13 research outputs found
Role-based Trust Management Model in Multi-domain Environment
No full textBased on the in-depth analysis of issues in dRBAC model, which include the lack of commission depth control in distributed environment, the inefficiency of cascading revocation of the authorization roles and the incapability of judging whether the commission violates the principles of RBAC model before it is done, this paper proposed MD-dRBAC Model, designed trust management mechanism for MD-dRBAC Model, which was used to control the access, established the credible authority commission tree and finally proposed the detection algorithm for implicit authorities upgrading to avoid violation of the least privilege principle in RBAC model Extensive security and performance analysis show that the proposed schemes are highly efficient and secure
Joint Optimization of Caching and Routing Strategies in Content Delivery Networks: A Big Data Case
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Real-World Tumor Response of Palbociclib Plus Letrozole Versus Letrozole for Metastatic Breast Cancer in US Clinical Practice
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Data-Driven Loan Default Prediction: A Machine Learning Approach for Enhancing Business Process Management
No full textLoan default prediction is a critical task for financial institutions, directly influencing risk management, loan approval decisions, and profitability. This study evaluates the effectiveness of machine learning models, specifically XGBoost, Gradient Boosting, Random Forest, and LightGBM, in predicting loan defaults. The research investigates the following question: How effective are machine learning models in predicting loan defaults compared to traditional approaches? A structured machine learning pipeline is developed, including data preprocessing, feature engineering, class imbalance handling (SMOTE and class weighting), model training, hyperparameter tuning, and evaluation. Models are assessed using accuracy, F1-score, ROC AUC, precision–recall curves, and confusion matrices. The results show that Gradient Boosting achieves the highest overall classification performance (accuracy = 0.8887, F1-score = 0.8084, recall = 0.8021), making it the most effective model for identifying defaulters. XGBoost exhibits superior discriminatory power with the highest ROC AUC (0.9714). A cost-sensitive threshold-tuning procedure is embedded to align predictions with regulatory loss weights to support audit requirements
Simultaneous surface and depth neural activity recording with graphene transistor-based dual-modality probes
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