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To overcome the problems mentioned above, we applied the integrated methods of blockchain and machine learning to secure system transactions and handle a dataset to overcome the fake dataset. To manage and analyze the collected dataset, big data techniques were used. The blockchain system was implemented in the private Hyperledger Fabric platform. Similarly, the fault diagnosis prediction aspect was evaluated based on the hybrid prediction technique. The system\u2019s quality control was evaluated based on non-linear machine learning techniques, which modeled that complex environment and found the true positive rate of the system\u2019s quality control approach.<\/jats:p>","DOI":"10.3390\/s21041467","type":"journal-article","created":{"date-parts":[[2021,2,20]],"date-time":"2021-02-20T03:00:37Z","timestamp":1613790037000},"page":"1467","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":131,"title":["Integration of Blockchain, IoT and Machine Learning for Multistage Quality Control and Enhancing Security in Smart Manufacturing"],"prefix":"10.3390","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1520-1799","authenticated-orcid":false,"given":"Zeinab","family":"Shahbazi","sequence":"first","affiliation":[{"name":"Department of Computer Engineering, Jeju National University, Jejusi 63243, Korea"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1107-9941","authenticated-orcid":false,"given":"Yung-Cheol","family":"Byun","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering, Jeju National University, Jejusi 63243, Korea"}]}],"member":"1968","published-online":{"date-parts":[[2021,2,20]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Moyne, J., and Iskandar, J. 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