique. The detection of “bacteria in milk typically adhere and aggregate on stainless steel surfaces, resulting in biofilm formation in milk storage tanks and milk process lines. The exponential growth of biofilms in milk processing environments incorporates more opportunities for microbial contamination of the processed dairy products. These biofilms may contain spoilage and pathogenic microorganisms, such as Yersinia, enterocolitis, and strains of Escherichia coli which can survive on different surfaces for periods ranging from several hours to days. In addition, even within biofilms the most deleterious spoilage bacteria originating from raw milk is pseudomonads. The detection and predictive tracking of pseudomonads originating from raw milk and pathogenic microorganisms captured by ATP bioluminescent micrograph sensors are ingested into the Nephilim Base Unit Architecture, authentication server that can be configured on the (Access Point) AP or on an external server. We also incorporate image anomaly detection to classify micrograph biofilm images which are considered outliers (e.g. ATP biofilm sensor yielding a totally black image). Micrograph images detected as outliers are filtered, flagged, and ranked using Spark filter and window functions before encrypted data propagation.

 Keywords: Non-Intrusive Inspection (NII); DJI Phantom 4 Drones; Hadoop Ecosystem; Directed Acyclic Graph Execution Engine (DAG)

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