# Smart Manufacturing Real-Time Analysis Based on Blockchain and Machine Learning Approaches
## 6 Conclusions and Future Directions  
This research focused on a real-time monitoring system based on the integration of IoT sensors, big data, and a hybrid prediction model. This system anticipates improving the monitoring system in the manufacturing environment, extracts faults during the procedure, and similarly prevents issues in the assembly line. The integration of big data and IoT sensors into this system presented a large amount of real-time sensor datasets. Based on this technique, handling the datasets and extracting the proper information is examined. The applied big data technique is NoSQL MongoDB. The results section shows the effectiveness of this system, which is scalable and more reasonable than traditional methods. Moreover, the system performance was analyzed based on the network's delay, CPU, and RAM. The results showed quite acceptable solutions and were successful in the data collection and transmitting process within a short time and with low cost. Smart manufacturing's fault detection issue is also a severe problem for identifying normal and abnormal functions. The presented hybrid prediction model contains Random Forest classification, which is used to predict the input data issues. Compared with other machine learning approaches, Random Forest has higher accuracy and fits the proposed system.  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Model** & **Score** & **RMSE** \\ \hline Multiple linear regression & 0.766 & 0.313 \\ Random Forest & 0.894 & 0.204 \\ KNN (K-Nearest Neighbors) & 0.885 & 0.213 \\ Decision tree & 0.8925 & 0.207 \\ Extra tree & 0.8928 & 0.206 \\ Hybrid prediction + Random Forest & 0.956 & 0.105 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Comparison of performance of various classification models for fault detection The output of this procedure is expected to improve the manufacturing industry's decision-making and ignore unexpected faults. Blockchain technology applied in this system covers the security of the collected dataset and similarly prevents providing fake data, improves data transmission and costs and improves system safety. In future research, we will try to improve the supply chain procedure and try other related IoT sensors to improve the fault detection results. Blockchain presents an opportunity in the automotive manufacturing industry to improve the competition in the industrial world. Blockchain improves the various types of business models with lower transaction fees and reduces information transfer between various users. Reducing fraud and systemic risks are other advantages of using this system. The use of machine learning techniques in the industrial environment provides the advantage of better data acquisition and improves system accuracy. The classification and detection algorithms retain the accuracy of various models without reducing the advantages and accuracy; similarly, handling a large amount of sensor data is much easier and accurate with machine learning techniques.  
Data curation, Z.S.; funding acquisition, Y.-C.B.; investigation, Z.S.; methodology, Z.S.; project administration, Y.-C.B.; supervision, Y.-C.B.; validation, Z.S.; visualization, Z.S. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
No data available.  
The authors declare no conflict of interest.