## 4 Proposed Model
### Simulation Results and Discussion  
In this context, the simulation results are compared with the state-of-the-art methods to check the validity and robustness of the proposed analytical accreditation model in terms of certificate credential identification, pattern extraction, detection, recognition, and classification for verification of certificate credential and management. We simulate our proposed model on the sample dataset (collected from distinct open-source platforms) of certificates. The artificial neural network is applied to the collected dataset for the purpose of training the network, this whole scenario is designed to identify tamper or forged credentials and pre-verify certificate credentials before attestation, as already discussed in the above sections. In the artificial neural network used to analyze the high-dimensional classification, the process of analysis is as follows: (i) distribute individual aspects of the certificate, (ii) identify rate of originality/tamper or forged proof, (iii) extract pattern, (iv) detection, (v) recognition, and (vi) classification, as shown in Figure 5.  
Figure 3: Blockchain hyperledger sawtooth-enabled certificate credentials verification and accreditation architecture for HED. According to the process of pre-verification, we evaluate the detection and classification of originality and tampering of certificate credentials through the number of recognition items and rate of error generation. By this act, we get the frequency of credential originality classification, shown in Figure 6. The evaluation matric of the proposed process is  
Figure 4: Events of education-accreditation node transactions. Figure 5: Simulation results of the proposed accreditation model. (**A**) Input image, (**B**) encode to identify tampering, (**C**) convert into grayscale to fine trace, (**D**) image matrix, and (**E**) output to check individual aspects. defined as follows: (i) evaluation of pattern for classification, (ii) validation, and (iii) rate of detection, shown in Figure 7.  
Another simulation is applied on the same dataset to find a trace of a different portion of certificate credentials for the purpose of analyzing forged proof accurately. Figure 8  
Figure 6: Certificate pre-verification/classification. (**A**) Calculate frequency of credential classification, (**B**) error rate, and (**C**) calculate frequency of analysis. Figure 7: Rate of certificate credentials detection. (**A**) Evaluate pattern for classification, (**B**) rate of validation, and (**C**) pattern classification evaluation through a 3D model. presents the result of the proposed accreditation model for pre-verification/classification of distinct aspects, where we provide an input image of the certificate to trace credential credibility. Initially, the model encodes images to identify tampering/originality, then it moves towards the conversion of grayscale to fine traces. After the completion of the tracing process, the image matrixes calculate bit-by-bit to analyze alteration through the bits of the image. Finally, we get the output of the image with a detailed description, as shown in Figure 8.  
We test our proposed blockchain hyperledger sawtooth-enabled accreditation model with other public network blockchains (Ethereum) and customized blockchain to check the security and privacy of the education ledger, as shown in Table 2. Our proposed model and preservation mechanism provides better results and performs great with an accuracy of 91.7% and is efficient in accordance with the dynamic collection of records (variable nature). The criteria of analysis privacy and protection are as follows: (i) transparency, (ii) integrity, (iii) provenance, (iv) consensus protocols and policies, (v) immutability, (vi) network communication (on-chain and off-chain), (vii) hash-based protection, and (viii) preservation mechanism, as shown in Figure 3:0  
Figure 3:1 Another simulation result of the proposed accreditation model.