This research explores the use of Fuzzy K-Nearest Neighbor(F-KNN)and Artificial Neural Networks(ANN)for predicting heart stroke incidents,focusing on the impact of feature selection methods,specifically Chi-Square and...This research explores the use of Fuzzy K-Nearest Neighbor(F-KNN)and Artificial Neural Networks(ANN)for predicting heart stroke incidents,focusing on the impact of feature selection methods,specifically Chi-Square and Best First Search(BFS).The study demonstrates that BFS significantly enhances the performance of both classifiers.With BFS preprocessing,the ANN model achieved an impressive accuracy of 97.5%,precision and recall of 97.5%,and an Receiver Operating Characteristics(ROC)area of 97.9%,outperforming the Chi-Square-based ANN,which recorded an accuracy of 91.4%.Similarly,the F-KNN model with BFS achieved an accuracy of 96.3%,precision and recall of 96.3%,and a Receiver Operating Characteristics(ROC)area of 96.2%,surpassing the performance of the Chi-Square F-KNN model,which showed an accuracy of 95%.These results highlight that BFS improves the ability to select the most relevant features,contributing to more reliable and accurate stroke predictions.The findings underscore the importance of using advanced feature selection methods like BFS to enhance the performance of machine learning models in healthcare applications,leading to better stroke risk management and improved patient outcomes.展开更多
We study the carbon dioxide reduction reaction(CO_(2)RR)activity and selectivity of Fe single-atom catalyst(Fe-SAC)and Fe dual-atom catalyst(Fe-DAC)active sites at the interior of graphene and the edges of graphitic n...We study the carbon dioxide reduction reaction(CO_(2)RR)activity and selectivity of Fe single-atom catalyst(Fe-SAC)and Fe dual-atom catalyst(Fe-DAC)active sites at the interior of graphene and the edges of graphitic nanopore by using a combination of DFT calculations and microkinetic simulations.The trend of limiting potentials for CO_(2)RR to produce CO can be described by using either the adsorption energy of COOH,CO,or their combination.CO_(2)RR process with reasonable reaction rates can be achieved only on the active site configurations with weak tendencies toward CO poisoning.The efficiency of CO_(2)RR on a catalyst depends on its ability to suppress the parasitic hydrogen evolution reaction(HER),which is directly related to the behavior of H adsorption on the catalyst’s active site.We find that the edges of the graphitic nanopore can act as potential adsorption sites for an H atom,and in some cases,the edge site can bind the H atom much stronger than the main Fe site.The linear scaling between CO and H adsorptions is broken if this condition is met.This condition also allows some edge active site configurations to have their CO_(2)RR limiting potential lower than the HER process favoring CO production over H2 production.展开更多
基金funded by FCT/MECI through national funds and,when applicable,co-funded EU funds under UID/50008:Instituto de Telecomunicacoes.
文摘This research explores the use of Fuzzy K-Nearest Neighbor(F-KNN)and Artificial Neural Networks(ANN)for predicting heart stroke incidents,focusing on the impact of feature selection methods,specifically Chi-Square and Best First Search(BFS).The study demonstrates that BFS significantly enhances the performance of both classifiers.With BFS preprocessing,the ANN model achieved an impressive accuracy of 97.5%,precision and recall of 97.5%,and an Receiver Operating Characteristics(ROC)area of 97.9%,outperforming the Chi-Square-based ANN,which recorded an accuracy of 91.4%.Similarly,the F-KNN model with BFS achieved an accuracy of 96.3%,precision and recall of 96.3%,and a Receiver Operating Characteristics(ROC)area of 96.2%,surpassing the performance of the Chi-Square F-KNN model,which showed an accuracy of 95%.These results highlight that BFS improves the ability to select the most relevant features,contributing to more reliable and accurate stroke predictions.The findings underscore the importance of using advanced feature selection methods like BFS to enhance the performance of machine learning models in healthcare applications,leading to better stroke risk management and improved patient outcomes.
基金supported by the Ministry of Education,Culture,Research,and Technology of the Republic of Indonesia through the‘WCR 2022’program under contract number 007/E5/PG.02.00.PT/2022.
文摘We study the carbon dioxide reduction reaction(CO_(2)RR)activity and selectivity of Fe single-atom catalyst(Fe-SAC)and Fe dual-atom catalyst(Fe-DAC)active sites at the interior of graphene and the edges of graphitic nanopore by using a combination of DFT calculations and microkinetic simulations.The trend of limiting potentials for CO_(2)RR to produce CO can be described by using either the adsorption energy of COOH,CO,or their combination.CO_(2)RR process with reasonable reaction rates can be achieved only on the active site configurations with weak tendencies toward CO poisoning.The efficiency of CO_(2)RR on a catalyst depends on its ability to suppress the parasitic hydrogen evolution reaction(HER),which is directly related to the behavior of H adsorption on the catalyst’s active site.We find that the edges of the graphitic nanopore can act as potential adsorption sites for an H atom,and in some cases,the edge site can bind the H atom much stronger than the main Fe site.The linear scaling between CO and H adsorptions is broken if this condition is met.This condition also allows some edge active site configurations to have their CO_(2)RR limiting potential lower than the HER process favoring CO production over H2 production.
