The purpose of this community service project is to provide education to enhance technological literacy by introducing the basics of Computer Vision applications among students. The activities included a seminar attended by 170 students. An analysis using the Wilcoxon statistical test on pre-post test results showed a significant improvement in participants' understanding of Computer Vision applications. The test results indicated a significant difference before and after the activity with a value of 0.011. Through this community service, participants have successfully grasped the material presented to enhance literacy as change agents in the digital era, positively impacting societal progress through improved understanding of technology in the field of Computer Vision.

Investigating potential corrosion inhibitors via empirical research is a labor- and resource-intensive process. In this work, we evaluated various linear and non-linear algorithms as predictive models for corrosion inhibition efficiency (CIE) values using a machine learning (ML) paradigm based on the quantitative structure-property relationship (QSPR) model. In the quinoxaline compound dataset, our analysis showed that the XGBoost model performed the best predictor of other ensemble-based models. The coefficient of determination (R2), mean absolute percentage error (MAPE), and root mean squared error (RMSE) metrics were used to objectively assess this superiority. To sum up, our study offers a fresh viewpoint on the effectiveness of machine learning algorithms in determining the ability of organic compounds like quinoxaline to suppress corrosion on iron surfaces.