The emergence of Industrial Internet of Things (IIoT) leads in developing automated environments, such as smart factories, smart airports and smart healthcare systems. AI applications enable the automation and data analytics across industrial technologies, including the Internet of Things (IoT), cloud and edge, and fog computing paradigms. Existing Artificial Intelligence (AI), especially Deep Learning (DL) models, still suffer from designing a generalized architecture that reveals semantics and contexts of models, considering Human-in-the-loop (HITL). Adversarial Machine Learning (AML) models have been widely utilized to fool DL applications using malicious actors. This makes a great interest to establish white-box models, rather than black-box ones, to determine their trustworthiness and reliability in business problems in IIoT networks.

"Hey Siri, what is IIoT?" Every time you ask Siri, you get an accurate answer. Wonder how your virtual assistant suggests, thinks and talks like a human being? Because it has been taught that way through machine learning. Finance, retail and healthcare are just a few of the many industries that are benefitted by machine learning.