Computation

The rapid expansion of medical data, characterized by its complex high-dimensional attributes, presents numerous promising opportunities and substantial challenges in healthcare analytics. Adopting effective feature selection techniques is essential to take advantage of the potential of such data. This research presents a modified algorithm called (mDA), which is the hybrid algorithm between the Evolutionary Population Dynamics and the Dragonfly Algorithm. This method combines Evolutionary Population Dynamics’s strength with the Dragonfly Algorithm’s flexible capabilities, offering a robust evolutionary machine learning approach specifically designed for medical data analysis. By integrating the dynamic population modeling of Evolutionary Population Dynamics with the adaptive search techniques of Dragonfly Algorithm, the proposed mDA significantly improves accuracy, reduces the number of features, and obtains the minimum average of the fitness scores. Comparative experiments conducted on seven diverse medical datasets against other established algorithms confirm the superior performance of the proposed mDA, establishing it as a valuable approach in examining complex medical data.

To address the challenges of interdependent design parameters and reliance on empirical trial-and-error in ultrasonic cell levitation culture devices, this study proposes a top-down design framework integrating multi-physics modeling with complex network analysis. First, acoustic field simulations optimize transducer arrangement and define the cell manipulation field, establishing the Top-level Basic Structure (TBS). A skeleton model of the acoustofluidic coupled field is constructed based on the TBS. Core parameters are then determined by refining the TBS through multi-physics analysis. Second, a 24-node design change propagation network is constructed. Leveraging the TBS model coupled with multi-physics fields, a directed network model analyzes parameter interactions. The HITS algorithm is applied to prioritize the design sequence based on authority and hub scores, resolving parameter conflicts. Experimental validation demonstrates a device acoustic pressure of 1.3 × 10⁴ Pa, stable cell levitation within the focused acoustic field, and a 40% reduction in design cycle time compared to traditional methods. This framework systematically sequences parameters, effectively determines the design order, enhances design efficiency, and significantly reduces dependence on empirical trial-and-error. It provides a novel approach for developing high-throughput organoid culture equipment.

Fraud in financial services—especially account opening fraud—poses major operational and reputational risks. Static rules struggle to adapt to evolving tactics, missing novel patterns and generating excessive false positives. Machine learning promises adaptive detection, but deployment faces severe class imbalance: in the NeurIPS 2022 BAF Base benchmark used here, fraud prevalence is 1.10%. Standard metrics (accuracy, f1_weighted) can look strong while doing little for the minority class. We compare Logistic Regression, SVM (RBF), Random Forest, LightGBM, and a GRU model on N = 1,000,000 accounts under a unified preprocessing pipeline. All models are trained to minimize their loss function, while configurations are selected on a stratified development set using validation-weighted F1-score f1_weighted. For the four classical models, class weighting in the loss (class_weight  ) is treated as a hyperparameter and tuned. Similarly, the GRU is trained with a fixed class-weighted CrossEntropy loss that up-weights fraud cases. This ensures that both model families leverage weighted training objectives, while their final hyperparameters are consistently selected by the f1_weighted metric. Despite similar AUCs and aligned feature importance across families, the classical models converge to high-precision, low-recall solutions (1–6% fraud recall), whereas the GRU recovers 78% recall at 5% precision (AUC $=0.8800$). Under extreme imbalance, objective choice and operating point matter at least as much as architecture.