Predicting Consumer Service Price Evolution during the COVID-19 Pandemic: An Optimized Machine Learning Approach

This research analyzes the impact of the COVID-19 pandemic on consumer service pricing within the European Union, focusing on the Transportation, Accommodation, and Food Service sectors. Our study employs various machine learning models, including multilayer perceptron, XGBoost, CatBoost, and random forest, along with genetic algorithms for comprehensive hyperparameter tuning and price evolution forecasting. We incorporate coronavirus cases and deaths as factors to enhance prediction accuracy. The dataset comprises monthly reports of COVID-19 cases and deaths, alongside managerial survey responses regarding company estimations. Applying genetic algorithms for hyperparameter optimization across all models results in significant enhancements, yielding optimized models that exhibit RMSE score reductions ranging from 3.35% to 5.67%. Additionally, the study demonstrates that XGBoost yields more accurate predictions, achieving an RMSE score of 17.07

Implementing a Web Application Screener for Preschoolers: Executive Functions and School Readiness

Web applications can be constructed to assess the executive functions and literacy skills of preschool aged children using a variety of research protocols. This work describes such a web application and its research protocol with tasks that screen inhibition, auditory and visual working memory, letter sound connection, word identification, and cognitive flexibility. The application was tested on a group of 65 preschoolers with cognitive deficits whose parents were advised to allow their children to reattend kindergarten classes and a control group of 65 typically achieving peers of similar age and gender. The results revealed that children at the age of four and five years old with cognitive deficits presented lower scores of correct answers and larger latencies in all six tasks compared to children that participated in the control group