利用全球导航卫星反射信号(Global Navigation Satellite System Reflectometry,GNSS-R)进行海洋环境要素探测已成为国内外遥感领域一大热点。镜面反射点作为GNSS-R遥感探测的坐标基准,其预测的精度与速度对后续环境要素的反演有着显著...利用全球导航卫星反射信号(Global Navigation Satellite System Reflectometry,GNSS-R)进行海洋环境要素探测已成为国内外遥感领域一大热点。镜面反射点作为GNSS-R遥感探测的坐标基准,其预测的精度与速度对后续环境要素的反演有着显著影响。针对星载GNSS-R实时预测高精度镜面反射点的需求,提出了一种基于AdaGrad优化的GNSS-R镜面反射点自适应预测算法。利用空间几何关系对镜面反射点进行粗略估计,通过预处理后利用AdaGrad优化寻找镜面反射点的最优解,仿真实验证明算法的精确性、高效性。展开更多
Hyperspectral(HS)image classification is a hot research area due to challenging issues such as existence of high dimensionality,restricted training data,etc.Precise recognition of features from the HS images is importa...Hyperspectral(HS)image classification is a hot research area due to challenging issues such as existence of high dimensionality,restricted training data,etc.Precise recognition of features from the HS images is important for effective classification outcomes.Additionally,the recent advancements of deep learning(DL)models make it possible in several application areas.In addition,the performance of the DL models is mainly based on the hyperparameter setting which can be resolved by the design of metaheuristics.In this view,this article develops an automated red deer algorithm with deep learning enabled hyperspec-tral image(HSI)classification(RDADL-HIC)technique.The proposed RDADL-HIC technique aims to effectively determine the HSI images.In addition,the RDADL-HIC technique comprises a NASNetLarge model with Adagrad optimi-zer.Moreover,RDA with gated recurrent unit(GRU)approach is used for the identification and classification of HSIs.The design of Adagrad optimizer with RDA helps to optimally tune the hyperparameters of the NASNetLarge and GRU models respectively.The experimental results stated the supremacy of the RDADL-HIC model and the results are inspected interms of different measures.The comparison study of the RDADL-HIC model demonstrated the enhanced per-formance over its recent state of art approaches.展开更多
Process analytics is one of the popular research domains that advanced in the recent years.Process analytics encompasses identification,monitoring,and improvement of the processes through knowledge extraction from his...Process analytics is one of the popular research domains that advanced in the recent years.Process analytics encompasses identification,monitoring,and improvement of the processes through knowledge extraction from historical data.The evolution of Artificial Intelligence(AI)-enabled Electronic Health Records(EHRs)revolutionized the medical practice.Type 2 Diabetes Mellitus(T2DM)is a syndrome characterized by the lack of insulin secretion.If not diagnosed and managed at early stages,it may produce severe outcomes and at times,death too.Chronic Kidney Disease(CKD)and Coronary Heart Disease(CHD)are the most common,long-term and life-threatening diseases caused by T2DM.There-fore,it becomes inevitable to predict the risks of CKD and CHD in T2DM patients.The current research article presents automated Deep Learning(DL)-based Deep Neural Network(DNN)with Adagrad Optimization Algorithm i.e.,DNN-AGOA model to predict CKD and CHD risks in T2DM patients.The paper proposes a risk prediction model for T2DM patients who may develop CKD or CHD.This model helps in alarming both T2DM patients and clinicians in advance.At first,the proposed DNN-AGOA model performs data preprocessing to improve the quality of data and make it compatible for further processing.Besides,a Deep Neural Network(DNN)is employed for feature extraction,after which sigmoid function is used for classification.Further,Adagrad optimizer is applied to improve the performance of DNN model.For experimental validation,benchmark medical datasets were used and the results were validated under sev-eral dimensions.The proposed model achieved a maximum precision of 93.99%,recall of 94.63%,specificity of 73.34%,accuracy of 92.58%,and F-score of 94.22%.The results attained through experimentation established that the pro-posed DNN-AGOA model has good prediction capability over other methods.展开更多
文摘利用全球导航卫星反射信号(Global Navigation Satellite System Reflectometry,GNSS-R)进行海洋环境要素探测已成为国内外遥感领域一大热点。镜面反射点作为GNSS-R遥感探测的坐标基准,其预测的精度与速度对后续环境要素的反演有着显著影响。针对星载GNSS-R实时预测高精度镜面反射点的需求,提出了一种基于AdaGrad优化的GNSS-R镜面反射点自适应预测算法。利用空间几何关系对镜面反射点进行粗略估计,通过预处理后利用AdaGrad优化寻找镜面反射点的最优解,仿真实验证明算法的精确性、高效性。
文摘Hyperspectral(HS)image classification is a hot research area due to challenging issues such as existence of high dimensionality,restricted training data,etc.Precise recognition of features from the HS images is important for effective classification outcomes.Additionally,the recent advancements of deep learning(DL)models make it possible in several application areas.In addition,the performance of the DL models is mainly based on the hyperparameter setting which can be resolved by the design of metaheuristics.In this view,this article develops an automated red deer algorithm with deep learning enabled hyperspec-tral image(HSI)classification(RDADL-HIC)technique.The proposed RDADL-HIC technique aims to effectively determine the HSI images.In addition,the RDADL-HIC technique comprises a NASNetLarge model with Adagrad optimi-zer.Moreover,RDA with gated recurrent unit(GRU)approach is used for the identification and classification of HSIs.The design of Adagrad optimizer with RDA helps to optimally tune the hyperparameters of the NASNetLarge and GRU models respectively.The experimental results stated the supremacy of the RDADL-HIC model and the results are inspected interms of different measures.The comparison study of the RDADL-HIC model demonstrated the enhanced per-formance over its recent state of art approaches.
文摘Process analytics is one of the popular research domains that advanced in the recent years.Process analytics encompasses identification,monitoring,and improvement of the processes through knowledge extraction from historical data.The evolution of Artificial Intelligence(AI)-enabled Electronic Health Records(EHRs)revolutionized the medical practice.Type 2 Diabetes Mellitus(T2DM)is a syndrome characterized by the lack of insulin secretion.If not diagnosed and managed at early stages,it may produce severe outcomes and at times,death too.Chronic Kidney Disease(CKD)and Coronary Heart Disease(CHD)are the most common,long-term and life-threatening diseases caused by T2DM.There-fore,it becomes inevitable to predict the risks of CKD and CHD in T2DM patients.The current research article presents automated Deep Learning(DL)-based Deep Neural Network(DNN)with Adagrad Optimization Algorithm i.e.,DNN-AGOA model to predict CKD and CHD risks in T2DM patients.The paper proposes a risk prediction model for T2DM patients who may develop CKD or CHD.This model helps in alarming both T2DM patients and clinicians in advance.At first,the proposed DNN-AGOA model performs data preprocessing to improve the quality of data and make it compatible for further processing.Besides,a Deep Neural Network(DNN)is employed for feature extraction,after which sigmoid function is used for classification.Further,Adagrad optimizer is applied to improve the performance of DNN model.For experimental validation,benchmark medical datasets were used and the results were validated under sev-eral dimensions.The proposed model achieved a maximum precision of 93.99%,recall of 94.63%,specificity of 73.34%,accuracy of 92.58%,and F-score of 94.22%.The results attained through experimentation established that the pro-posed DNN-AGOA model has good prediction capability over other methods.
