ARPI - Automated Recognition of Pest Insect Images
Critical infrastructures, distributed sensor/actuators systems, social networks and public protection applications are examples of systems characterized by high complexity and production of big and uncertain data. As such, solutions designed to address specific applications require sophisticated mechanisms to grant data handling and process understanding, robustness and resilience abilities, capacity to detect changes in nonstationary and adapt to concept drift, self-awareness to diagnose a fault and self-healing mechanisms to repair it as well as support remote controllability and reprogrammability of the solution. Moreover, we neither can further accept strong hypotheses that make the mathematics amenable at the cost of loss in effectiveness and applicability nor decouple the design of an intelligent cyber-physical system from reality and its implementation and deployment. A multidisciplinary approach is needed at the system/system of systems level requiring the introduction of intelligence and adaptation abilities directly in the design phase of the solution. Here, machine learning and computational intelligence are precious tools, combined with traditional techniques, to address and solve the above aspects yielding credible solutions, transferable to industry. Machine learning techniques have been constituting the leitmotiv of the study and lead to the design of intelligent systems and smart solutions to not-trivial problems and real-world applications.