Human fall detection(FD)acts as an important part in creating sensor based alarm system,enabling physical therapists to minimize the effect of fall events and save human lives.Generally,elderly people suffer from seve...Human fall detection(FD)acts as an important part in creating sensor based alarm system,enabling physical therapists to minimize the effect of fall events and save human lives.Generally,elderly people suffer from several diseases,and fall action is a common situation which can occur at any time.In this view,this paper presents an Improved Archimedes Optimization Algorithm with Deep Learning Empowered Fall Detection(IAOA-DLFD)model to identify the fall/non-fall events.The proposed IAOA-DLFD technique comprises different levels of pre-processing to improve the input image quality.Besides,the IAOA with Capsule Network based feature extractor is derived to produce an optimal set of feature vectors.In addition,the IAOA uses to significantly boost the overall FD performance by optimal choice of CapsNet hyperparameters.Lastly,radial basis function(RBF)network is applied for determining the proper class labels of the test images.To showcase the enhanced performance of the IAOA-DLFD technique,a wide range of experiments are executed and the outcomes stated the enhanced detection outcome of the IAOA-DLFD approach over the recent methods with the accuracy of 0.997.展开更多
In this paper,we consider the NP-hard problem of finding the minimum dominant resolving set of graphs.A vertex set B of a connected graph G resolves G if every vertex of G is uniquely identified by its vector of dista...In this paper,we consider the NP-hard problem of finding the minimum dominant resolving set of graphs.A vertex set B of a connected graph G resolves G if every vertex of G is uniquely identified by its vector of distances to the vertices in B.A resolving set is dominating if every vertex of G that does not belong to B is a neighbor to some vertices in B.The dominant metric dimension of G is the cardinality number of the minimum dominant resolving set.The dominant metric dimension is computed by a binary version of the Archimedes optimization algorithm(BAOA).The objects of BAOA are binary encoded and used to represent which one of the vertices of the graph belongs to the dominant resolving set.The feasibility is enforced by repairing objects such that an additional vertex generated from vertices of G is added to B and this repairing process is iterated until B becomes the dominant resolving set.This is the first attempt to determine the dominant metric dimension problem heuristically.The proposed BAOA is compared to binary whale optimization(BWOA)and binary particle optimization(BPSO)algorithms.Computational results confirm the superiority of the BAOA for computing the dominant metric dimension.展开更多
目的基于改进的机器学习模型建立重症急性胰腺炎诊断的早期预测模型,并分析其临床价值。方法纳入2014年1月至2023年8月陆军特色医学中心消化内科、肝胆外科以及联勤保障部队第九四五医院急诊与重症医学科收治的352例急性胰腺炎患者,根...目的基于改进的机器学习模型建立重症急性胰腺炎诊断的早期预测模型,并分析其临床价值。方法纳入2014年1月至2023年8月陆军特色医学中心消化内科、肝胆外科以及联勤保障部队第九四五医院急诊与重症医学科收治的352例急性胰腺炎患者,根据病情严重程度将其分为重症组(n=88)和非重症组(n=264),开展病例对照研究。利用RUSBoost模型以及改进的阿基米德优化算法,分析入院48 h内的39项常规实验室生化指标,帮助构建重症急性胰腺炎早期诊断预测模型,同步完成特征筛选和超参数优化,并利用ReliefF算法特征重要性排序和Logistic多因素分析,对筛选出的特征进行价值分析。结果在训练集上,改进机器学习模型的曲线下面积(area under curve,AUC)为0.922;在测试集上,改进机器学习模型的AUC达到了0.888。基于改进机器学习模型筛选出的预测重症急性胰腺炎发生的4个关键特征分别为C反应蛋白、血氯、血镁、纤维蛋白原水平,与ReliefF算法特征重要性排序和Logistic多因素分析结果相吻合。结论应用改进机器学习模型分析实验室检查结果,可帮助临床早期预测重症急性胰腺炎的发生。展开更多
基金supported by Taif University Researchers Supporting Program(Project Number:TURSP-2020/195),Taif University,Saudi ArabiaThe authors extend their appreciation to the Deanship of Scientific Research at King Khalid University for funding this work under Grant Number(RGP 2/209/42)Princess Nourah bint Abdulrahman University Researchers Supporting Project Number(PNURSP2022R234),Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.
文摘Human fall detection(FD)acts as an important part in creating sensor based alarm system,enabling physical therapists to minimize the effect of fall events and save human lives.Generally,elderly people suffer from several diseases,and fall action is a common situation which can occur at any time.In this view,this paper presents an Improved Archimedes Optimization Algorithm with Deep Learning Empowered Fall Detection(IAOA-DLFD)model to identify the fall/non-fall events.The proposed IAOA-DLFD technique comprises different levels of pre-processing to improve the input image quality.Besides,the IAOA with Capsule Network based feature extractor is derived to produce an optimal set of feature vectors.In addition,the IAOA uses to significantly boost the overall FD performance by optimal choice of CapsNet hyperparameters.Lastly,radial basis function(RBF)network is applied for determining the proper class labels of the test images.To showcase the enhanced performance of the IAOA-DLFD technique,a wide range of experiments are executed and the outcomes stated the enhanced detection outcome of the IAOA-DLFD approach over the recent methods with the accuracy of 0.997.
文摘In this paper,we consider the NP-hard problem of finding the minimum dominant resolving set of graphs.A vertex set B of a connected graph G resolves G if every vertex of G is uniquely identified by its vector of distances to the vertices in B.A resolving set is dominating if every vertex of G that does not belong to B is a neighbor to some vertices in B.The dominant metric dimension of G is the cardinality number of the minimum dominant resolving set.The dominant metric dimension is computed by a binary version of the Archimedes optimization algorithm(BAOA).The objects of BAOA are binary encoded and used to represent which one of the vertices of the graph belongs to the dominant resolving set.The feasibility is enforced by repairing objects such that an additional vertex generated from vertices of G is added to B and this repairing process is iterated until B becomes the dominant resolving set.This is the first attempt to determine the dominant metric dimension problem heuristically.The proposed BAOA is compared to binary whale optimization(BWOA)and binary particle optimization(BPSO)algorithms.Computational results confirm the superiority of the BAOA for computing the dominant metric dimension.
文摘目的基于改进的机器学习模型建立重症急性胰腺炎诊断的早期预测模型,并分析其临床价值。方法纳入2014年1月至2023年8月陆军特色医学中心消化内科、肝胆外科以及联勤保障部队第九四五医院急诊与重症医学科收治的352例急性胰腺炎患者,根据病情严重程度将其分为重症组(n=88)和非重症组(n=264),开展病例对照研究。利用RUSBoost模型以及改进的阿基米德优化算法,分析入院48 h内的39项常规实验室生化指标,帮助构建重症急性胰腺炎早期诊断预测模型,同步完成特征筛选和超参数优化,并利用ReliefF算法特征重要性排序和Logistic多因素分析,对筛选出的特征进行价值分析。结果在训练集上,改进机器学习模型的曲线下面积(area under curve,AUC)为0.922;在测试集上,改进机器学习模型的AUC达到了0.888。基于改进机器学习模型筛选出的预测重症急性胰腺炎发生的4个关键特征分别为C反应蛋白、血氯、血镁、纤维蛋白原水平,与ReliefF算法特征重要性排序和Logistic多因素分析结果相吻合。结论应用改进机器学习模型分析实验室检查结果,可帮助临床早期预测重症急性胰腺炎的发生。
