COVID-19 is one of themost highly infectious diseases ever emerged and caused by newly discovered severe acute respiratory syndrome coronavirus 2(SARS-CoV-2).It has already led the entire world to health and economic ...COVID-19 is one of themost highly infectious diseases ever emerged and caused by newly discovered severe acute respiratory syndrome coronavirus 2(SARS-CoV-2).It has already led the entire world to health and economic crisis.It has invaded the whole universe all most every way.The present study demonstrates with a nine mutually exclusive compartmental model on transmission dynamics of this pandemic disease(COVID-19),with special focus on the transmissibility of symptomatic and asymptomatic infection from susceptible individuals.Herein,the compartmental model has been investigated with mathematical analysis and computer simulations in order to understand the dynamics of COVID-19 transmission.Initially,mathematical analysis of the model has been carried out in broadly by illustrating some well-known methods including exactness,equilibrium and stability analysis in terms of basic reproduction number.We investigate the sensitivity of the model with respect to the variation of the parameters’values.Furthermore,computer simulations are performed to illustrate the results.Our analysis reveals that the death rate from coronavirus disease increases as the infection rate increases,whereas infection rate extensively decreases with the increase of quarantined individuals.The quarantined individuals also lead to increase the concentration of recovered individuals.However,the infection rate of COVID-19 increases more surprisingly as the rate of asymptomatic individuals increases than that of the symptomatic individuals.Moreover,the infection rate decreases significantly due to increase of self-immunity rate.展开更多
Background:Liver disease(LD)significantly impacts global health,requiring accurate diagnostic methods.This study aims to develop an automated system for LD prediction using machine learning(ML)and explainable artifici...Background:Liver disease(LD)significantly impacts global health,requiring accurate diagnostic methods.This study aims to develop an automated system for LD prediction using machine learning(ML)and explainable artificial intelligence(XAI),enhancing diagnostic precision and interpretability.Methods:This research systematically analyzes two distinct datasets encompassing liver health indicators.A combination of preprocessing techniques,including feature optimization methods such as Forward Feature Selection(FFS),Backward Feature Selection(BFS),and Recursive Feature Elimination(RFE),is applied to enhance data quality.After that,ML models,namely Support Vector Machines(SVM),Naive Bayes(NB),Random Forest(RF),K-nearest neighbors(KNN),Decision Trees(DT),and a novel Tree Selection and Stacking Ensemble-based RF(TSRF),are assessed in the dataset to diagnose LD.Finally,the ultimate model is selected based on incorporating cross-validation and evaluation through performance metrics like accuracy,precision,specificity,etc.,and efficient XAI methods express the ultimate model’s interoperability.Findings:The analysis reveals TSRF as the most effective model,achieving a peak accuracy of 99.92%on Dataset-1 without feature optimization and 88.88%on Dataset-2 with RFE optimization.XAI techniques,including SHAP and LIME plots,highlight key features influencing model predictions,providing insights into the reasoning behind classification outcomes.Interpretation:The findings highlight TSRF’s potential in improving LD diagnosis,using XAI to enhance transparency and trust in ML models.Despite high accuracy and interpretability,limitations such as dataset bias and lack of clinical validation remain.Future work focuses on integrating advanced XAI,diversifying datasets,and applying the approach in clinical settings for reliable diagnostics.展开更多
基金The authors greatly acknowledge the partial financial support provided by the Ministry of Science and Technology,Government of the People’s Republic of Bangladesh under special allocation in 2019–2020 with the research Grant Ref.No.39.00.0000.009.06.024.19-12/410(EAS).Supports with Ref.:17-392RG/MATHS/AS_I-FR3240297753 funded by TWAS,Italy and Ref.No.6(74)UGC/ST/Physical-17/2017/3169 funded by the UGC,Bangladesh are also acknowledged.
文摘COVID-19 is one of themost highly infectious diseases ever emerged and caused by newly discovered severe acute respiratory syndrome coronavirus 2(SARS-CoV-2).It has already led the entire world to health and economic crisis.It has invaded the whole universe all most every way.The present study demonstrates with a nine mutually exclusive compartmental model on transmission dynamics of this pandemic disease(COVID-19),with special focus on the transmissibility of symptomatic and asymptomatic infection from susceptible individuals.Herein,the compartmental model has been investigated with mathematical analysis and computer simulations in order to understand the dynamics of COVID-19 transmission.Initially,mathematical analysis of the model has been carried out in broadly by illustrating some well-known methods including exactness,equilibrium and stability analysis in terms of basic reproduction number.We investigate the sensitivity of the model with respect to the variation of the parameters’values.Furthermore,computer simulations are performed to illustrate the results.Our analysis reveals that the death rate from coronavirus disease increases as the infection rate increases,whereas infection rate extensively decreases with the increase of quarantined individuals.The quarantined individuals also lead to increase the concentration of recovered individuals.However,the infection rate of COVID-19 increases more surprisingly as the rate of asymptomatic individuals increases than that of the symptomatic individuals.Moreover,the infection rate decreases significantly due to increase of self-immunity rate.
文摘Background:Liver disease(LD)significantly impacts global health,requiring accurate diagnostic methods.This study aims to develop an automated system for LD prediction using machine learning(ML)and explainable artificial intelligence(XAI),enhancing diagnostic precision and interpretability.Methods:This research systematically analyzes two distinct datasets encompassing liver health indicators.A combination of preprocessing techniques,including feature optimization methods such as Forward Feature Selection(FFS),Backward Feature Selection(BFS),and Recursive Feature Elimination(RFE),is applied to enhance data quality.After that,ML models,namely Support Vector Machines(SVM),Naive Bayes(NB),Random Forest(RF),K-nearest neighbors(KNN),Decision Trees(DT),and a novel Tree Selection and Stacking Ensemble-based RF(TSRF),are assessed in the dataset to diagnose LD.Finally,the ultimate model is selected based on incorporating cross-validation and evaluation through performance metrics like accuracy,precision,specificity,etc.,and efficient XAI methods express the ultimate model’s interoperability.Findings:The analysis reveals TSRF as the most effective model,achieving a peak accuracy of 99.92%on Dataset-1 without feature optimization and 88.88%on Dataset-2 with RFE optimization.XAI techniques,including SHAP and LIME plots,highlight key features influencing model predictions,providing insights into the reasoning behind classification outcomes.Interpretation:The findings highlight TSRF’s potential in improving LD diagnosis,using XAI to enhance transparency and trust in ML models.Despite high accuracy and interpretability,limitations such as dataset bias and lack of clinical validation remain.Future work focuses on integrating advanced XAI,diversifying datasets,and applying the approach in clinical settings for reliable diagnostics.
