Antifungal susceptibility using disk diffusion assay for the lead compounds 3a and 3b against <i>C</i>. <i>albicans</i> ATCC 90028, FLC-susceptible <i>C</i>. <i>albicans</i> D27 and FLC-resistant <i>C</i>. <i>albicans</i> D15.9.

<p>Antifungal susceptibility using disk diffusion assay for the lead compounds 3a and 3b against <i>C</i>. <i>albicans</i> ATCC 90028, FLC-susceptible <i>C</i>. <i>albicans</i> D27 and FLC-resistant <i>C</i>. <i>albicans</i> D15.9.</p

Anti-<i>Candida</i> activity of newly synthesized 1, 2, 3-triazoles.

<p>Anti-<i>Candida</i> activity of newly synthesized 1, 2, 3-triazoles.</p

Effect of 3a and 3b on the rate of H⁺ efflux by <i>C</i>. <i>albicans</i> ATCC 90028, FLC-susceptible <i>C</i>. <i>albicans</i> D27 and FLC-resistant <i>C</i>. <i>albicans</i> D15.9 in the presence and absence of glucose (G).

<p>Both the test compounds had a greater inhibitory effect on H⁺ efflux in comparison to FLC.</p

Intracellular pH of <i>C</i>. <i>albicans</i> ATCC 90028; FLC-susceptible <i>C</i>. <i>albicans</i> D27and FLC-resistant <i>C</i>. <i>albicans</i> D15.9 with and without the treatment of 100 μg/mL of test compounds 3a and 3b.

<p>Fluconazole (10 μg/mL) was used as standard drug. The decrease in pH showed the accumulation of H⁺ ions due to reduced activity of the proton pump. The acidity error bars represent Mean±S.D. from three independent recordings.</p

Algorithm for feature sensitivity analysis.

This research addresses the pressing challenge of intrusion detection and prevention in Wireless Sensor Networks (WSNs), offering an innovative and comprehensive approach. The research leverages Support Vector Regression (SVR) models to predict the number of barriers necessary for effective intrusion detection and prevention while optimising their strategic placement. The paper employs the Ant Colony Optimization (ACO) algorithm to enhance the precision of barrier placement and resource allocation. The integrated approach combines SVR predictive modelling with ACO-based optimisation, contributing to advancing adaptive security solutions for WSNs. Feature ranking highlights the critical influence of barrier count attributes, and regularisation techniques are applied to enhance model robustness. Importantly, the results reveal substantial percentage improvements in model accuracy metrics: a 4835.71% reduction in Mean Squared Error (MSE) for ACO-SVR1, an 862.08% improvement in Mean Absolute Error (MAE) for ACO-SVR1, and an 86.29% enhancement in R-squared (R2) for ACO-SVR1. ACO-SVR2 has a 2202.85% reduction in MSE, a 733.98% improvement in MAE, and a 54.03% enhancement in R-squared. These considerable improvements verify the method’s effectiveness in enhancing WSNs, ensuring reliability and resilience in critical infrastructure. The paper concludes with a performance comparison and emphasises the remarkable efficacy of regularisation. It also underscores the practicality of precise barrier count estimation and optimised barrier placement, enhancing the security and resilience of WSNs against potential threats.</div

Fig 12 -

(a) Best Solution and All Nodes for ACO–SVR1 Model and (b) Best Solution and All Nodes for ACO–SVR2 Model.</p

Algorithm for regularization techniques application.

Algorithm for regularization techniques application.</p

Bar plot comparing the effect of L1 and L2 regularization on various metrics for model 1.

Bar plot comparing the effect of L1 and L2 regularization on various metrics for model 1.</p

Scatter plot of actual vs. predicted values for SVR2 model.

Scatter plot of actual vs. predicted values for SVR2 model.</p

Ranking of features according to feature importance.

Ranking of features according to feature importance.</p