Timing for securing the biometric template transformation based on supervised learning using Double Random Phase Encoding Method

2025-06-18T10:02:20+00:00

Background: Among optical encryption techniques, Double Random Phase Encoding (DRPE) is one of the most widely used. Individual identities and the process of recognition remain essential to ensuring proper data access security.
Aim: The study aims to optimize an approach that ensures the significant performance effectiveness of the cancelable biometric methods for different templates and the associated time taken to transform biometric data.
Problem: This study is majorly concerned about the performance effectiveness of cancelable biometric methods that measure the likelihood that an authorized effort may be mistakenly rejected as unauthorized. Also, when compromised, several non-renewability safety challenges arise, and insufficient matching performance templates are required to build a security protection method.
Method and material. The study uses supervised learning for the Double Random Phase Encoding Method (DRPE), a 4F optical encryption system, and 20 randomly chosen photos from the ORL database of faces.
Results. The result based on the supervised learning for the Double Random Phase Encoding Method revealed false positive rates for both the fingerprint and face templates.
Conclusion. The study concluded that the performance effectiveness of the cancelable biometric in this study has a false positive rate likelihood that an authorized effort may not be mistakenly rejected as an unauthorized one.

A Machine Learning-based approach to predicting tuberculosis in the Democratic Republic of Congo

2025-07-16T08:25:04+00:00

INTRODUCTION: Tuberculosis remains a public health problem in Democratic Republic of Congo (DRC), despite advances in Machine Learning for the prediction of this disease. However, existing models are often adapted to Asian contexts and do not take into account the specific epidemiological and social characteristics of the DRC. Given this shortcoming, our study explores a Machine Learning approach specifically designed to improve the prediction of tuberculosis in the Congolese population.
OBJECTIVES: Our problem is based on the following question: "What approach, based on Machine Learning and specific to the population of DRC, is likely to improve the prediction of tuberculosis?" To answer this, we adopted an exploratory paradigm with a sequential mixed design (qualitative and quantitative). The study was conducted on a sample of 1505 patients and six healthcare professionals in the health zones of Lubumbashi and Nzanza.
METHODS: The data was collected using questionnaires and semi-structured interviews, then analysed using bivariate and multivariate approaches.
RESULTS: The results show that incorporating Congolese specificities into Machine Learning models significantly improves the prediction of tuberculosis. Of the models tested, Random Forest and Decision Tree performed best in terms of precision, recall, F1-score and AUC, while Voting Classifier, Stacking and Adaboost showed a good compromise between precision and robustness.
CONCLUSION: This study highlights the need to develop predictive models adapted to the local context in order to improve tuberculosis control in DRC. We propose an optimised model incorporating characteristics specific to the Congolese population, with a possible large-scale application to improve detection and prevention of the disease.

EAI Endorsed Transactions on Intelligent Systems and Machine Learning Applications

Timing for securing the biometric template transformation based on supervised learning using Double Random Phase Encoding Method

A Machine Learning-based approach to predicting tuberculosis in the Democratic Republic of Congo