Publisher

UNUGHAUNUGHA

Journal PROCEEDING AL GHAZALI International ConferencePROCEEDING AL GHAZALI International Conference Abstract

Hydrogen sulfide (H₂S) is a toxic and potentially hazardous gas commonly found in industrial environments, where leaks can lead to serious health and safety risks. Effective detection of H₂S leaks is essential for preventing accidents and ensuring workplace safety. This study explores the implementation of the C4.5 algorithm combined with optimized feature extraction techniques to improve the accuracy of H₂S leak detection. By utilizing feature extraction, significant attributes of gas leak indicators are identified and analyzed, enhancing the classification accuracy of the C4.5 algorithm. The experimental results demonstrate that optimized feature extraction can significantly improve the algorithms ability to detect H₂S leaks promptly and accurately. The proposed method not only offers a reliable solution for gas leak detection but also contributes to safer industrial monitoring practices. This study highlights the potential of machine learning techniques, particularly decision tree-based methods, to advance environmental safety through intelligent monitoring systems.

Conclusion

This study demonstrates that applying feature extraction techniques significantly enhances the accuracy and reliability of the C4.5 algorithm in detecting hydrogen sulfide (H₂S) leaks in industrial environments.By identifying and selecting the most relevant features from raw data, this technique successfully reduces data noise and optimizes the classification capability of the C4.The test results indicate that combining feature extraction with C4.5 not only improves accuracy levels but also reduces the rate of false alarms, which are common in traditional gas monitoring systems.

Future Research

Future research should explore the application of other machine learning algorithms, such as Random Forest or Support Vector Machines, to compare their performance with the C4.5 algorithm in H₂S leak detection, providing a more comprehensive understanding of the optimal approach. Furthermore, expanding the dataset to include data from diverse industrial environments is crucial to ensure the robustness and generalizability of the model under varying conditions. Finally, investigating more advanced feature extraction techniques, like Principal Component Analysis or deep learning-based methods, could uncover deeper patterns within the complex gas leak data, potentially leading to even more accurate and reliable detection systems, ultimately contributing to safer and more efficient industrial practices by minimizing risks associated with hazardous gas leaks and improving overall workplace safety.

References

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