Bankruptcy forecasting is an important function for banks and other financial institutions for determining default risk and making informed choices. However, conventional bankruptcy forecasting models are challenged by the intricate, non-linear relations among different financial metrics. These models are often unable to identify the best features and obtain high predictive accuracy, which results in poor risk estimation. For solving these issues, sensitivity analysis is used within this paper to find out which features are most contributing to bankruptcy outcomes. The predictive performance is boosted by using sophisticated optimization methods, including Ant Colony Optimization (ACO) and Cuckoo Search Optimization (CSO), for hyperparameter and feature selection. These tools facilitate improved determination of features and the accuracy of a model. The findings indicate that Competitiveness is indeed the most significant characteristic that is ranked highest by Recursive Feature Elimination (RFE) and sensitivity analysis. Moreover, ensemble-based models, including XGBC, optimized by ACO (XGAC), achieve better performance compared to standard models, including Logistic Regression Classification (LRC). The performance is tested against two scenarios, namely, bankrupt (B) and non-bankrupt (NB), with better performance by XGAC. The conclusions are of direct applicability for real-world bankruptcy forecasting, which will help banks enhance risk-handling capabilities. Further research ought to focus on combining time-series data for further model enhancement.
The number of edge devices in large-scale edge systems is rapidly increasing, while these devices operate under constraints in processing capacity, memory, and network bandwidth. Efficient resource utilization and data management are therefore important for edge query processing. Join operations are among the most computationally and resource-intensive database operations, particularly when large volumes of data must be scanned. Column Imprint-Hash Join (CI-HJ), a state-of-the-art approach for edge query processing, addresses this challenge through data-aware equi-height binning to reduce unnecessary cacheline accesses during hash joins. However, its binning strategy does not consider query workload characteristics and may therefore provide limited benefit when frequently accessed value ranges are not aligned with the data distribution. This paper presents Workload-Aware Column Imprint-Hash Join (WACI-HJ), which incorporates query workload characteristics into Column Imprint construction to improve workload-aware data skipping. WACI-HJ further employs ARIMA-based workload prediction to anticipate query access patterns and organize the data structure before subsequent queries arrive. The framework consists of two phases: (1) WACI-HJ Generation, comprising Pre-processing, Prediction, and Blocking and Hashing modules for workload-aware bin construction, and (2) Query Processing and Resource Utilization, which processes incoming queries using the currently available WACI-HJ structure and monitors CPU, RAM, and I/O utilization. Evaluation using benchmark and real-world smart-transportation datasets demonstrates a 54% reduction in Percentage of Cachelines Read (PCR) and a 10% improvement in Query Execution Time (QET) compared with CI-HJ. The proposed approach is also evaluated under different data scales and skewness levels to examine its scalability and robustness. Resource-utilization analysis shows reductions of 1%, 38%, and 49% in CPU, RAM, and I/O utilization, respectively. These results indicate that workload-aware Column Imprint organization can reduce unnecessary data access and improve low-latency query processing in resource-constrained edge environments. The approach is particularly applicable to smart-transportation workloads, including traffic analysis, congestion management, and route-related query processing, and can be extended to other edge-computing applications requiring efficient query processing.
