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logistic regression Trust–A Trust Model for Internet-of-Things Using regression Analysis

Computer Systems Science & Engineering 2023年第2期44卷 1125-1142页

作者： Feslin Anish Mon Solomon Godfrey Winster Sathianesan R.Ramesh Department of CSE Saveetha Engineering CollegeChennaiIndia Department of CSE SRM Institute of Science and TechnologyChennaiIndia Department of ECE Saveetha Engineering CollegeChennaiIndia

Internet of Things(IoT)is a popular social network in which devices are virtually connected for communicating and sharing *** is applied greatly in business enterprises and government sectors for delivering the services to their customers,clients and ***,the interaction is success-ful only based on the trust that each device has on *** trust is very much essential for a social *** Internet of Things have access over sen-sitive information,it urges to many threats that lead data management to *** issue is addressed by trust management that help to take decision about trust-worthiness of requestor and provider before communication and *** trust-based systems are existing for different domain using Dynamic weight meth-od,Fuzzy classiﬁcation,Bayes inference and very few regression analysis for *** proposed algorithm is based on logistic regression,which provide strong statistical background to trust *** make our stand strong on regression support to trust,we have compared the performance with equivalent sound Bayes analysis using Beta *** performance is studied in simu-lated IoT setup with Quality of Service(QoS)and Social parameters for the *** proposed model performs better in terms of various *** IoT connects heterogeneous devices such as tags and sensor devices for sharing of information and avail different application *** most salient features of IoT system is to design it with scalability,extendibility,compatibility and resiliency against *** existing worksﬁnds a way to integrate direct and indirect trust to con-verge quickly and estimate the bias due to attacks in addition to the above features.

关键词： LRTrust logistic regression trust management internet of things

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logistic regression for Evolving Data Streams Classification

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Journal of Shanghai Jiaotong university(Science) 2007年第2期12卷 197-203页

作者：尹志武黄上腾薛贵荣 Dept.of Computer Science and Eng. Shanghai Jiaotong Univ.

logistic regression is a fast classifier and can achieve higher accuracy on small training ***,it can work on both discrete and continuous attributes with nonlinear *** on these properties of logistic regression,this paper proposed an algorithm,called evolutionary logistical regression classifier(ELRClass),to solve the classification of evolving data *** algorithm applies logistic regression repeatedly to a sliding window of samples in order to update the existing classifier,to keep this classifier if its performance is deteriorated by the reason of bursting noise,or to construct a new classifier if a major concept drift is *** intensive experimental results demonstrate the effectiveness of this algorithm.

关键词： classification logistic regression data stream mining

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logistic regression Analysis of Damp-Heat and Cold Damp Impeding Syndrome of Rheumatoid Arthritis:A Perspective in Chinese Medicine

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Chinese Journal of Integrative Medicine 2012年第8期18卷 575-581页

作者：王志中方勇飞王勇牟方祥陈军邹庆华钟兵李景怡柏干苹张荣华 Department of Rheumatology Southwest HospitalThird Military Medical University Department of Health Statistics Third Military Medical University

Objective： To investigate a method for quantitative differential diagnosis of damp-heat and cold-damp impeding syndrome of rheumatoid arthritis （RA） in Chinese medicine （CM）. Methods： Laboratory parameters were collected from 306 patients with RA. The clinical symptoms and laboratory parameters were compared between patients with these two syndromes （158 with RA of damp-heat impeding syndrome, and 148 with RA of cold-damp impeding syndrome）, and a regression equation was established to facilitate discrimination of the two RA syndromes. Results： There were significant differences in disease activity score in 28 joints [DAS28 （4）], erythrocyte sedimentation rate （ESR）, white blood cell count （WBC）, C-reactive protein （CRP）, platelet count （PLT）, albumin （ALB） and globulin （GLB） between the two syndrome of RA （P〈0.05）. logistic regression analysis showed that the parameters ESR, WBC, CRP, joint pyrexia, joint cold, thirst, sweating, aversion to wind and cold, and cold extremities were statistically useful to discriminate damp-heat from cold-damp impeding syndrome. The regression equation was as follows： P=1/{1＋exp[-（3.0-0.021X1-0.196X2-0.163X3- 1.559X4＋1.504X5-0.927X6-1.039X7＋1.070X8＋1.330X9）]}. The independent variables X1-X9 were ESR, WBC, CRP, hot joint, cold joint, thirst, sweating, aversion to wind and cold, and cold limbs. A P value 〉 0.5 signified cold-damp impeding syndrome, and a P value 〈 0.5 signified damp-heat impeding syndrome. The accuracy was 90.2%. Conclusion： The regression equation may be useful for discriminating damp-heat from cold-damp impeding syndrome of RA.

关键词： logistic regression damp-heat impeding syndrome cold-damp impeding syndrome rheumatoid arthritis quantitative diagnosis

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logistic regression for Prediction and Diagnosis of Bacterial Regrowth in Water Distribution System

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Transactions of Tianjin University 2009年第5期15卷 371-374页

作者：董丽华赵新华吴卿杨幼安 School of Environmental Science and Engineering Tianjin University People's Liberation Army 93756

This paper focuses on the quantitative expression of bacterial regrowth in water distribution system. Considering public health risks of bacterial regrowth,the experiment was performed on a distribution system of selected ***,chemical,and microbiological parameters such as turbidity,temperature,residual chlorine and pH were measured over a three-month period and correlation analysis was carried *** with principal components analysis(PCA) ,a logistic regression model is developed to predict and diagnose bacterial regrowth and locate the zones with high risks of microbiology in the distribution *** model gives the probability of bacterial regrowth with the number of heterotrophic plate counts as the binary response variable and three new principal components variables as the explanatory *** veracity of the logistic regression model was 90%,which meets the precision requirement of the model.

关键词： bacterial regrowth water distribution system heterotrophic plate counts logistic regression principal components analysis odds ratio veracity

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logistic regression Analysis on Factors Affecting Adoption of Rice-Fish Farming in North Iran

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Rice science 2012年第2期19卷 153-160页

作者： Seyyed Ali NOORHOSSEINI-NIYAKI Mohammad Sadegh ALLAHYARI Department of Agronomy Lahidjan BranchIslamic Azad UniversityLahidjanIran Department of Agricultural Management Rasht BranchIslamic Azad UniversityRashtIran

We evaluated the factors influencing the adoption of rice-fish farming in the Tavalesh region near the Caspian Sea in northern Iran. We conducted a survey with open-ended questions. Data were collected from 184 respondents （61 adopters and 123 non-adopters） randomly sampled from selected villages and analyzed using logistic regression and multi- response analysis. Family size, number of contacts with an extension agent, participation in extension-education activities, membership in social institutions and the presence of farm workers were the most important socio-economic factors for the adoption of rice-fish farming system. In addition, economic problems were the most common issue reported by adopters. Other issues such as lack of access to appropriate fish food, losses of fish, lack of access to high quality fish fingerlings and dehydration and poor water quality were also important to a number of farmers.

关键词： adoption rice-fish farming logistic regression

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Application of a Novel Method for Machine Performance Degradation Assessment Based on Gaussian Mixture Model and logistic regression

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Chinese Journal of Mechanical Engineering 2011年第5期24卷 879-884页

作者： LIU Wenbin ZHONG Xin LEE Jay LIAO Linxia ZHOU Min Chemical Safety Engineering Research Center of Ministry of Education Beijing University of Chemical Technology Beijing 100029 China The First Research Institute of Ministry of Public Security Beijing 100048 China National Science Foundation Industry/University Cooperative Research Center of Intelligent Maintenance Systems University of Cincinnati Cincinnati OH 45221 USA China National Petroleum Corporation Beijing 100007 China

The currently prevalent machine performance degradation assessment techniques involve estimating a machine＇s current condition based upon the recognition of indications of failure features,which entail complete data collected in different ***,failure data are always hard to acquire,thus making those techniques hard to be *** this paper,a novel method which does not need failure history data is *** packet decomposition（WPD） is used to extract features from raw signals,principal component analysis（PCA） is utilized to reduce feature dimensions,and Gaussian mixture model（GMM） is then applied to approximate the feature space ***-channel confidence value（SCV） is calculated by the overlap between GMM of the monitoring condition and that of the normal condition,which can indicate the performance of ***,multi-channel confidence value（MCV）,which can be deemed as the overall performance index of multi-channel,is calculated via logistic regression（LR） and that the task of decision-level sensor fusion is also *** SCV and MCV can serve as the basis on which proactive maintenance measures can be taken,thus preventing machine *** method has been adopted to assess the performance of the turbine of a centrifugal compressor in a factory of Petro-China,and the result shows that it can effectively complete this *** proposed method has engineering significance for machine performance degradation assessment.

关键词： performance degradation assessment Gaussian mixture model logistic regression proactive maintenance sensor fusion

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A method of predicting visual detectability of low-velocity impact damage in composite structures based on logistic regression model

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Chinese Journal of Aeronautics 2021年第1期34卷 296-308页

作者： Fengyang JIANG Zhidong GUAN Zengshan LI Xiaodong WANG School of Aeronautic Science and Engineering Beihang UniversityBeijing 100083China

This paper proposed a new method for quantitative assessment of visual detectability of damage based on logistic regression,using the Probability of Detection(POD)as a *** were performed to establish the massive hit/miss data of visual *** investigations verified the reliability of the *** prediction function concluded comprises more than one flaw size parameters,including the depth and diameter of the *** results show that the depth and diameter of the dents are pivotal for the evaluation of detectability;the type of detection,the detection distance,and the qualifications of personnel are critical external factors to be *** function,with an accuracy rate of nearly 85%,is capable of predicting the visual detection probability of impact damage under various detection environments,which will provide a reference for the damage tolerance design of composite materials and field maintenance in the NonDestructive Testing(NDT)field.

关键词： Composite materials Damage tolerance Machine learning NDT logistic regression

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Landslide Susceptibility Mapping by Using Landslide RatioBased logistic regression:A Case Study in the Southern Taiwan

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Journal of Mountain Science 2015年第3期12卷 721-736页

作者： WU Chun-Hung Department of Water Resources Engineering and Conservation Feng Chia University

The object of the research is to compare the model performance and explain the error source of original logistic regression landslide susceptibility model(abbreviated as or-LRLSM) and landslide ratio-based logistic regression landslide susceptibility model(abbreviated as lr-LRLSM) in the Chishan watershed with a serious landslide disaster after 2009 Typhoon Morakot. The landslide inventory induced by 2009 Typhoon Morakot in South Taiwan is the main research material, while the Chishan watershed is the research area. Six variables, including elevation, slope, aspect, geological formation, accumulated rainfall, and bank erosion, were included in the two models. The performance of lr-LRLSM is better than that of or-LRLSM. The Cox & Snell R2, Nagelkerke R2 value, and the area under the relative operating characteristic curve(abbreviated as AUC) of lrLRLSM is larger than those of or-LRLSM, and the average correct ratio for the lr-LRLSM to predict landslide or non-landslide is larger than that of orLRLSM by 5.0%. The increase of the average correct ratio(abbreviated as ACR) difference from or-LRLSM to lr-LRLSM shows in slope, revised accumulated rainfall, aspect, geological formation and bank erosion variables, and only light decreases in elevation variable. The error sources of continuous variables in building the or-LRLSM is the dissimilarity between the distribution of landslide ratio and production of coefficient and characteristic values, while those of categorical variables is due to low correlation of landslide ratio and the coefficient value of each parameter. Using the classification of landslide ratio as the database to build logistic regression landslide susceptibility model(abbreviated as LRLSM) can revise the errors. The comparison of or-LRLSM and lr-LRLSM in the Chishan watershed also shows that building the landslide susceptibility model(abbreviated as LSM) by using lr-LRLSM is practical and of better performance than that by using the or-LRLSM.

关键词： logistic regression Landslidesusceptibility Landslide ratio Chishan watershed Typhoon Morakot

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Comparison of multinomial logistic regression and logistic regression： which is more efficient in allocating land use？

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Frontiers of Earth Science 2014年第4期8卷 512-523页

作者： Yingzhi LIN Xiangzheng DENG Xing LI Enjun MA School of Mathematics and Physics China University of Geosciences Wuhan 430074 China Institute of Geographic Sciences and Natural Resources Research Chinese Academy of Sciences Beijing 100101 China Center for Chinese Agricultural Policy Chinese Academy of Sciences Beijing 100101 China

Spatially explicit simulation of land use change is the basis for estimating the effects of land use and cover change on energy fluxes, ecology and the environment. At the pixel level, logistic regression is one of the most common approaches used in spatially explicit land use allocation models to determine the relationship between land use and its causal factors in driving land use change, and thereby to evaluate land use suitability. However, these models have a drawback in that they do not determine/ allocate land use based on the direct relationship between land use change and its driving factors. Consequently, a multinomial logistic regression method was introduced to address this flaw, and thereby, judge the suitability of a type of land use in any given pixel in a case study area of the Jiangxi Province, China. A comparison of the two regression methods indicated that the proportion of correctly allocated pixels using multinomial logistic regression was 92.98%, which was 8.47% higher than that obtained using logistic regression. Paired t-test results also showed that pixels were more clearly distinguished by multinomial logistic regression than by logistic regression. In conclusion, multinomial logistic regression is a more efficient and accurate method for the spatial allocation of land use changes. The application of this method in future land use change studies may improve the accuracy of predicting the effects of land use and cover change on energy fluxes, ecology, and environment.

关键词： multinomial logistic regression land usechange logistic regression land use suitability land useallocation

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Split Method of Multipliers and Its Application to Parallel and Distributed logistic regression

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Chinese Journal of Electronics 2014年第2期23卷 305-310页

作者： FENG Hui TANG Xianding YANG Tao HU Bo Department of Electronic Engineering Fudan University

We consider the scenario where two variables need to be optimized simultaneously. The minimization over one variable has an analytical solution, while it is intractable for the other. Under the Lagrangian dual framework, we propose two iterative optimization algorithms, which make partial minimization and gradient descent alternatingly over two variables. The first algorithm asserts that the iteration result converges to a KKT point under proper stepsize rules, which only needs the augmented Lagrangian function to be convex over partial variable. The second algorithm provides the local attraction property around the KKT point. Our algorithms provide a general solution to parallel and distributed optimization with summable objective functions. Simulation results on parallel and distributed logistic regression classification are present, which show faster convergence rate with less computational complexity compared with other methods.

关键词： Method of multiplier Primal-dual method Parallel optimization Distributed optimization logistic regression

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