Basic idea of the execution is to assure that the Omicron disease severer affected role collected statistics functioned in the way that can compel preparation, subdivision from their first outlook.
4.1 ERNN Algorithm:
Recurrent NNs (RNN) is foremost used techniques of Artificial NN (ANN) systems. It is a kind of organization of brain systems where it has circles as input associations. The Elman Network preparing design is as like as the Multi-Layer Perceptron (MLP) preparing, the establishment yield contrasted with the goal end result and mistake is applied to refresh the company hundreds as indicated with the aid of using the Backpropagation mistake calculation with the unique case that the upsides of affiliation hundreds are consistent for 1.0. The prototypical of the Elman network explained below as in Equation 1.
ERNN  is one of the most used classes of Neural Networks (NN). Its Arrangement Labeling-Part of discourse named element acknowledgment and labeling are extremely useful and effective to obtain desired results. To infer the mentioned advantages, we trundled out positive enhancements to Recurrent NN (traditional) and get ERNN; by making a group ,  of changes:
Step 1: Drafts the degree of put-away records from experience.
Step 2: Drafts the quantity of data being added in the existing implementation.
Step 3: Drafts the amount of the yield evidence existence
Our experiment involved the following related processes:
Step 1: Introduce the essential collection
Step 2: Introduce the research dataset
Step 3: Implement In the floodlight ordering of the change of evidence
Step 4: Prepare the data composition with 70-time phases and 2 yield
Step 5: Introduce Keras deep learning library
Step 6: Reset of the ERNN
Step 7: Enhancement of the ERNN part & about regulation of loss calculation function.
Step 8: Improvement of yield chunk.
Step 9: Accelerate the ERNN
Step 10: Fitting the ERNN in the research dataset
Step 11: Load the Omicron disease infection test image data for 2020
Step 12: Become an expected Omicron disease infection in Dec 2019
Step 13: Imagine aftereffects with anticipated or genuine Omicron disease infection
INPUT DATSET: Here the input dataset is having 14 columns with target class, i.e., severity level of the Omicron disease.
Research Data (Input): The research dataset collected from various opensource resources such as Kaggle had 6098 x-ray images.
5. RESULTS: Here are the result of in finding Omicron disease detection by integrating ERNN.
Fig 5.1 Exemplify the execution flow through Epochs on Omicron dataset
Fig 5.2 Exposes the CPU, RAM, and other computing resources occupancy of Omicron ERNN code.
Fig 5.3 Demonstrates the accurateness of the code through Epochs vs. Accuracy graph representation.
Fig 5.4 Exemplifies the reduction ratio of the loss compared with epochs during RNN code execution on Omicron dataset taken from Kaggle.
Fig 5.5 Depicts the time frequency to complete each iteration of Omicron RNN code execution with respect to each epoch.
Fig 5.6 The above image explains and compares the Loss with respect to Accuracy of Omicron RNN code execution on database.
Fig 5.7 Explains how the Loss is reduced with respect to time and accuracy on each epoch.
Fig 5.8 The Omicron RNN code achieved the accuracy of 88.28% during training of the module.
5.1 Evaluation Methods:
The following are measurements of evaluation methods or metrics.