Early detection of Alzheimer’s disease(AD)is crucial,particularly in resource-constrained medical settings.This study introduces an optimized deep learning framework that conceptualizes neural networks as computatio...Early detection of Alzheimer’s disease(AD)is crucial,particularly in resource-constrained medical settings.This study introduces an optimized deep learning framework that conceptualizes neural networks as computational“sensors”for neurodegenerative diagnosis,incorporating feature selection,selective layer unfreezing,pruning,and algorithmic optimization.An enhanced lightweight hybrid DenseNet201 model is proposed,integrating layer pruning strategies for feature selection and bioinspired optimization techniques,including Genetic Algorithm(GA)and Harris Hawks Optimization(HHO),for hyperparameter tuning.Layer pruning helps identify and eliminate less significant features,while model parameter optimization further enhances performance by fine-tuning critical hyperparameters,improving convergence speed,and maximizing classification accuracy.GA is also used to reduce the number of selected features further.A detailed comparison of six AD classification model setups is provided to illustrate the variations and their impact on performance.Applying the lightweight hybrid DenseNet201 model for MRI-based AD classification yielded an impressive baseline F1 score of 98%.Overall feature reduction reached 51.75%,enhancing interpretability and lowering processing costs.The optimized models further demonstrated perfect generalization,achieving 100%classification accuracy.These findings underscore the potential of advanced optimization techniques in developing efficient and accurate AD diagnostic tools suitable for environments with limited computational resources.展开更多
As a result of the increased number of COVID-19 cases,Ensemble Machine Learning(EML)would be an effective tool for combatting this pandemic outbreak.An ensemble of classifiers can improve the performance of single mac...As a result of the increased number of COVID-19 cases,Ensemble Machine Learning(EML)would be an effective tool for combatting this pandemic outbreak.An ensemble of classifiers can improve the performance of single machine learning(ML)classifiers,especially stacking-based ensemble learning.Stacking utilizes heterogeneous-base learners trained in parallel and combines their predictions using a meta-model to determine the final prediction results.However,building an ensemble often causes the model performance to decrease due to the increasing number of learners that are not being properly selected.Therefore,the goal of this paper is to develop and evaluate a generic,data-independent predictive method using stacked-based ensemble learning(GA-Stacking)optimized by aGenetic Algorithm(GA)for outbreak prediction and health decision aided processes.GA-Stacking utilizes five well-known classifiers,including Decision Tree(DT),Random Forest(RF),RIGID regression,Least Absolute Shrinkage and Selection Operator(LASSO),and eXtreme Gradient Boosting(XGBoost),at its first level.It also introduces GA to identify comparisons to forecast the number,combination,and trust of these base classifiers based on theMean Squared Error(MSE)as a fitness function.At the second level of the stacked ensemblemodel,a Linear Regression(LR)classifier is used to produce the final prediction.The performance of the model was evaluated using a publicly available dataset from the Center for Systems Science and Engineering,Johns Hopkins University,which consisted of 10,722 data samples.The experimental results indicated that the GA-Stacking model achieved outstanding performance with an overall accuracy of 99.99%for the three selected countries.Furthermore,the proposed model achieved good performance when compared with existing baggingbased approaches.The proposed model can be used to predict the pandemic outbreak correctly and may be applied as a generic data-independent model 3946 CMC,2023,vol.74,no.2 to predict the epidemic trend for other countries when comparing preventive and control measures.展开更多
基金supported by the Deanship of Scientific Research,Vice Presidency for Graduate Studies and Scientific Research,King Faisal University,Saudi Arabia(Grant No.KFU251428).
文摘Early detection of Alzheimer’s disease(AD)is crucial,particularly in resource-constrained medical settings.This study introduces an optimized deep learning framework that conceptualizes neural networks as computational“sensors”for neurodegenerative diagnosis,incorporating feature selection,selective layer unfreezing,pruning,and algorithmic optimization.An enhanced lightweight hybrid DenseNet201 model is proposed,integrating layer pruning strategies for feature selection and bioinspired optimization techniques,including Genetic Algorithm(GA)and Harris Hawks Optimization(HHO),for hyperparameter tuning.Layer pruning helps identify and eliminate less significant features,while model parameter optimization further enhances performance by fine-tuning critical hyperparameters,improving convergence speed,and maximizing classification accuracy.GA is also used to reduce the number of selected features further.A detailed comparison of six AD classification model setups is provided to illustrate the variations and their impact on performance.Applying the lightweight hybrid DenseNet201 model for MRI-based AD classification yielded an impressive baseline F1 score of 98%.Overall feature reduction reached 51.75%,enhancing interpretability and lowering processing costs.The optimized models further demonstrated perfect generalization,achieving 100%classification accuracy.These findings underscore the potential of advanced optimization techniques in developing efficient and accurate AD diagnostic tools suitable for environments with limited computational resources.
文摘As a result of the increased number of COVID-19 cases,Ensemble Machine Learning(EML)would be an effective tool for combatting this pandemic outbreak.An ensemble of classifiers can improve the performance of single machine learning(ML)classifiers,especially stacking-based ensemble learning.Stacking utilizes heterogeneous-base learners trained in parallel and combines their predictions using a meta-model to determine the final prediction results.However,building an ensemble often causes the model performance to decrease due to the increasing number of learners that are not being properly selected.Therefore,the goal of this paper is to develop and evaluate a generic,data-independent predictive method using stacked-based ensemble learning(GA-Stacking)optimized by aGenetic Algorithm(GA)for outbreak prediction and health decision aided processes.GA-Stacking utilizes five well-known classifiers,including Decision Tree(DT),Random Forest(RF),RIGID regression,Least Absolute Shrinkage and Selection Operator(LASSO),and eXtreme Gradient Boosting(XGBoost),at its first level.It also introduces GA to identify comparisons to forecast the number,combination,and trust of these base classifiers based on theMean Squared Error(MSE)as a fitness function.At the second level of the stacked ensemblemodel,a Linear Regression(LR)classifier is used to produce the final prediction.The performance of the model was evaluated using a publicly available dataset from the Center for Systems Science and Engineering,Johns Hopkins University,which consisted of 10,722 data samples.The experimental results indicated that the GA-Stacking model achieved outstanding performance with an overall accuracy of 99.99%for the three selected countries.Furthermore,the proposed model achieved good performance when compared with existing baggingbased approaches.The proposed model can be used to predict the pandemic outbreak correctly and may be applied as a generic data-independent model 3946 CMC,2023,vol.74,no.2 to predict the epidemic trend for other countries when comparing preventive and control measures.
