Mobile clouds are the most common medium for aggregating,storing,and analyzing data from the medical Internet of Things(MIoT).It is employed to monitor a patient’s essential health signs for earlier disease diagnosis...Mobile clouds are the most common medium for aggregating,storing,and analyzing data from the medical Internet of Things(MIoT).It is employed to monitor a patient’s essential health signs for earlier disease diagnosis and prediction.Among the various disease,skin cancer was the wide variety of cancer,as well as enhances the endurance rate.In recent years,many skin cancer classification systems using machine and deep learning models have been developed for classifying skin tumors,including malignant melanoma(MM)and other skin cancers.However,accurate cancer detection was not performed with minimum time consumption.In order to address these existing problems,a novel Multidimensional Bregman Divergencive Feature Scaling Based Cophenetic Piecewise Regression Recurrent Deep Learning Classification(MBDFS-CPRRDLC)technique is introduced for detecting cancer at an earlier stage.The MBDFS-CPRRDLC performs skin cancer detection using different layers such as input,hidden,and output for feature selection and classification.The patient information is composed of IoT.The patient information was stored in mobile clouds server for performing predictive analytics.The collected data are sent to the recurrent deep learning classifier.In the first hidden layer,the feature selection process is carried out using the Multidimensional Bregman Divergencive Feature Scaling technique to find the significant features for disease identification resulting in decreases time consumption.Followed by,the disease classification is carried out in the second hidden layer using cophenetic correlative piecewise regression for analyzing the testing and training data.This process is repeatedly performed until the error gets minimized.In this way,disease classification is accurately performed with higher accuracy.Experimental evaluation is carried out for factors namely Accuracy,precision,recall,F-measure,as well as cancer detection time,by the amount of patient data.The observed result confirms that the proposed MBDFS-CPRRDLC technique increases accur展开更多
公共楼宇是智能电网用电环节需求响应的重要组成部分,在强不确定性环境下,为了提高公共楼宇短期负荷预测的精度,并能更好反映楼宇负荷的不确定性。提出了一种集合多维尺度分析技术(multidimensional scaling,MDS),基于Copula函数相关性...公共楼宇是智能电网用电环节需求响应的重要组成部分,在强不确定性环境下,为了提高公共楼宇短期负荷预测的精度,并能更好反映楼宇负荷的不确定性。提出了一种集合多维尺度分析技术(multidimensional scaling,MDS),基于Copula函数相关性测度、长短期记忆网络分位数回归(quantile regression long short-term memory,QRLSTM)和核密度估计(kernel density estimation,KDE)的短期公共楼宇负荷概率密度预测的方法。首先采用MDS技术对楼宇群进行初步划分,再通过基于Copula函数的相关性测度方法定量计算影响因素(外界天气、人类活动)与目标楼宇负荷的相关程度;其次,运用QRLSTM回归模型预测未来不同分位数上的负荷值。最后,通过核密度估计得到未来任意时刻预测点的概率密度函数。实验结果表明,综合考虑强相关影响因素,并结合QRLSTM回归和KDE技术,能够更好地解决短期公共楼宇负荷概率密度预测问题。展开更多
基金This research is funded by Princess Nourah Bint Abdulrahman University Researchers Supporting Project number(PNURSP2022R194)Princess Nourah Bint Abdulrahman University,Riyadh,Saudi Arabia.
文摘Mobile clouds are the most common medium for aggregating,storing,and analyzing data from the medical Internet of Things(MIoT).It is employed to monitor a patient’s essential health signs for earlier disease diagnosis and prediction.Among the various disease,skin cancer was the wide variety of cancer,as well as enhances the endurance rate.In recent years,many skin cancer classification systems using machine and deep learning models have been developed for classifying skin tumors,including malignant melanoma(MM)and other skin cancers.However,accurate cancer detection was not performed with minimum time consumption.In order to address these existing problems,a novel Multidimensional Bregman Divergencive Feature Scaling Based Cophenetic Piecewise Regression Recurrent Deep Learning Classification(MBDFS-CPRRDLC)technique is introduced for detecting cancer at an earlier stage.The MBDFS-CPRRDLC performs skin cancer detection using different layers such as input,hidden,and output for feature selection and classification.The patient information is composed of IoT.The patient information was stored in mobile clouds server for performing predictive analytics.The collected data are sent to the recurrent deep learning classifier.In the first hidden layer,the feature selection process is carried out using the Multidimensional Bregman Divergencive Feature Scaling technique to find the significant features for disease identification resulting in decreases time consumption.Followed by,the disease classification is carried out in the second hidden layer using cophenetic correlative piecewise regression for analyzing the testing and training data.This process is repeatedly performed until the error gets minimized.In this way,disease classification is accurately performed with higher accuracy.Experimental evaluation is carried out for factors namely Accuracy,precision,recall,F-measure,as well as cancer detection time,by the amount of patient data.The observed result confirms that the proposed MBDFS-CPRRDLC technique increases accur
文摘公共楼宇是智能电网用电环节需求响应的重要组成部分,在强不确定性环境下,为了提高公共楼宇短期负荷预测的精度,并能更好反映楼宇负荷的不确定性。提出了一种集合多维尺度分析技术(multidimensional scaling,MDS),基于Copula函数相关性测度、长短期记忆网络分位数回归(quantile regression long short-term memory,QRLSTM)和核密度估计(kernel density estimation,KDE)的短期公共楼宇负荷概率密度预测的方法。首先采用MDS技术对楼宇群进行初步划分,再通过基于Copula函数的相关性测度方法定量计算影响因素(外界天气、人类活动)与目标楼宇负荷的相关程度;其次,运用QRLSTM回归模型预测未来不同分位数上的负荷值。最后,通过核密度估计得到未来任意时刻预测点的概率密度函数。实验结果表明,综合考虑强相关影响因素,并结合QRLSTM回归和KDE技术,能够更好地解决短期公共楼宇负荷概率密度预测问题。
