Major chronic diseases such as Cardiovascular Disease(CVD),diabetes,and cancer impose a significant burden on people and healthcare systems around the globe.Recently,Deep Learning(DL)has shown great potential for the ...Major chronic diseases such as Cardiovascular Disease(CVD),diabetes,and cancer impose a significant burden on people and healthcare systems around the globe.Recently,Deep Learning(DL)has shown great potential for the development of intelligentmobile Health(mHealth)interventions for chronic diseases that could revolutionize the delivery of health care anytime,anywhere.The aimof this study is to present a systematic review of studies that have used DL based on mHealth data for the diagnosis,prognosis,management,and treatment of major chronic diseases and advance our understanding of the progress made in this rapidly developing field.Type 2 Diabetes Mellitus(T2DMs)is a regular chronic disorder that is caused by the secretion of insulin,which leads to serious death-related issues and the most complicated ones.Coronary Heart Disease(CHD)is the most frequent issue related to T2DM patients.The major concern is recognizing the high possibility of CHD complications,yet the model is not available to identify it.This work introduces a deep learning technique that can predict heart disease effectively using a hybrid model,which integrates DNNs(Deep Neural Networks)with a Multi-Head Attention Model called MADNN.The scheme canbedesignedtoautomatically learnthe best-quality features fromElectronic Health Records(EHRs),and effectively combine heterogeneous and time-sequencedmedical data for predicting the risk of CVD.The analysis is done using the Kaggle dataset.The outcomes prove that the MADNN has improved accuracy by about 95%and indicates the precise accuracy is higher for the disease compared with SVM,CNN and ANN.展开更多
双电源配电环网是实现配电网故障无缝自愈、有效解决短时停电问题的基础,但配电环网中潮流呈自然分布,且缺乏有效的调节手段,统一潮流控制器(unified power flowcontroller,UPFC)可对环网潮流进行控制,但需突破其经济性瓶颈。为此,在调...双电源配电环网是实现配电网故障无缝自愈、有效解决短时停电问题的基础,但配电环网中潮流呈自然分布,且缺乏有效的调节手段,统一潮流控制器(unified power flowcontroller,UPFC)可对环网潮流进行控制,但需突破其经济性瓶颈。为此,在调节功率一定时,以负荷节点电压与环网两端馈线出力极限为约束条件,分别以UPFC输出的有功功率和视在功率最小为目标,提出了潮流控制策略。仿真结果表明2种优化控制策略能够满足配电环网潮流调度要求,且兼顾了UPFC应用的经济性。展开更多
文摘Major chronic diseases such as Cardiovascular Disease(CVD),diabetes,and cancer impose a significant burden on people and healthcare systems around the globe.Recently,Deep Learning(DL)has shown great potential for the development of intelligentmobile Health(mHealth)interventions for chronic diseases that could revolutionize the delivery of health care anytime,anywhere.The aimof this study is to present a systematic review of studies that have used DL based on mHealth data for the diagnosis,prognosis,management,and treatment of major chronic diseases and advance our understanding of the progress made in this rapidly developing field.Type 2 Diabetes Mellitus(T2DMs)is a regular chronic disorder that is caused by the secretion of insulin,which leads to serious death-related issues and the most complicated ones.Coronary Heart Disease(CHD)is the most frequent issue related to T2DM patients.The major concern is recognizing the high possibility of CHD complications,yet the model is not available to identify it.This work introduces a deep learning technique that can predict heart disease effectively using a hybrid model,which integrates DNNs(Deep Neural Networks)with a Multi-Head Attention Model called MADNN.The scheme canbedesignedtoautomatically learnthe best-quality features fromElectronic Health Records(EHRs),and effectively combine heterogeneous and time-sequencedmedical data for predicting the risk of CVD.The analysis is done using the Kaggle dataset.The outcomes prove that the MADNN has improved accuracy by about 95%and indicates the precise accuracy is higher for the disease compared with SVM,CNN and ANN.
文摘双电源配电环网是实现配电网故障无缝自愈、有效解决短时停电问题的基础,但配电环网中潮流呈自然分布,且缺乏有效的调节手段,统一潮流控制器(unified power flowcontroller,UPFC)可对环网潮流进行控制,但需突破其经济性瓶颈。为此,在调节功率一定时,以负荷节点电压与环网两端馈线出力极限为约束条件,分别以UPFC输出的有功功率和视在功率最小为目标,提出了潮流控制策略。仿真结果表明2种优化控制策略能够满足配电环网潮流调度要求,且兼顾了UPFC应用的经济性。
