Today’s transportation systems suffer from a high number of accidents caused by drowsy driving, so strong automated detection systems are required for implementation in the Intelligent Transportation Systems (ITS). This paper introduces an analytical framework, divided into three stages to enhance the real-time detection of drowsiness at video level based on modules and provides the experimental results to analyze the proposed framework. To overcome the problems associated with computation-intensive deep learning-based methods, the proposed method involves employing Complete Local Binary Pattern (CLBP) texture descriptors, Dim: 512 with a greedy Pearson de-correlated feature selection that results in a trash-free subset of 58 texture bins (58 discriminative bins+auxiliary predictors), so that the features involved are largely reduced to 61. A comprehensive benchmark of 28 Classifier Presets including 9 Classifier families is performed on the NTHU Drowsy Driver Detection Dataset (357 video-derived observations) via five-fold cross validation. The best configuration, which is the Quadratic Support Vector Machine (SVM) trained on top 59 dimensions of normalised CLBP features, produces 92.42% accuracy and 88.1% dimensionality reduction with a weighted F1-score of 92.13%, which is 1.13 percentage points better than the weighted F1-score of the full-feature baseline. This solution offers an alternative to deep convolutional solutions for drowsiness detection in ITS that is both simple to interpret and able to use less computation.
Timely bankruptcy of the corporations is a major issue that investors, financial institutions, and regulatory bodies need to know in order to reduce economic losses and enhance decisions on risk management. Nevertheless, bankruptcy forecasting is difficult because of extreme imbalance in classes and nonlinear correlation between financial data. This paper suggests a machine learning model of corporate bankruptcy prediction, which is explainable and statistically justified through advanced ensemble learning methods. A comparative study was conducted on a financial dataset based on Logistic Regression, Random Forest, XGBoost, LightGBM, Tuned LightGBM, and Stacking Ensemble models with $\mathbf{6, 8 1 9}$ firms and $\mathbf{9 5}$ attributes. In order to solve the problem of data imbalance, threshold optimization was used, and the optimal decision threshold was obtained (0.13). Accuracy, Precision, Recall, F1-score, ROC-AUC, PR- AUC, Matthews Correlation Coefficient, and Brier Score were used to measure model performance. The optimized LightGBM model had a better performance with the following parameters: F1-score of 0.5124, MCC of 0.4999, ROC-AUC of 0.9549 and a Brier Score of 0.0234, which showed high discrimination and good probability calibration. The explainability of the proposed framework with the help of SHAP and the statistical test developed by McNemar additionally confirmed the strength and interpretability of the proposed framework, which is why it can be applied to real-world financial risk assessment.
Kanchan, Meenu Gupta, Rakesh Kumar et al.· International Conference on...· 0 citations
Kubernetes Horizontal Pod Autoscaler(HPA) and other existing auto-scaling solutions that respond reactively to demand experience significant delays in provisioning and inefficiencies when responding to sudden workload spikes. This paper proposes a new Real-Time Workload Monitoring-Based Intelligent Auto-Scaling Framework that uses a hybrid time-series forecasting model to proactively manage cloud computing resources. Specifically, it combines periodicity detection with Prophet-based models and long-term dependence capture via Long Short-Term Memory (LSTM) networks to provide advanced notice of CPU and memory requirements before load surges occur. The results of this approach were compared against traditional reactive auto-scaling methods and demonstrated that it significantly reduced scaling latencies, minimized service level agreement (SLA) violations, and optimized resource utilization during load surges.
Nikita Singh, Meenu Gupta, Rakesh Kumar et al.· International Conference on...· 0 citations