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Tricube Weighted Linear Regression and Interquartile for Cloud Infrastructural Resource Optimization
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作者 Neema George B.K.Anoop Vinodh P.Vijayan 《Computer Systems Science & Engineering》 SCIE EI 2023年第6期2281-2297,共17页
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
关键词 Cloud infrastructure tricube weighted linear regression inter quartile CPU RAM network bandwidth utilization
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A fault recognition method based on clustering linear regression
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作者 陈雷 SHI Jiaqi ZHANG Ting 《High Technology Letters》 EI CAS 2023年第4期406-415,共10页
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
关键词 fault recognition CLUSTERING linear regression curve fitting seismic interpreta-tion
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Prediction of Link Failure in MANET-IoT Using Fuzzy Linear Regression
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作者 R.Mahalakshmi V.Prasanna Srinivasan +1 位作者 S.Aghalya D.Muthukumaran 《Intelligent Automation & Soft Computing》 SCIE 2023年第5期1627-1637,共11页
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
关键词 Mobile ad-hoc network fuzzy linear regression method link failure detection particle swarm optimization hill climbing
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Development of a Quantitative Prediction Support System Using the Linear Regression Method
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作者 Jeremie Ndikumagenge Vercus Ntirandekura 《Journal of Applied Mathematics and Physics》 2023年第2期421-427,共7页
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
关键词 PREDICTION linear regression Machine Learning Least Squares Method
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Integrating Multiple Linear Regression and Infectious Disease Models for Predicting Information Dissemination in Social Networks
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作者 Junchao Dong Tinghui Huang +1 位作者 Liang Min Wenyan Wang 《Journal of Electronic Research and Application》 2023年第2期20-27,共8页
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
关键词 Social networks Epidemic model linear regression model
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Calculations of rock matrix modulus based on a linear regression relation 被引量:5
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作者 贺锡雷 贺振华 +2 位作者 汪瑞良 王绪本 蒋炼 《Applied Geophysics》 SCIE CSCD 2011年第3期155-162,239,共9页
The rock matrix bulk modulus or its inverse, the compressive coefficient, is an important input parameter for fluid substitution by the Biot-Gassmann equation in reservoir prediction. However, it is not easy to accura... The rock matrix bulk modulus or its inverse, the compressive coefficient, is an important input parameter for fluid substitution by the Biot-Gassmann equation in reservoir prediction. However, it is not easy to accurately estimate the bulk modulus by using conventional methods. In this paper, we present a new linear regression equation for calculating the parameter. In order to get this equation, we first derive a simplified Gassmann equation by using a reasonable assumption in which the compressive coefficient of the saturated pore fluid is much greater than the rock matrix, and, second, we use the Eshelby- Walsh relation to replace the equivalent modulus of a dry rock in the Gassmann equation. Results from the rock physics analysis of rock sample from a carbonate area show that rock matrix compressive coefficients calculated with water-saturated and dry rock samples using the linear regression method are very close (their error is less than 1%). This means the new method is accurate and reliable. 展开更多
关键词 Bulk modulus rock matrix fluid substitution rock physics linear regression
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Selection of the Linear Regression Model According to the Parameter Estimation 被引量:31
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作者 Sun Dao-de Department of Computer, Fuyang Teachers College, Anhui 236032,China 《Wuhan University Journal of Natural Sciences》 EI CAS 2000年第4期400-405,共6页
In this paper, based on the theory of parameter estimation, we give a selection method and, in a sense of a good character of the parameter estimation, we think that it is very reasonable. Moreover, we offer a calcula... In this paper, based on the theory of parameter estimation, we give a selection method and, in a sense of a good character of the parameter estimation, we think that it is very reasonable. Moreover, we offer a calculation method of selection statistic and an applied example. 展开更多
关键词 parameter estimation linear regression model selection criterion mean square error
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Prediction of kiwifruit firmness using fruit mineral nutrient concentration by artificial neural network(ANN) and multiple linear regressions(MLR) 被引量:8
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作者 Ali Mohammadi Torkashvand Abbas Ahmadi Niloofar Layegh Nikravesh 《Journal of Integrative Agriculture》 SCIE CAS CSCD 2017年第7期1634-1644,共11页
Many properties of fruit are influenced by plant nutrition. Fruit firmness is one of the most important fruit characteristics and determines post-harvest life of the fruit, in recent decades, artificial intelligence s... Many properties of fruit are influenced by plant nutrition. Fruit firmness is one of the most important fruit characteristics and determines post-harvest life of the fruit, in recent decades, artificial intelligence systems were employed for developing predictive models to estimate and predict many agriculture processes. In the present study, the predictive capabilities of multiple linear regressions (MLR) and artificial neural networks (ANNs) are evaluated to estimate fruit firmness in six months, including each of nutrients concentrations (nitrogen (N), potassium (K), calcium (Ca) and magnesium (Mg)) alone (P1), com- bination of nutrients concentrations (P2), nutrient concentration ratios alone (P3), and combination of nutrient concentrations and nutrient concentration ratios (P4). The results showed that MLR model estimated fruit firmness more accuracy than ANN model in three datasets (P1, P2 and P4). However, the application of P3 (N/Ca ratio) as the input dataset in ANN model improved the prediction of fruit firmness than the MLR model. Correlation coefficient and root mean squared error (RMSE) were 0.850 and 0.539 between the measured and the estimated data by the ANN model, respectively. Generally, the ANN model showed greater potential in determining the relationship between 6-mon-fruit firmness and nutrients concentration. 展开更多
关键词 artificial neural network FIRMNESS FRUIT KIWI multiple linear regression NUTRIENT
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Combined model based on optimized multi-variable grey model and multiple linear regression 被引量:11
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作者 Pingping Xiong Yaoguo Dang +1 位作者 Xianghua wu Xuemei Li 《Journal of Systems Engineering and Electronics》 SCIE EI CSCD 2011年第4期615-620,共6页
The construction method of background value is improved in the original multi-variable grey model (MGM(1,m)) from its source of construction errors. The MGM(1,m) with optimized background value is used to elimin... The construction method of background value is improved in the original multi-variable grey model (MGM(1,m)) from its source of construction errors. The MGM(1,m) with optimized background value is used to eliminate the random fluctuations or errors of the observational data of all variables, and the combined prediction model together with the multiple linear regression is established in order to improve the simulation and prediction accuracy of the combined model. Finally, a combined model of the MGM(1,2) with optimized background value and the binary linear regression is constructed by an example. The results show that the model has good effects for simulation and prediction. 展开更多
关键词 multi-variable grey model (MGM(1 m)) backgroundvalue OPTIMIZATION multiple linear regression combined predic-tion model.
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A study of the mixed layer of the South China Sea based on the multiple linear regression 被引量:7
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作者 DUAN Rui YANG Kunde +1 位作者 MA Yuanliang HU Tao 《Acta Oceanologica Sinica》 SCIE CAS CSCD 2012年第6期19-31,共13页
Multiple linear regression (MLR) method was applied to quantify the effects of the net heat flux (NHF), the net freshwater flux (NFF) and the wind stress on the mixed layer depth (MLD) of the South China Sea ... Multiple linear regression (MLR) method was applied to quantify the effects of the net heat flux (NHF), the net freshwater flux (NFF) and the wind stress on the mixed layer depth (MLD) of the South China Sea (SCS) based on the simple ocean data assimilation (SODA) dataset. The spatio-temporal distributions of the MLD, the buoyancy flux (combining the NHF and the NFF) and the wind stress of the SCS were presented. Then using an oceanic vertical mixing model, the MLD after a certain time under the same initial conditions but various pairs of boundary conditions (the three factors) was simulated. Applying the MLR method to the results, regression equations which modeling the relationship between the simulated MLD and the three factors were calculated. The equations indicate that when the NHF was negative, it was the primary driver of the mixed layer deepening; and when the NHF was positive, the wind stress played a more important role than that of the NHF while the NFF had the least effect. When the NHF was positive, the relative quantitative effects of the wind stress, the NHF, and the NFF were about i0, 6 and 2. The above conclusions were applied to explaining the spatio-temporal distributions of the MLD in the SCS and thus proved to be valid. 展开更多
关键词 mixed layer multiple linear regression South China Sea vertical mixing model
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A class of estimators of the mean survival time from interval censored data with application to linear regression 被引量:9
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作者 ZHENG Zu-kang 《Applied Mathematics(A Journal of Chinese Universities)》 SCIE CSCD 2008年第4期377-390,共14页
A class of estimators of the mean survival time with interval censored data are studied by unbiased transformation method. The estimators are constructed based on the observations to ensure unbiasedness in the sense t... A class of estimators of the mean survival time with interval censored data are studied by unbiased transformation method. The estimators are constructed based on the observations to ensure unbiasedness in the sense that the estimators in a certain class have the same expectation as the mean survival time. The estimators have good properties such as strong consistency (with the rate of O(n^-1/1 (log log n)^1/2)) and asymptotic normality. The application to linear regression is considered and the simulation reports are given. 展开更多
关键词 interval censored data linear regression
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Predicting the Acute Toxicity of Aromatic Amines by Linear and Nonlinear Regression Methods 被引量:4
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作者 张晓龙 周志祥 +3 位作者 刘阳华 范雪兰 李捍东 王建涛 《Chinese Journal of Structural Chemistry》 SCIE CAS CSCD 2014年第2期244-252,共9页
In current paper, a quantitative structure-activity relationship (QSAR) study was performed for the prediction of acute toxicity of aromatic amines. A set of 56 compounds was randomly divided into a training set of ... In current paper, a quantitative structure-activity relationship (QSAR) study was performed for the prediction of acute toxicity of aromatic amines. A set of 56 compounds was randomly divided into a training set of 46 compounds and a test set of 10 compounds. The electronic and topological descriptors computed by the Scigress package and Dragon software were used as predictor variables. Multiple linear regression (MLR) and support vector machine (SVM) were utilized to build the linear and nonlinear QSAR models, respectively. The obtained models with five descriptors show strong predictive ability. The linear model fits the training set with R2 = 0.71, with higher SVM values of R2 = 0.77. The validation results obtained from the test set indicate that the SVM model is comparable or superior to that obtained by MLR, both in terms of prediction ability and robustness. 展开更多
关键词 aromatic amines acute toxicity quantitative structure-activity relationship(QSAR) support vector machine (SVM) multiple linear regression (MLR)
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Statistical analysis of nitrogen use efficiency in Northeast China using multiple linear regression and Random Forest 被引量:2
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作者 LIU Ying-xia Gerard B.M.HEUVELINK +4 位作者 Zhanguo BAI HE Ping JIANG Rong HUANG Shaohui XU Xin-peng 《Journal of Integrative Agriculture》 SCIE CAS CSCD 2022年第12期3637-3657,共21页
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
关键词 partial factor productivity of N partial nutrient balance of N stepwise multiple linear regression Random Forest county scale Northeast China
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Alcoholism Detection by Wavelet Energy Entropy and Linear Regression Classifier 被引量:2
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作者 Xianqing Chen Yan Yan 《Computer Modeling in Engineering & Sciences》 SCIE EI 2021年第4期325-343,共19页
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
关键词 Alcohol detection wavelet energy entropy linear regression classifier cross-validation computer-aided diagnosis
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Finite Element Analysis and Linear Regression of Maximum Temperature for Inner Wall of Chimney Foundation 被引量:2
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作者 ZHANG Yu-mei SONG Yu-pu +1 位作者 ZHAO Yun-fei ZHANG Ye-min 《Journal of China University of Mining and Technology》 EI 2005年第3期234-240,共7页
The uniform design method was adopted and the twenty-four groups of different geometric and physical pa-rameters were chosen. The finite element model was built. Comparisons between the simulation results and the test... The uniform design method was adopted and the twenty-four groups of different geometric and physical pa-rameters were chosen. The finite element model was built. Comparisons between the simulation results and the test re-sults prove that the simulation results are correct. The distribution of the temperature field of the chimney foundationwas analyzed. The multivariate linear regression of the hightest tomperature was performed on the inner wall of thechimney foundation by the numerical calculated results. The fitting property of the highest temperature with six influ-ence factors was obtained. A simple method for the calculation of the temperature field of the chimney foundation wasprovided. 展开更多
关键词 chimney foundation temperature field nonlinear finite element analysis linear regression
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Revisiting ENSO impacts on the Indian Ocean SST based on a combined linear regression method 被引量:1
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作者 Lianyi Zhang Yan Du +1 位作者 Tomoki Tozuka Shoichiro Kido 《Acta Oceanologica Sinica》 SCIE CAS CSCD 2021年第5期47-57,共11页
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
关键词 Indian Ocean ENSO sea surface temperature climate modes combined linear regression
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A LARGE SAMPLE ESTIMATE IN MEDIAN LINEAR REGRESSION MODEL Ⅰ: NONTRUNCATED CASE 被引量:1
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作者 陈希孺 《Acta Mathematica Scientia》 SCIE CSCD 1990年第4期412-421,共10页
This paper uses a grouping-adjusting procedure to the data from a median linear regression model, and estimtes the regression coefficients by the method of weighted least squares. This method simplifies computation an... This paper uses a grouping-adjusting procedure to the data from a median linear regression model, and estimtes the regression coefficients by the method of weighted least squares. This method simplifies computation and in the meantime, preserves the same asymptotic normal distribution for the estimator, as in the ordinary minimum L_1-norm estimates. 展开更多
关键词 A LARGE SAMPLE ESTIMATE IN MEDIAN linear regression MODEL NONTRUNCATED CASE
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Study of measurements of air velocity transducer deriving average air velocity of roadway based on one-dimensional linear regression 被引量:1
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作者 ZHAO Dan LIU Jian +1 位作者 PAN Jing-tao LI Zong-xiang 《Journal of Coal Science & Engineering(China)》 2012年第2期134-137,共4页
One-dimensional linear regression equation between measured value of air velocity transducer and the average air velocity was established by experimental data. The effect is to be evaluated. Through judging the parame... One-dimensional linear regression equation between measured value of air velocity transducer and the average air velocity was established by experimental data. The effect is to be evaluated. Through judging the parameters, one-dimensional linear equation established is valid. Regression equation can approximately put the measurements of air velocity transducer into the value of average air velocity. The distribution of air velocity field is simulated using Comsol in the conditions of the same length of roadway, the same air velocity and different sections. 展开更多
关键词 air velocity one-dimensional linear regression mine safety
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Statistical Analysis of Fuzzy Linear Regression Model Based on Centroid Method 被引量:1
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作者 Aiwu Zhang 《Applied Mathematics》 2016年第7期579-586,共8页
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
关键词 Centroid Method Fuzzy linear regression Model Parameter Estimation Data Deletion Model Cook Distance
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Using Linear Regression Analysis and Defense in Depth to Protect Networks during the Global Corona Pandemic 被引量:1
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作者 Rodney Alexander 《Journal of Information Security》 2020年第4期261-291,共31页
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
关键词 Information Assurance Defense in Depth Information Technology Network Security CYBERSECURITY linear regression Analysis PANDEMIC
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