Abstract: The present disclosure relates to a method and system for multi-source algae image target detection, and relates to the field of monitoring of algal bloom events in fresh water. The method includes first crawling images of algae of a selected species by using a built automated algae crawling tool, where the images include all formats; classifying and labeling algae in the algae images, and forming a source domain dataset by using all the classified and labeled algae images; performing transfer learning by using a faster recurrent revolutional neural network (Faster RCNN) with reference to a target domain dataset, to obtain a multi-source algae image target detection model; and finally performing identification and classification by using the multi-source algae image target detection model.

Abstract: Multiple agent root cause analysis techniques are described. In an implementation, a processor performs operations to troubleshoot performance of a system operation by a plurality of domains (e.g., of a distributed system). The processor executes a system artificial intelligence (AI) agent to determine which domains of the plurality of domains include domain functions in support of the system operation, and to generate, using machine learning, queries to the determined domains based on the system operation. Domain responses are received from domain AI agents associated with the determined domains responsive to the queries and generated based on domain data associated with respective domains. A system response is generated by the system AI agent using machine learning based on the domain responses.

Abstract: The present disclosure relates to a method and system for multi-source algae image target detection, and relates to the field of monitoring of algal bloom events in fresh water. The method includes first crawling images of algae of a selected species by using a built automated algae crawling tool, where the images include all formats; classifying and labeling algae in the algae images, and forming a source domain dataset by using all the classified and labeled algae images; performing transfer learning by using a faster recurrent revolutional neural network (Faster RCNN) with reference to a target domain dataset, to obtain a multi-source algae image target detection model; and finally performing identification and classification by using the multi-source algae image target detection model.