###### Abstract  
Symmetric data play an effective role in the risk assessment process, and, therefore, integrating symmetrical information using Failure Mode and Effects Analysis (FMEA) is essential in implementing projects with big data. This proactive approach helps to quickly identify risks and take measures to address them. However, this task is always time-consuming and costly. On the other hand, there is an essential need for an expert in this field to carry out this process manually. Therefore, in the present study, the authors propose a new methodology to automatically manage this task through a deep-learning technique. Moreover, due to the different nature of the risk data, it is not possible to consider a single neural network architecture for all of them. To overcome this problem, a Genetic Algorithm (GA) was employed to find the best architecture and hyperparameters. Finally, the risks were processed and predicted using the new proposed methodology without sending data to other servers, i.e., external servers. The results of the analysis for the first risk, i.e., latency and real-time processing, showed that using the proposed methodology can improve the detection accuracy of the failure mode by 71.52%, 54.72%, 72.47%, and 75.73% compared to the unique algorithm with the activation function of Relu and number of neurons 32, respectively, related to the one, two, three, and four hidden layers. neural network; deep-learning technique; genetic algorithm; hyperparameter +
Footnote †: journal: Medical  
to the success of this technique is to use a diverse group of members with different perspectives, expertise, and experiences. Typically, this team includes representatives from various engineering, manufacturing, quality, and maintenance departments in a factory. However, this approach can lead to conflicting opinions and ultimately make the process slower or less effective. But experience has shown that, if the opinions are aligned, the result obtained is much better than individual performance in a system. Scientific achievements of scientists show that Artificial Intelligence (AI) can work better than human experts in some situations, because AI systems are designed to analyze and process large amounts of data quickly and accurately. AI can process data faster than a human expert and can identify patterns and correlations that may not be immediately apparent to a human. In addition to the above, there are several reasons to use Deep Neural Network (DNN) in FMEA instead of relying on traditional expert analysis. DNN is used to extract hidden information from symmetry data by employing network-trained indicators to discover underlying patterns and relationships within the dataset. This can be carried out by analyzing the outputs of specific layers within the network, particularly the hidden layers where the network learns complex patterns and data representations. Through its multiple layers, DNN has the ability to capture complex features and hierarchies that may not be apparent in the original dataset [12; 13]. These advantages include objectivity, stability, speed, scalability, and improved accuracy [14]. AI can also learn and adapt over time. Machine-learning algorithms allow AI systems to continuously improve their performance by analyzing past data and adjusting their predictions or recommendations. A new method has been presented that uses Monte Carlo simulation to generate random uncertainty in input parameters and fuzzy sets to represent uncertainty in RPN evaluation. After that, the scholars used a Fuzzy Inference System (FIS) to combine these uncertainties and obtain the final RPN [15]. The authors evaluated the effectiveness of their proposed method using a case study of a hydraulic system in a manufacturing plant. The results showed that the proposed method can accurately assess RPN and prioritize mitigation efforts [16]. Moreover, a novel approach has been presented to implement an intelligent FMEA system using a hierarchy of back-propagation neural networks. [PERSON] et al.