Machine Learning, IoT, and Blockchain Integration for Improving Process Management Application Security

Abstract

AbstractThe application of machine learning (ML) procedures is enormously scaling up owing to the quick digitization and advancement of innovative know-hows like the Internet of Things (IoT) and blockchain technology (BT). In the present digital period, it is discovered that ML techniques are employed in the fields of industries, health care, IoT, engineering, process management, finance, etc. Smart Process Applications (SPA) are founded on the numerous requirements for forecasting the consistency and eminence of the application. Several ML techniques are being evaluated in this respect. This study, therefore, employed the combination of BT and ML to protect network communications and manage datasets to overwhelm the counterfeit dataset. To bring about and evaluate the gathered dataset, big data procedures were employed. Likewise, the fault diagnosis forecast aspect was evaluated on the predictive ML approach proposed which is the improved ensemble learning (IEL) classification ML technique. The system was implemented using the traditional ensemble learning (TEL) and the improved ensemble learning (IEL), and performance matrices like accuracy, precision, sensitivity, and false-positive rate (FPR) were used to evaluate the system performance. In conclusion, the two classification ML techniques (TEL and IEL) were compared using the performance metrics, and it was discovered that the IEL outperformed the TEL with an accuracy of 98.3%, precision of 97.5%, sensitivity of 100%, and FPR of 4.8%.KeywordsBlockchainInternet of ThingsMachine learningBig dataEnsemble learningProcess management