Fig. 4: The RSBoost hybrid model utilizes CNN-SVM feature extraction with LSBoost regression on heterogeneous datasets to achieve robust classification and regression.

a The figure illustrates the dynamic structure of the RSBoost hybrid algorithm, integrating with CNN-SVM and LSBoost layers. The CNN-SVM layer, tailored for classification, computes length scale values to extract 3 key morphological features. It employs a superpixel tool to analyze sensor color distributions, shape factors to track color area changes over time, and the RGB index to identify color variations. The LSBoost layer, designed for regression learning, is trained on RGB intensity datasets from 3 bacterial types based on varying concentration combinations (CFU/ml). This process is enhanced by parallel learning, feature equation extraction, weight allocation, and iterative feedback training, showcasing the algorithm’s adaptability. b A 3D scatter plot of nine classes (S. aureus at 10¹ CFU/mL in purple; S. aureus at 10² CFU/mL in pink; S. aureus at 10³ CFU/mL in red; E. coli at 10¹ CFU/mL in light blue; E. coli at 10² CFU/mL in navy; E. coli at 10³ CFU/mL in blue; P. aeruginosa at 10¹ CFU/mL in lime; P. aeruginosa at 10² CFU/mL in turtle; and P. aeruginosa at 10³ CFU/mL in darkest green), evaluated using Monte Carlo cross-validation (MCCV), compares actual versus predicted values. c A ROC curve illustrates the classification results. d An R-plot compares actual and predicted RGB intensity values for nine classes (Applied the same color legend to all nine classes mentioned in b). e In the Pearson correlation matrix, correlation coefficients near +1 (in yellow) indicate minimal latent features, whereas coefficients near −1 (in sky-blue) denote maximal potential for latent feature extraction.