# Enhancing Data Integrity in Blockchain Oracles Through Multi-Label Analysis
## 4 Conclusions  
The data integrity validation proposal based on Ensemble Learning and Multi-level weighted hard voting algorithm has proven effective in detecting and controlling non-integral data in coffee futures-oriented smart contracts. By implementing this method  
Figure 11: Training time and Mean Squared Error. Figure 10: Results of integrity tests in transactions generated using the proposed method. in a test environment with Ganache, high precision levels were observed, achieving a 98% success rate in generating validated transactions, highlighting the robustness of the approach. The combination of bagging models and hard voting allows for capturing multiple data perspectives, which contributes to greater accuracy in the classification process and reduces the risk of erroneous decisions in smart contracts. When comparing the proposal with methods used by other decentralized oracles, such as Chainlink, Band Protocol, and Tellow, it becomes evident that each employs different techniques to ensure data integrity in the blockchain. Chainlink, for instance, uses consensus algorithms with incentives to penalize dishonesty and reward accuracy, while Band Protocol aggregates data from multiple sources, applying a consensus algorithm to ensure information quality. The proposal presented in this study stands out due to its focus on various models and Integrity Multi-level Weighted Voting (IMWV), offering a competitive advantage by improving system reliability in the face of inconsistent data compared to validation approaches that rely on a single consensus model. Additionally, latency is a critical factor in data validation for smart contracts. This study's latency remained within acceptable limits for most tests, reflecting efficient system design. However, latency could be impacted by network load and transaction complexity, underscoring the importance of continuously monitoring and adjusting the system to optimize performance. Since the effectiveness of the hard voting method depends on the speed with which models cast their votes, an increase in latency can compromise the integrity of the final decision, highlighting the need to manage this parameter proactively. This proposed method provides a replicable and adaptable solution for other scenarios within the blockchain ecosystem that rely on oracles. Its modular design allows for incorporating new integrity models or metrics as needed, extending its applicability beyond futures contracts. Furthermore, this work paves the way to explore additional enhancements, such as incorporating advanced machine learning techniques to predict integrity scores and leveraging decentralized networks to mitigate reliance on single data sources. The combination of these elements could further strengthen trust in blockchain-based systems. To conclude, as future work, it is proposed to evaluate the approach using more methods and various strategies, such as vector attacks and cybersecurity approaches. This will allow the model to be tested against potential threats and evaluate its performance in more complex scenarios, where factors such as data integrity and security are critical. Additionally, there are plans to explore defense techniques that strengthen the model's reliability and resilience against potential vulnerabilities, ensuring its effectiveness in environments with higher security risks. C.C.O. : Conceptualization, Data curation, Funding acquisition, Investigation, Software, Supervision, Validation, Visualization, Writing--original draft, Writing--review and editing. G.R.-G.: Conceptualization, Data curation, Formal Analysis, Funding acquisition, Investigation, Project administration, Resources, Software, Supervision, Validation, Writing--original draft, Writing--review and editing. J.C.C. : Conceptualization, Data curation, Formal Analysis, Funding acquisition, Methodology, Project administration, Resources, Software, Validation, Visualization, Writing--original draft, Writing--review and editing. All authors have read and agreed to the published version of the manuscript.