Cloud infrastructural resource optimization is the process of precisely selecting the allocating the correct resources either to a workload or application.When workload execution,accuracy,and cost are accurately stabi...Cloud infrastructural resource optimization is the process of precisely selecting the allocating the correct resources either to a workload or application.When workload execution,accuracy,and cost are accurately stabilized in opposition to the best possible framework in real-time,efficiency is attained.In addition,every workload or application required for the framework is characteristic and these essentials change over time.But,the existing method was failed to ensure the high Quality of Service(QoS).In order to address this issue,a Tricube Weighted Linear Regression-based Inter Quartile(TWLR-IQ)for Cloud Infrastructural Resource Optimization is introduced.A Tricube Weighted Linear Regression is presented in the proposed method to estimate the resources(i.e.,CPU,RAM,and network bandwidth utilization)based on the usage history in each cloud server.Then,Inter Quartile Range is applied to efficiently predict the overload hosts for ensuring a smooth migration.Experimental results show that our proposed method is better than the approach in Cloudsim under various performance metrics.The results clearly showed that the proposed method can reduce the energy consumption and provide a high level of commitment with ensuring the minimum number of Virtual Machine(VM)Migrations as compared to the state-of-the-art methods.展开更多
Aiming at the problems of low accuracy,long time consumption,and failure to obtain quantita-tive fault identification results of existing automatic fault identification technic,a fault recognition method based on clus...Aiming at the problems of low accuracy,long time consumption,and failure to obtain quantita-tive fault identification results of existing automatic fault identification technic,a fault recognition method based on clustering linear regression is proposed.Firstly,Hough transform is used to detect the line segment of the enhanced image obtained by the coherence cube algorithm.Secondly,the endpoint of the line segment detected by Hough transform is taken as the key point,and the adaptive clustering linear regression algorithm is used to cluster the key points adaptively according to the lin-ear relationship between them.Finally,a fault is generated from each category of key points based on least squares curve fitting method to realize fault identification.To verify the feasibility and pro-gressiveness of the proposed method,it is compared with the traditional method and the latest meth-od on the actual seismic data through experiments,and the effectiveness of the proposed method is verified by the experimental results on the actual seismic data.展开更多
A Mobile Ad-hoc NETwork(MANET)contains numerous mobile nodes,and it forms a structure-less network associated with wireless links.But,the node movement is the key feature of MANETs;hence,the quick action of the nodes ...A Mobile Ad-hoc NETwork(MANET)contains numerous mobile nodes,and it forms a structure-less network associated with wireless links.But,the node movement is the key feature of MANETs;hence,the quick action of the nodes guides a link failure.This link failure creates more data packet drops that can cause a long time delay.As a result,measuring accurate link failure time is the key factor in the MANET.This paper presents a Fuzzy Linear Regression Method to measure Link Failure(FLRLF)and provide an optimal route in the MANET-Internet of Things(IoT).This work aims to predict link failure and improve routing efficiency in MANET.The Fuzzy Linear Regression Method(FLRM)measures the long lifespan link based on the link failure.The mobile node group is built by the Received Signal Strength(RSS).The Hill Climbing(HC)method selects the Group Leader(GL)based on node mobility,node degree and node energy.Additionally,it uses a Data Gathering node forward the infor-mation from GL to the sink node through multiple GL.The GL is identified by linking lifespan and energy using the Particle Swarm Optimization(PSO)algo-rithm.The simulation results demonstrate that the FLRLF approach increases the GL lifespan and minimizes the link failure time in the MANET.展开更多
The development of prediction supports is a critical step in information systems engineering in this era defined by the knowledge economy, the hub of which is big data. Currently, the lack of a predictive model, wheth...The development of prediction supports is a critical step in information systems engineering in this era defined by the knowledge economy, the hub of which is big data. Currently, the lack of a predictive model, whether qualitative or quantitative, depending on a company’s areas of intervention can handicap or weaken its competitive capacities, endangering its survival. In terms of quantitative prediction, depending on the efficacy criteria, a variety of methods and/or tools are available. The multiple linear regression method is one of the methods used for this purpose. A linear regression model is a regression model of an explained variable on one or more explanatory variables in which the function that links the explanatory variables to the explained variable has linear parameters. The purpose of this work is to demonstrate how to use multiple linear regressions, which is one aspect of decisional mathematics. The use of multiple linear regressions on random data, which can be replaced by real data collected by or from organizations, provides decision makers with reliable data knowledge. As a result, machine learning methods can provide decision makers with relevant and trustworthy data. The main goal of this article is therefore to define the objective function on which the influencing factors for its optimization will be defined using the linear regression method.展开更多
Social network is the mainstream medium of current information dissemination,and it is particularly important to accurately predict its propagation law.In this paper,we introduce a social network propagation model int...Social network is the mainstream medium of current information dissemination,and it is particularly important to accurately predict its propagation law.In this paper,we introduce a social network propagation model integrating multiple linear regression and infectious disease model.Firstly,we proposed the features that affect social network communication from three dimensions.Then,we predicted the node influence via multiple linear regression.Lastly,we used the node influence as the state transition of the infectious disease model to predict the trend of information dissemination in social networks.The experimental results on a real social network dataset showed that the prediction results of the model are consistent with the actual information dissemination trends.展开更多
Understanding the spatial-temporal dynamics of crop nitrogen(N)use efficiency(NUE)and the relationship with explanatory environmental variables can support land-use management and policymaking.Nevertheless,the applica...Understanding the spatial-temporal dynamics of crop nitrogen(N)use efficiency(NUE)and the relationship with explanatory environmental variables can support land-use management and policymaking.Nevertheless,the application of statistical models for evaluating the explanatory variables of space-time variation in crop NUE is still under-researched.In this study,stepwise multiple linear regression(SMLR)and Random Forest(RF)were used to evaluate the spatial and temporal variation of NUE indicators(i.e.,partial factor productivity of N(PFPN);partial nutrient balance of N(PNBN))at county scale in Northeast China(Heilongjiang,Liaoning and Jilin provinces)from 1990 to 2015.Explanatory variables included agricultural management practices,topography,climate,economy,soil and crop types.Results revealed that the PFPN was higher in the northern parts and lower in the center of the Northeast China and PNBN increased from southern to northern parts during the 1990–2015 period.The NUE indicators decreased with time in most counties during the study period.The model efficiency coefficients of the SMLR and RF models were 0.44 and 0.84 for PFPN,and 0.67 and 0.89 for PNBN,respectively.The RF model had higher relative importance of soil and climatic covariates and lower relative importance of crop covariates compared to the SMLR model.The planting area index of vegetables and beans,soil clay content,saturated water content,enhanced vegetation index in November&December,soil bulk density,and annual minimum temperature were the main explanatory variables for both NUE indicators.This is the first study to show the quantitative relative importance of explanatory variables for NUE at a county level in Northeast China using RF and SMLR.This novel study gives reference measurements to improve crop NUE which is one of the most effective means of managing N for sustainable development,ensuring food security,alleviating environmental degradation and increasing farmer’s profitability.展开更多
The El Nino-Southern Oscillation(ENSO)has great impacts on the Indian Ocean sea surface temperature(SST).In fact,two major modes of the Indian Ocean SST namely the Indian Ocean Basin(IOB)and the Indian Ocean Dipole(IO...The El Nino-Southern Oscillation(ENSO)has great impacts on the Indian Ocean sea surface temperature(SST).In fact,two major modes of the Indian Ocean SST namely the Indian Ocean Basin(IOB)and the Indian Ocean Dipole(IOD)modes,exerting strong influences on the Indian Ocean rim countries,are both influenced by the ENSO.Based on a combined linear regression method,this study quantifies the ENSO impacts on the IOB and the IOD during ENSO concurrent,developing,and decaying stages.After removing the ENSO impacts,the spring peak of the IOB disappears along with significant decrease in number of events,while the number of events is only slightly reduced and the autumn peak remains for the IOD.By isolating the ENSO impacts during each stage,this study reveals that the leading impacts of ENSO contribute to the IOD development,while the delayed impacts facilitate the IOD phase switch and prompt the IOB development.Besides,the decadal variations of ENSO impacts are various during each stage and over different regions.These imply that merely removing the concurrent ENSO impacts would not be sufficient to investigate intrinsic climate variability of the Indian Ocean,and the present method may be useful to study climate variabilities independent of ENSO.展开更多
This paper transforms fuzzy number into clear number using the centroid method, thus we can research the traditional linear regression model which is transformed from the fuzzy linear regression model. The model’s in...This paper transforms fuzzy number into clear number using the centroid method, thus we can research the traditional linear regression model which is transformed from the fuzzy linear regression model. The model’s input and output are fuzzy numbers, and the regression coefficients are clear numbers. This paper considers the parameter estimation and impact analysis based on data deletion. Through the study of example and comparison with other models, it can be concluded that the model in this paper is applied easily and better.展开更多
Predicting the value of one or more variables using the values of other variables is a very important process in the various engineering experiments that include large data that are difficult to obtain using different...Predicting the value of one or more variables using the values of other variables is a very important process in the various engineering experiments that include large data that are difficult to obtain using different measurement processes.Regression is one of the most important types of supervised machine learning,in which labeled data is used to build a prediction model,regression can be classified into three different categories:linear,polynomial,and logistic.In this research paper,different methods will be implemented to solve the linear regression problem,where there is a linear relationship between the target and the predicted output.Various methods for linear regression will be analyzed using the calculated Mean Square Error(MSE)between the target values and the predicted outputs.A huge set of regression samples will be used to construct the training dataset with selected sizes.A detailed comparison will be performed between three methods,including least-square fit;Feed-Forward Artificial Neural Network(FFANN),and Cascade Feed-Forward Artificial Neural Network(CFFANN),and recommendations will be raised.The proposed method has been tested in this research on random data samples,and the results were compared with the results of the most common method,which is the linear multiple regression method.It should be noted here that the procedures for building and testing the neural network will remain constant even if another sample of data is used.展开更多
Alcoholism is an unhealthy lifestyle associated with alcohol dependence.Not only does drinking for a long time leads to poor mental health and loss of self-control,but alcohol seeps into the bloodstream and shortens t...Alcoholism is an unhealthy lifestyle associated with alcohol dependence.Not only does drinking for a long time leads to poor mental health and loss of self-control,but alcohol seeps into the bloodstream and shortens the lifespan of the body’s internal organs.Alcoholics often think of alcohol as an everyday drink and see it as a way to reduce stress in their lives because they cannot see the damage in their bodies and they believe it does not affect their physical health.As their drinking increases,they become dependent on alcohol and it affects their daily lives.Therefore,it is important to recognize the dangers of alcohol abuse and to stop drinking as soon as possible.To assist physicians in the diagnosis of patients with alcoholism,we provide a novel alcohol detection system by extracting image features of wavelet energy entropy from magnetic resonance imaging(MRI)combined with a linear regression classifier.Compared with the latest method,the 10-fold cross-validation experiment showed excellent results,including sensitivity 91.54±1.47%,specificity 93.66±1.34%,Precision 93.45±1.27%,accuracy 92.61±0.81%,F1 score 92.48±0.83%and MCC 85.26±1.62%.展开更多
Cost effective sampling design is a major concern in some experiments especially when the measurement of the characteristic of interest is costly or painful or time consuming.Ranked set sampling(RSS)was first proposed...Cost effective sampling design is a major concern in some experiments especially when the measurement of the characteristic of interest is costly or painful or time consuming.Ranked set sampling(RSS)was first proposed by McIntyre[1952.A method for unbiased selective sampling,using ranked sets.Australian Journal of Agricultural Research 3,385-390]as an effective way to estimate the pasture mean.In the current paper,a modification of ranked set sampling called moving extremes ranked set sampling(MERSS)is considered for the best linear unbiased estimators(BLUEs)for the simple linear regression model.The BLUEs for this model under MERSS are derived.The BLUEs under MERSS are shown to be markedly more efficient for normal data when compared with the BLUEs under simple random sampling.展开更多
Recognition method of traffic flow change point was put forward based on traffic flow theory and the statistical change point analysis of multiple linear regressions. The method was calibrated and tested with the fiel...Recognition method of traffic flow change point was put forward based on traffic flow theory and the statistical change point analysis of multiple linear regressions. The method was calibrated and tested with the field data of Liantong Road of Zibo city to verify the validity and the feasibility of the theory. The results show that change point method of multiple linear regression can make out the rule of quantitative changes in traffic flow more accurately than ordinary methods. So, the change point method can be applied to traffic information management system more effectively.展开更多
Anti-inflammatory activity of a series of tri-substituted pyrimidine derivatives was predicted using two Quantitative Structure-Activity Relationship models. These relationships were developed from molecular descripto...Anti-inflammatory activity of a series of tri-substituted pyrimidine derivatives was predicted using two Quantitative Structure-Activity Relationship models. These relationships were developed from molecular descriptors calculated using the DFT quantum chemistry method using the B3LYP/6-31G(d,p) level of theory and molecular lipophilicity. Thus, the four descriptors which are the dipole moment μ<sub>D</sub>, the energy of the highest occupied molecular orbital E<sub>HOMO</sub>, the isotropic polarizability α and the ACD/logP lipophilicity were selected for this purpose. The Multiple Linear Regression (MLR) and Artificial Neural Network (ANN) models are respectively accredited with the following statistical indicators: R<sup>2</sup>=91.28%, R<sup>2</sup><sub>aj</sub>=89.11%, RMCE = 0.2831, R<sup>2</sup><sub>ext</sub>=86.50% and R<sup>2</sup>=98.22%, R<sup>2</sup><sub>aj</sub>=97.75%, RMCE = 0.1131, R<sup>2</sup><sub>ext</sub>=98.54%. The results obtained with the artificial neural network are better than those of the multiple linear regression. However, these results show that the two models developed have very good predictive performance of anti-inflammatory activity. These two models can therefore be used to predict anti-inflammatory activity of new similar pyrimidine derivatives.展开更多
The purpose of this research was to determine whether the Linear Regression Analysis can be effectively applied to the prioritization of defense-in-depth security tools and procedures to reduce cyber threats during th...The purpose of this research was to determine whether the Linear Regression Analysis can be effectively applied to the prioritization of defense-in-depth security tools and procedures to reduce cyber threats during the Global Corona Virus Pandemic. The way this was determined or methods used in this study consisted of scanning 20 peer reviewed Cybersecurity Articles from prominent Cybersecurity Journals for a list of defense in depth measures (tools and procedures) and the threats that those measures were designed to reduce. The methods further involved using the Likert Scale Model to create an ordinal ranking of the measures and threats. The defense in depth tools and procedures were then compared to see whether the Likert scale and Linear Regression Analysis could be effectively applied to prioritize and combine the measures to reduce pandemic related cyber threats. The results of this research reject the H0 null hypothesis that Linear Regression Analysis does not affect the relationship between the prioritization and combining of defense in depth tools and procedures (independent variables) and pandemic related cyber threats (dependent variables).展开更多
Efficiency and precision in prediction of Chlorophyll-a using this model is still a pandemic among researchers, due to the natural conditions in ocean water systems itself, which involved chemical, biological and phys...Efficiency and precision in prediction of Chlorophyll-a using this model is still a pandemic among researchers, due to the natural conditions in ocean water systems itself, which involved chemical, biological and physical processes and interaction among them may affect the model performance drastically. Thus, to overcome this problem as well as to improve the strength of MLR, we proposed a hybrid approach, i.e., an Artificial Neural Network to the MLR coins as Artificial Neural Network-Multiple Linear Regression (ANN-MLR). To investigate the performance of the proposed model, we compared Multiple Linear Regression (MLR), Artificial Neural Network (ANN) and proposed hybrid Artificial Neural Network and Multiple Linear Regression (ANN-MLR) in the prediction of chlorophyll-a (chl-a) concentration by statistical measurement which are MSE and MAE. Achieving our objectives of study, we used 4 parameters, i.e. temperature (°C), pH, salinity (ppt), DO (ppm) at the Offshore Kuala Terengganu, Terengganu, Malaysia. The results showed that our proposed model can improve the performance of the model as compared to ANN and MLR due to small errors generated, error reduced, and increased the correlation coefficient for all parameters in both MSE and MAE, respectively. Thus, this result indicated that our proposed model is efficient, precise and almost perfect correlation as compared to ANN and MLR.展开更多
It is meaningful to study trends in food and education expenditure as proportions of total household expenditure.In this study,based on year 2006 to 2017 data from Heilongjiang province in China and Ontario province i...It is meaningful to study trends in food and education expenditure as proportions of total household expenditure.In this study,based on year 2006 to 2017 data from Heilongjiang province in China and Ontario province in Canada,a linear regression model is used to forecast the Engel’s coefficients(proportion spent on food)and the education proportion from year 2018 to 2027 for those two regions.The results suggest that in both regions the Engel’s coefficients show a decreasing trend,while the education expenditure proportions show an increasing trend.The ratios of education expenditure to food expenditure in both places show an increasing trend.展开更多
This paper selects seven indicators of financial revenue and housing sales price in recent 19 years in China,and uses SPSS and Excel to carry out descriptive statistics,independent sample t-test,correlation analysis a...This paper selects seven indicators of financial revenue and housing sales price in recent 19 years in China,and uses SPSS and Excel to carry out descriptive statistics,independent sample t-test,correlation analysis and regression analysis to comprehensively study the correlation between financial revenue and housing sales price in China,and establishes the relationship between financial revenue and housing sales price When the average selling price of commercial housing increases by one unit,the fiscal revenue will increase by 27.855 points.展开更多
This paper proposes a strategy for machine learning in the ciphertext domain.The data to be trained in the linear regression equation is encrypted by SHE homomorphic encryption,and then trained in the ciphertext domai...This paper proposes a strategy for machine learning in the ciphertext domain.The data to be trained in the linear regression equation is encrypted by SHE homomorphic encryption,and then trained in the ciphertext domain.At the same time,it is guaranteed that the error of the training results between the ciphertext domain and the plaintext domain is in a controllable range.After the training,the ciphertext can be decrypted and restored to the original plaintext training data.展开更多
Recent developments in database technology have seen a wide variety of data being stored in huge collections. The wide variety makes the analysis tasks of a generic database a strenuous task in knowledge discovery. On...Recent developments in database technology have seen a wide variety of data being stored in huge collections. The wide variety makes the analysis tasks of a generic database a strenuous task in knowledge discovery. One approach is to summarize large datasets in such a way that the resulting summary dataset is of manageable size. Histogram has received significant attention as summarization/representative object for large database. But, it suffers from computational and space complexity. In this paper, we propose an idea to transform the histogram object into a Piecewise Linear Regression (PLR) line object and suggest that PLR objects can be less computational and storage intensive while compared to those of histograms. On the other hand to carry out a cluster analysis, we propose a distance measure for computing the distance between the PLR lines. Case study is presented based on the real data of online education system LMS. This demonstrates that PLR is a powerful knowledge representative for very large database.展开更多
In this paper, we study some robustness aspects of linear regression models of the presence of outliers or discordant observations considering the use of stable distributions for the response in place of the usual nor...In this paper, we study some robustness aspects of linear regression models of the presence of outliers or discordant observations considering the use of stable distributions for the response in place of the usual normality assumption. It is well known that, in general, there is no closed form for the probability density function of stable distributions. However, under a Bayesian approach, the use of a latent or auxiliary random variable gives some simplification to obtain any posterior distribution when related to stable distributions. To show the usefulness of the computational aspects, the methodology is applied to two examples: one is related to a standard linear regression model with an explanatory variable and the other is related to a simulated data set assuming a 23 factorial experiment. Posterior summaries of interest are obtained using MCMC (Markov Chain Monte Carlo) methods and the OpenBugs software.展开更多
文摘Cloud infrastructural resource optimization is the process of precisely selecting the allocating the correct resources either to a workload or application.When workload execution,accuracy,and cost are accurately stabilized in opposition to the best possible framework in real-time,efficiency is attained.In addition,every workload or application required for the framework is characteristic and these essentials change over time.But,the existing method was failed to ensure the high Quality of Service(QoS).In order to address this issue,a Tricube Weighted Linear Regression-based Inter Quartile(TWLR-IQ)for Cloud Infrastructural Resource Optimization is introduced.A Tricube Weighted Linear Regression is presented in the proposed method to estimate the resources(i.e.,CPU,RAM,and network bandwidth utilization)based on the usage history in each cloud server.Then,Inter Quartile Range is applied to efficiently predict the overload hosts for ensuring a smooth migration.Experimental results show that our proposed method is better than the approach in Cloudsim under various performance metrics.The results clearly showed that the proposed method can reduce the energy consumption and provide a high level of commitment with ensuring the minimum number of Virtual Machine(VM)Migrations as compared to the state-of-the-art methods.
基金the National Natural Science Foundation of China(No.41804135)the Key Laboratory of Petroleum Resources Research,Institute of Geology and Geophysics,Chinese Academy of Sciences,Open Project(No.KLOR2018-9)the Beijing Information Science and Technology University Research Fund Project(No.2025025).
文摘Aiming at the problems of low accuracy,long time consumption,and failure to obtain quantita-tive fault identification results of existing automatic fault identification technic,a fault recognition method based on clustering linear regression is proposed.Firstly,Hough transform is used to detect the line segment of the enhanced image obtained by the coherence cube algorithm.Secondly,the endpoint of the line segment detected by Hough transform is taken as the key point,and the adaptive clustering linear regression algorithm is used to cluster the key points adaptively according to the lin-ear relationship between them.Finally,a fault is generated from each category of key points based on least squares curve fitting method to realize fault identification.To verify the feasibility and pro-gressiveness of the proposed method,it is compared with the traditional method and the latest meth-od on the actual seismic data through experiments,and the effectiveness of the proposed method is verified by the experimental results on the actual seismic data.
文摘A Mobile Ad-hoc NETwork(MANET)contains numerous mobile nodes,and it forms a structure-less network associated with wireless links.But,the node movement is the key feature of MANETs;hence,the quick action of the nodes guides a link failure.This link failure creates more data packet drops that can cause a long time delay.As a result,measuring accurate link failure time is the key factor in the MANET.This paper presents a Fuzzy Linear Regression Method to measure Link Failure(FLRLF)and provide an optimal route in the MANET-Internet of Things(IoT).This work aims to predict link failure and improve routing efficiency in MANET.The Fuzzy Linear Regression Method(FLRM)measures the long lifespan link based on the link failure.The mobile node group is built by the Received Signal Strength(RSS).The Hill Climbing(HC)method selects the Group Leader(GL)based on node mobility,node degree and node energy.Additionally,it uses a Data Gathering node forward the infor-mation from GL to the sink node through multiple GL.The GL is identified by linking lifespan and energy using the Particle Swarm Optimization(PSO)algo-rithm.The simulation results demonstrate that the FLRLF approach increases the GL lifespan and minimizes the link failure time in the MANET.
文摘The development of prediction supports is a critical step in information systems engineering in this era defined by the knowledge economy, the hub of which is big data. Currently, the lack of a predictive model, whether qualitative or quantitative, depending on a company’s areas of intervention can handicap or weaken its competitive capacities, endangering its survival. In terms of quantitative prediction, depending on the efficacy criteria, a variety of methods and/or tools are available. The multiple linear regression method is one of the methods used for this purpose. A linear regression model is a regression model of an explained variable on one or more explanatory variables in which the function that links the explanatory variables to the explained variable has linear parameters. The purpose of this work is to demonstrate how to use multiple linear regressions, which is one aspect of decisional mathematics. The use of multiple linear regressions on random data, which can be replaced by real data collected by or from organizations, provides decision makers with reliable data knowledge. As a result, machine learning methods can provide decision makers with relevant and trustworthy data. The main goal of this article is therefore to define the objective function on which the influencing factors for its optimization will be defined using the linear regression method.
基金This work was supported by the 2021 Project of the“14th Five-Year Plan”of Shaanxi Education Science“Research on the Application of Educational Data Mining in Applied Undergraduate Teaching-Taking the Course of‘Computer Application Technology’as an Example”(SGH21Y0403)the Teaching Reform and Research Projects for Practical Teaching in 2022“Research on Practical Teaching of Applied Undergraduate Projects Based on‘Combination of Courses and Certificates”-Taking Computer Application Technology Courses as an Example”(SJJG02012)the 11th batch of Teaching Reform Research Project of Xi’an Jiaotong University City College“Project-Driven Cultivation and Research on Information Literacy of Applied Undergraduate Students in the Information Times-Taking Computer Application Technology Course Teaching as an Example”(111001).
文摘Social network is the mainstream medium of current information dissemination,and it is particularly important to accurately predict its propagation law.In this paper,we introduce a social network propagation model integrating multiple linear regression and infectious disease model.Firstly,we proposed the features that affect social network communication from three dimensions.Then,we predicted the node influence via multiple linear regression.Lastly,we used the node influence as the state transition of the infectious disease model to predict the trend of information dissemination in social networks.The experimental results on a real social network dataset showed that the prediction results of the model are consistent with the actual information dissemination trends.
基金the China Scholarship Council(CSC)(201903250115)the National Natural Science Foundation of China(31972515)the China Agriculture Research System of MOF and MARA(CARS-09-P31).
文摘Understanding the spatial-temporal dynamics of crop nitrogen(N)use efficiency(NUE)and the relationship with explanatory environmental variables can support land-use management and policymaking.Nevertheless,the application of statistical models for evaluating the explanatory variables of space-time variation in crop NUE is still under-researched.In this study,stepwise multiple linear regression(SMLR)and Random Forest(RF)were used to evaluate the spatial and temporal variation of NUE indicators(i.e.,partial factor productivity of N(PFPN);partial nutrient balance of N(PNBN))at county scale in Northeast China(Heilongjiang,Liaoning and Jilin provinces)from 1990 to 2015.Explanatory variables included agricultural management practices,topography,climate,economy,soil and crop types.Results revealed that the PFPN was higher in the northern parts and lower in the center of the Northeast China and PNBN increased from southern to northern parts during the 1990–2015 period.The NUE indicators decreased with time in most counties during the study period.The model efficiency coefficients of the SMLR and RF models were 0.44 and 0.84 for PFPN,and 0.67 and 0.89 for PNBN,respectively.The RF model had higher relative importance of soil and climatic covariates and lower relative importance of crop covariates compared to the SMLR model.The planting area index of vegetables and beans,soil clay content,saturated water content,enhanced vegetation index in November&December,soil bulk density,and annual minimum temperature were the main explanatory variables for both NUE indicators.This is the first study to show the quantitative relative importance of explanatory variables for NUE at a county level in Northeast China using RF and SMLR.This novel study gives reference measurements to improve crop NUE which is one of the most effective means of managing N for sustainable development,ensuring food security,alleviating environmental degradation and increasing farmer’s profitability.
基金The National Natural Science Foundation of China under contract Nos 41830538 and 42090042the Program of the Chinese Academy of Sciences under contract Nos 133244KYSB20190031,ZDRW-XH-2001902 and ISEE2018PY06the Program of the Southern Marine Science and Engineering Guangdong Laboratory(Guangzhou)under contract Nos GML2019ZD0303 and2019BT02H594。
文摘The El Nino-Southern Oscillation(ENSO)has great impacts on the Indian Ocean sea surface temperature(SST).In fact,two major modes of the Indian Ocean SST namely the Indian Ocean Basin(IOB)and the Indian Ocean Dipole(IOD)modes,exerting strong influences on the Indian Ocean rim countries,are both influenced by the ENSO.Based on a combined linear regression method,this study quantifies the ENSO impacts on the IOB and the IOD during ENSO concurrent,developing,and decaying stages.After removing the ENSO impacts,the spring peak of the IOB disappears along with significant decrease in number of events,while the number of events is only slightly reduced and the autumn peak remains for the IOD.By isolating the ENSO impacts during each stage,this study reveals that the leading impacts of ENSO contribute to the IOD development,while the delayed impacts facilitate the IOD phase switch and prompt the IOB development.Besides,the decadal variations of ENSO impacts are various during each stage and over different regions.These imply that merely removing the concurrent ENSO impacts would not be sufficient to investigate intrinsic climate variability of the Indian Ocean,and the present method may be useful to study climate variabilities independent of ENSO.
文摘This paper transforms fuzzy number into clear number using the centroid method, thus we can research the traditional linear regression model which is transformed from the fuzzy linear regression model. The model’s input and output are fuzzy numbers, and the regression coefficients are clear numbers. This paper considers the parameter estimation and impact analysis based on data deletion. Through the study of example and comparison with other models, it can be concluded that the model in this paper is applied easily and better.
文摘Predicting the value of one or more variables using the values of other variables is a very important process in the various engineering experiments that include large data that are difficult to obtain using different measurement processes.Regression is one of the most important types of supervised machine learning,in which labeled data is used to build a prediction model,regression can be classified into three different categories:linear,polynomial,and logistic.In this research paper,different methods will be implemented to solve the linear regression problem,where there is a linear relationship between the target and the predicted output.Various methods for linear regression will be analyzed using the calculated Mean Square Error(MSE)between the target values and the predicted outputs.A huge set of regression samples will be used to construct the training dataset with selected sizes.A detailed comparison will be performed between three methods,including least-square fit;Feed-Forward Artificial Neural Network(FFANN),and Cascade Feed-Forward Artificial Neural Network(CFFANN),and recommendations will be raised.The proposed method has been tested in this research on random data samples,and the results were compared with the results of the most common method,which is the linear multiple regression method.It should be noted here that the procedures for building and testing the neural network will remain constant even if another sample of data is used.
基金This research was supported by Zhejiang Provincial Natural Science Foundation of China under Grant No.LY17F010003.
文摘Alcoholism is an unhealthy lifestyle associated with alcohol dependence.Not only does drinking for a long time leads to poor mental health and loss of self-control,but alcohol seeps into the bloodstream and shortens the lifespan of the body’s internal organs.Alcoholics often think of alcohol as an everyday drink and see it as a way to reduce stress in their lives because they cannot see the damage in their bodies and they believe it does not affect their physical health.As their drinking increases,they become dependent on alcohol and it affects their daily lives.Therefore,it is important to recognize the dangers of alcohol abuse and to stop drinking as soon as possible.To assist physicians in the diagnosis of patients with alcoholism,we provide a novel alcohol detection system by extracting image features of wavelet energy entropy from magnetic resonance imaging(MRI)combined with a linear regression classifier.Compared with the latest method,the 10-fold cross-validation experiment showed excellent results,including sensitivity 91.54±1.47%,specificity 93.66±1.34%,Precision 93.45±1.27%,accuracy 92.61±0.81%,F1 score 92.48±0.83%and MCC 85.26±1.62%.
基金Supported by the National Natural Science Foundation of China(11901236)the Scientific Research Fund of Hunan Provincial Science and Technology Department(2019JJ50479)+3 种基金the Scientific Research Fund of Hunan Provincial Education Department(18B322)the Winning Bid Project of Hunan Province for the 4th National Economic Census([2020]1)the Young Core Teacher Foundation of Hunan Province([2020]43)the Funda-mental Research Fund of Xiangxi Autonomous Prefecture(2018SF5026)。
文摘Cost effective sampling design is a major concern in some experiments especially when the measurement of the characteristic of interest is costly or painful or time consuming.Ranked set sampling(RSS)was first proposed by McIntyre[1952.A method for unbiased selective sampling,using ranked sets.Australian Journal of Agricultural Research 3,385-390]as an effective way to estimate the pasture mean.In the current paper,a modification of ranked set sampling called moving extremes ranked set sampling(MERSS)is considered for the best linear unbiased estimators(BLUEs)for the simple linear regression model.The BLUEs for this model under MERSS are derived.The BLUEs under MERSS are shown to be markedly more efficient for normal data when compared with the BLUEs under simple random sampling.
基金National Natural Science Foundations of China(No. 61074140,No. 60974094)Young Teacher Development Support Project of Shandong University of Technology,China
文摘Recognition method of traffic flow change point was put forward based on traffic flow theory and the statistical change point analysis of multiple linear regressions. The method was calibrated and tested with the field data of Liantong Road of Zibo city to verify the validity and the feasibility of the theory. The results show that change point method of multiple linear regression can make out the rule of quantitative changes in traffic flow more accurately than ordinary methods. So, the change point method can be applied to traffic information management system more effectively.
文摘Anti-inflammatory activity of a series of tri-substituted pyrimidine derivatives was predicted using two Quantitative Structure-Activity Relationship models. These relationships were developed from molecular descriptors calculated using the DFT quantum chemistry method using the B3LYP/6-31G(d,p) level of theory and molecular lipophilicity. Thus, the four descriptors which are the dipole moment μ<sub>D</sub>, the energy of the highest occupied molecular orbital E<sub>HOMO</sub>, the isotropic polarizability α and the ACD/logP lipophilicity were selected for this purpose. The Multiple Linear Regression (MLR) and Artificial Neural Network (ANN) models are respectively accredited with the following statistical indicators: R<sup>2</sup>=91.28%, R<sup>2</sup><sub>aj</sub>=89.11%, RMCE = 0.2831, R<sup>2</sup><sub>ext</sub>=86.50% and R<sup>2</sup>=98.22%, R<sup>2</sup><sub>aj</sub>=97.75%, RMCE = 0.1131, R<sup>2</sup><sub>ext</sub>=98.54%. The results obtained with the artificial neural network are better than those of the multiple linear regression. However, these results show that the two models developed have very good predictive performance of anti-inflammatory activity. These two models can therefore be used to predict anti-inflammatory activity of new similar pyrimidine derivatives.
文摘The purpose of this research was to determine whether the Linear Regression Analysis can be effectively applied to the prioritization of defense-in-depth security tools and procedures to reduce cyber threats during the Global Corona Virus Pandemic. The way this was determined or methods used in this study consisted of scanning 20 peer reviewed Cybersecurity Articles from prominent Cybersecurity Journals for a list of defense in depth measures (tools and procedures) and the threats that those measures were designed to reduce. The methods further involved using the Likert Scale Model to create an ordinal ranking of the measures and threats. The defense in depth tools and procedures were then compared to see whether the Likert scale and Linear Regression Analysis could be effectively applied to prioritize and combine the measures to reduce pandemic related cyber threats. The results of this research reject the H0 null hypothesis that Linear Regression Analysis does not affect the relationship between the prioritization and combining of defense in depth tools and procedures (independent variables) and pandemic related cyber threats (dependent variables).
文摘Efficiency and precision in prediction of Chlorophyll-a using this model is still a pandemic among researchers, due to the natural conditions in ocean water systems itself, which involved chemical, biological and physical processes and interaction among them may affect the model performance drastically. Thus, to overcome this problem as well as to improve the strength of MLR, we proposed a hybrid approach, i.e., an Artificial Neural Network to the MLR coins as Artificial Neural Network-Multiple Linear Regression (ANN-MLR). To investigate the performance of the proposed model, we compared Multiple Linear Regression (MLR), Artificial Neural Network (ANN) and proposed hybrid Artificial Neural Network and Multiple Linear Regression (ANN-MLR) in the prediction of chlorophyll-a (chl-a) concentration by statistical measurement which are MSE and MAE. Achieving our objectives of study, we used 4 parameters, i.e. temperature (°C), pH, salinity (ppt), DO (ppm) at the Offshore Kuala Terengganu, Terengganu, Malaysia. The results showed that our proposed model can improve the performance of the model as compared to ANN and MLR due to small errors generated, error reduced, and increased the correlation coefficient for all parameters in both MSE and MAE, respectively. Thus, this result indicated that our proposed model is efficient, precise and almost perfect correlation as compared to ANN and MLR.
文摘It is meaningful to study trends in food and education expenditure as proportions of total household expenditure.In this study,based on year 2006 to 2017 data from Heilongjiang province in China and Ontario province in Canada,a linear regression model is used to forecast the Engel’s coefficients(proportion spent on food)and the education proportion from year 2018 to 2027 for those two regions.The results suggest that in both regions the Engel’s coefficients show a decreasing trend,while the education expenditure proportions show an increasing trend.The ratios of education expenditure to food expenditure in both places show an increasing trend.
基金Thank you for your valuable comments and suggestions.This research was supported by Yunnan applied basic research project(NO.2017FD150)Chuxiong Normal University General Research Project(NO.XJYB2001).
文摘This paper selects seven indicators of financial revenue and housing sales price in recent 19 years in China,and uses SPSS and Excel to carry out descriptive statistics,independent sample t-test,correlation analysis and regression analysis to comprehensively study the correlation between financial revenue and housing sales price in China,and establishes the relationship between financial revenue and housing sales price When the average selling price of commercial housing increases by one unit,the fiscal revenue will increase by 27.855 points.
文摘This paper proposes a strategy for machine learning in the ciphertext domain.The data to be trained in the linear regression equation is encrypted by SHE homomorphic encryption,and then trained in the ciphertext domain.At the same time,it is guaranteed that the error of the training results between the ciphertext domain and the plaintext domain is in a controllable range.After the training,the ciphertext can be decrypted and restored to the original plaintext training data.
文摘Recent developments in database technology have seen a wide variety of data being stored in huge collections. The wide variety makes the analysis tasks of a generic database a strenuous task in knowledge discovery. One approach is to summarize large datasets in such a way that the resulting summary dataset is of manageable size. Histogram has received significant attention as summarization/representative object for large database. But, it suffers from computational and space complexity. In this paper, we propose an idea to transform the histogram object into a Piecewise Linear Regression (PLR) line object and suggest that PLR objects can be less computational and storage intensive while compared to those of histograms. On the other hand to carry out a cluster analysis, we propose a distance measure for computing the distance between the PLR lines. Case study is presented based on the real data of online education system LMS. This demonstrates that PLR is a powerful knowledge representative for very large database.
基金financial support from the Brazilian Institution Conselho Nacional de Desenvolvimento Cientifico e Tecnologico(CNPq).
文摘In this paper, we study some robustness aspects of linear regression models of the presence of outliers or discordant observations considering the use of stable distributions for the response in place of the usual normality assumption. It is well known that, in general, there is no closed form for the probability density function of stable distributions. However, under a Bayesian approach, the use of a latent or auxiliary random variable gives some simplification to obtain any posterior distribution when related to stable distributions. To show the usefulness of the computational aspects, the methodology is applied to two examples: one is related to a standard linear regression model with an explanatory variable and the other is related to a simulated data set assuming a 23 factorial experiment. Posterior summaries of interest are obtained using MCMC (Markov Chain Monte Carlo) methods and the OpenBugs software.
