The Tunicate Swarm Algorithm(TSA)inspires by simulating the lives of Tunicates at sea and how food is obtained.This algorithm is easily entrapped to local optimization despite the simplicity and optimal,leading to ear...The Tunicate Swarm Algorithm(TSA)inspires by simulating the lives of Tunicates at sea and how food is obtained.This algorithm is easily entrapped to local optimization despite the simplicity and optimal,leading to early convergence compared to some metaheuristic algorithms.This paper sought to improve this algorithm's performance using mutating operators such as the lévy mutation operator,the Cauchy mutation operator,and the Gaussian mutation operator for global optimization problems.Thus,we introduced a version of this algorithm called the QLGCTSA algorithm.Each of these operators has a different performance,increasing the QLGCTSA algorithm performance at a specific optimization operation stage.This algorithm has been run on benchmark functions,including three different compositions,unimodal(UM),and multimodal(MM)groups and its performance evaluate six large-scale engineering problems.Experimental results show that the QLGCTSA algorithm had outperformed other competing optimization algorithms.展开更多
Heart disease prediction is a critical issue in healthcare,where accurate early diagnosis can save lives and reduce healthcare costs.The problem is inherently complex due to the high dimensionality of medical data,irr...Heart disease prediction is a critical issue in healthcare,where accurate early diagnosis can save lives and reduce healthcare costs.The problem is inherently complex due to the high dimensionality of medical data,irrelevant or redundant features,and the variability in risk factors such as age,lifestyle,andmedical history.These challenges often lead to inefficient and less accuratemodels.Traditional predictionmethodologies face limitations in effectively handling large feature sets and optimizing classification performance,which can result in overfitting poor generalization,and high computational cost.This work proposes a novel classification model for heart disease prediction that addresses these challenges by integrating feature selection through a Genetic Algorithm(GA)with an ensemble deep learning approach optimized using the Tunicate Swarm Algorithm(TSA).GA selects the most relevant features,reducing dimensionality and improvingmodel efficiency.Theselected features are then used to train an ensemble of deep learning models,where the TSA optimizes the weight of each model in the ensemble to enhance prediction accuracy.This hybrid approach addresses key challenges in the field,such as high dimensionality,redundant features,and classification performance,by introducing an efficient feature selection mechanism and optimizing the weighting of deep learning models in the ensemble.These enhancements result in a model that achieves superior accuracy,generalization,and efficiency compared to traditional methods.The proposed model demonstrated notable advancements in both prediction accuracy and computational efficiency over traditionalmodels.Specifically,it achieved an accuracy of 97.5%,a sensitivity of 97.2%,and a specificity of 97.8%.Additionally,with a 60-40 data split and 5-fold cross-validation,the model showed a significant reduction in training time(90 s),memory consumption(950 MB),and CPU usage(80%),highlighting its effectiveness in processing large,complex medical datasets for heart disease prediction.展开更多
文摘The Tunicate Swarm Algorithm(TSA)inspires by simulating the lives of Tunicates at sea and how food is obtained.This algorithm is easily entrapped to local optimization despite the simplicity and optimal,leading to early convergence compared to some metaheuristic algorithms.This paper sought to improve this algorithm's performance using mutating operators such as the lévy mutation operator,the Cauchy mutation operator,and the Gaussian mutation operator for global optimization problems.Thus,we introduced a version of this algorithm called the QLGCTSA algorithm.Each of these operators has a different performance,increasing the QLGCTSA algorithm performance at a specific optimization operation stage.This algorithm has been run on benchmark functions,including three different compositions,unimodal(UM),and multimodal(MM)groups and its performance evaluate six large-scale engineering problems.Experimental results show that the QLGCTSA algorithm had outperformed other competing optimization algorithms.
文摘Heart disease prediction is a critical issue in healthcare,where accurate early diagnosis can save lives and reduce healthcare costs.The problem is inherently complex due to the high dimensionality of medical data,irrelevant or redundant features,and the variability in risk factors such as age,lifestyle,andmedical history.These challenges often lead to inefficient and less accuratemodels.Traditional predictionmethodologies face limitations in effectively handling large feature sets and optimizing classification performance,which can result in overfitting poor generalization,and high computational cost.This work proposes a novel classification model for heart disease prediction that addresses these challenges by integrating feature selection through a Genetic Algorithm(GA)with an ensemble deep learning approach optimized using the Tunicate Swarm Algorithm(TSA).GA selects the most relevant features,reducing dimensionality and improvingmodel efficiency.Theselected features are then used to train an ensemble of deep learning models,where the TSA optimizes the weight of each model in the ensemble to enhance prediction accuracy.This hybrid approach addresses key challenges in the field,such as high dimensionality,redundant features,and classification performance,by introducing an efficient feature selection mechanism and optimizing the weighting of deep learning models in the ensemble.These enhancements result in a model that achieves superior accuracy,generalization,and efficiency compared to traditional methods.The proposed model demonstrated notable advancements in both prediction accuracy and computational efficiency over traditionalmodels.Specifically,it achieved an accuracy of 97.5%,a sensitivity of 97.2%,and a specificity of 97.8%.Additionally,with a 60-40 data split and 5-fold cross-validation,the model showed a significant reduction in training time(90 s),memory consumption(950 MB),and CPU usage(80%),highlighting its effectiveness in processing large,complex medical datasets for heart disease prediction.
