检索结果-内蒙古大学图书馆

Collaborative filtering recommendation algorithm for e-commerce products based on Bayesian network

International Journal of Reasoning-based Intelligent Systems 2024年第4期16卷 331-336页

作者： Wan, Hongli School of Computer Dalian Neusoft University of Information Dalian116023 China

In order to effectively improve the recommendation accuracy of collaborative screening of e-commerce products, ensure the results of collaborative screening of e-commerce products, and reduce the time consumption of collaborative screening of e-commerce products, this project plans to study the recommendation algorithm for collaborative screening of e-commerce products based on Bayesian networks. Use an identical method to determine the user selection of e-commerce platform. On this basis, a new method was adopted to conduct an overall similarity analysis of products in e-commerce platforms. Then, using Bayesian networks and expected maximum methods, establish a model of user interest in e-commerce products on e-commerce platforms, thereby completing joint screening of e-commerce products. Through testing the joint screening of different types of e-commerce products, it was found that this method had a recommendation rate of 91.6% and an accuracy rate of 95.9% in the joint screening of e-commerce products. Copyright © 2024 Inderscience Enterprises Ltd.

关键词： expectation maximization algorithm

来源：评论

学校读者我要写书评

暂无评论

Dissemination of the Spirit of Public Security Models in the Age of Melting Media

引用

Applied Mathematics and Nonlinear Sciences 2024年第1期9卷

作者： Hou, Ming Investigation Department Henan Police College Henan Zhengzhou450046 China

Public security heroic model culture is a political-cultural phenomenon with Chinese characteristics formed by public security organs in the process of selecting, recognizing, publicizing, and appeasing heroic models. This paper researches the culture of public security heroic models and proposes a model for disseminating the spirit of public security heroic models through social role perception, called RAIN. The RAIN model unified the two complementary tasks of the information dissemination process and the social roles of users. The model maps the dissemination parameters between users and those between social roles, reducing the number of parameters. The parameters are estimated using a Gibbs sampling-based learning algorithm. At the same time, a continuous influence maximization algorithm is proposed under the independent cascade propagation model, which can calculate the incremental values in each direction by the gradient descent method and get the optimal solution of the objective function value. The communication status of the spirit of public security heroism is analyzed, and the total word frequency of all the communicated high-frequency words is 8350, which is lower than 10,000 and is at the lower-middle communication level. The total communication power value and total influence value of the spirit of public security heroes are 1042 and 4701, respectively, and the number of high communication power and high influence content is only 22 and 21, so there are obvious deficiencies in the work of communication field. This paper takes the shortcomings as a starting point and proposes a communication optimization strategy accordingly. © 2024 Ming Hou, published by Sciendo.

关键词： expectation maximization algorithm

来源：评论

学校读者我要写书评

暂无评论

A Bayesian latent class extension of naive Bayesian classifier and its application to the classification of gastric cancer patients

引用

BMC MEDICAL RESEARCH METHODOLOGY 2023年第1期23卷 190-190页

作者： Gohari, Kimiya Kazemnejad, Anoshirvan Mohammadi, Marjan Eskandari, Farzad Saberi, Samaneh Esmaieli, Maryam Sheidaei, Ali Tarbiat Modares Univ Fac Med Sci Dept Biostat Tehran Iran Pasteur Inst Iran Biotechnol Res Ctr Dept Med Biotechnol HPGC Res Grp Tehran Iran Allameh Tabatabai Univ Dept Stat Tehran Iran Univ Tehran Med Sci Sch Publ Hlth Dept Epidemiol & Biostat Tehran Iran

BackgroundThe Naive Bayes (NB) classifier is a powerful supervised algorithm widely used in Machine Learning (ML). However, its effectiveness relies on a strict assumption of conditional independence, which is often violated in real-world scenarios. To address this limitation, various studies have explored extensions of NB that tackle the issue of non-conditional independence in the data. These approaches can be broadly categorized into two main categories: feature selection and structure *** this particular study, we propose a novel approach to enhancing NB by introducing a latent variable as the parent of the attributes. We define this latent variable using a flexible technique called Bayesian Latent Class Analysis (BLCA). As a result, our final model combines the strengths of NB and BLCA, giving rise to what we refer to as NB-BLCA. By incorporating the latent variable, we aim to capture complex dependencies among the attributes and improve the overall performance of the *** expectation-maximization (EM) algorithm and the Gibbs sampling approach were offered for parameter learning. A simulation study was conducted to evaluate the classification of the model in comparison with the ordinary NB model. In addition, real-world data related to 976 Gastric Cancer (GC) and 1189 Non-ulcer dyspepsia (NUD) patients was used to show the model's performance in an actual application. The validity of models was evaluated using the 10-fold *** presented model was superior to ordinary NB in all the simulation scenarios according to higher classification sensitivity and specificity in test data. The NB-BLCA model using Gibbs sampling accuracy was 87.77 (95% CI: 84.87-90.29). This index was estimated at 77.22 (95% CI: 73.64-80.53) and 74.71 (95% CI: 71.02-78.15) for the NB-BLCA model using the EM algorithm and ordinary NB classifier, *** considering the modification of the NB classifier, incorporating a lat

关键词： Naive Bayesian classifier Bayesian latent class analysis Gibbs sampling expectation maximization algorithm Gastric cancer

来源：评论

学校读者我要写书评

暂无评论

A comparative study of different deep learning methods for time-series probabilistic residential load power forecasting

引用

Frontiers in Energy Research 2024年 12卷

作者： Zhou, Liangcai Zhou, Yi Liu, Linlin Zhao, Xiaoying East China Branch of State Grid Corporation Shanghai China AINERGY LLC Santa ClaraCA United States

The widespread adoption of nonlinear power electronic devices in residential settings has significantly increased the stochasticity and uncertainty of power systems. The original load power data, characterized by numerous irregular, random, and probabilistic components, adversely impacts the predictive performance of deep learning techniques, particularly neural networks. To address this challenge, this paper proposes a time-series probabilistic load power prediction technique based on the mature neural network point prediction technique, i.e., decomposing the load power data into deterministic and stochastic components. The deterministic component is predicted using deep learning neural network technology, the stochastic component is fitted with Gaussian mixture distribution model and the parameters are fitted using great expectation algorithm, after which the stochastic component prediction data is obtained using the stochastic component generation method. Using a mature neural network point prediction technique, the study evaluates six different deep learning methods to forecast residential load power. By comparing the prediction errors of these methods, the optimal model is identified, leading to a substantial improvement in prediction accuracy. Copyright © 2024 Zhou, Zhou, Liu and Zhao.

关键词： expectation maximization algorithm

来源：评论

学校读者我要写书评

暂无评论

Switching Dynamic State Estimation and Event Detection for Inverter-Based Resources with Multiple Control Modes

引用

IEEE Transactions on Power Systems 2024年

作者： Huang, Heqing Lin, Yuzhang

The Dynamic State Estimation (DSE) for Inverter-Based Resources (IBRs) is an emerging topic as IBRs gradually replace synchronous generators (SGs) in power systems. Unlike SGs, the dynamic models of IBRs heavily depend on their control algorithms, and conventional DSE methods for SGs, which assume a unchanged state space and dynamic model, cannot handle IBRs with control mode changes in real time, particularly when the power grid operators are unaware of the current control mode of the IBRs. In response to these challenges, an expectation-maximization Sliding-Window Iterated Extended Kalman Filter (EM-SW-IEKF) method is proposed in this paper. It theoretically achieves maximum likelihood estimation under different modes through the EM algorithm, providing the most probable control mode of the system as well as the corresponding state estimate. This method is validated in various IBR systems (battery energy storage systems and solar photovoltaic systems) and under different control mode transitions (switching between grid-following and grid-forming controls and between low voltage ride through and maximum power point tracking controls). © 1969-2012 IEEE.

关键词： expectation maximization algorithm

来源：评论

学校读者我要写书评

暂无评论

Haplotype association analysis of North American Rheumatoid Arthritis Consortium data using a generalized linear model with regularization

引用

BMC proceedings 2009年第7-Sup期3 Suppl 7卷 S32页

作者： Wei Guo Chin-Yuan Liang Shili Lin Department of Statistics The Ohio State University Columbus Ohio 43210 USA. guowei@stat.osu.edu.

The Genetic Analysis Workshop 16 rheumatoid arthritis data include a set of 868 cases and 1194 controls genotyped at 545,080 single-nucleotide polymorphisms (SNPs) from the Illumina 550 k chip. We focus on investigating chromosomes 6 and 18, which have 35,574 and 16,450 SNPs, respectively. Association studies, including single SNP and haplotype-based analyses, were applied to the data on those two chromosomes. Specifically, we conducted a generalized linear model with regularization (rGLM) approach for detecting disease-haplotype association using unphased SNP data. A total of 444 and 43 four-SNP tests were found to be significant at the Bonferroni corrected 5% significance level on chromosome 6 and 18, respectively.

关键词： expectation maximization algorithm Rare Haplotype Lasso Penalty North American Rheumatoid Arthritis Consortium Rheumatoid Arthritis Data

来源：评论

学校读者我要写书评

暂无评论

Mixture modeling of hospital charge in Zimbabwe

引用

HELIYON 2023年第4期9卷 e14771页

作者： Mangava, Nyasha Nadarajah, Saralees Univ Zimbabwe Dept Stat Harare Zimbabwe Howard Univ Dept Math Washington DC 20059 USA

Objective: Zimbabwe is one of the poorest countries in the world, just emerging from a broken health care system. The objective is to figure out variables affecting hospital charge for *** and methods: The variables used are sex, smoking status, number of children, region, age and body mass index. The first six of these are factors and the remaining are covariates. A mixture model was fitted to describe the dependence of hospital charge on these variables. Results: A mixture model with five components each having a reversed Gumbel distribution was found to give an adequate fit. Both the covariates and all but one of the factors were found to be significant. Estimates of value at risk of hospital charge are given for all combinations of the ***: The results suggest that the hospital charge could be higher for females, higher for smokers, higher if the patient had more children and higher if the patient is older. Further, estimates of value at risk given suggest, for example, that a 90 year old female not smoking and having no children and an average body mass index will have a hospital charge less than Z$49851 with probability 0.999.

关键词： Estimation expectation maximization algorithm Value at risk

来源：评论

学校读者我要写书评

暂无评论

Estimation of diffusion constants from single molecular measurement without explicit tracking

引用

BMC SYSTEMS BIOLOGY 2018年第Sup1期12卷 15-15页

作者： Teraguchi, Shunsuke Kumagai, Yutaro Tohoku Univ Tohoku Med Megabank Org Aoba Ku 2-1 Seiryo Machi Sendai Miyagi 9808573 Japan Osaka Univ Immunol Frontier Res Ctr Quantitat Immunol Res Unit 3-1 Yamada Oka Suita Osaka 5650871 Japan

Background: Time course measurement of single molecules on a cell surface provides detailed information about the dynamics of the molecules that would otherwise be inaccessible. To extract the quantitative information, single particle tracking (SPT) is typically performed. However, trajectories extracted by SPT inevitably have linking errors when the diffusion speed of single molecules is high compared to the scale of the particle density. Methods: To circumvent this problem, we develop an algorithm to estimate diffusion constants without relying on SPT. The proposed algorithm is based on a probabilistic model of the distance to the nearest point in subsequent frames. This probabilistic model generalizes the model of single particle Brownian motion under an isolated environment into the one surrounded by indistinguishable multiple particles, with a mean field approximation. Results: We demonstrate that the proposed algorithm provides reasonable estimation of diffusion constants, even when other methods suffer due to high particle density or inhomogeneous particle distribution. In addition, our algorithm can be used for visualization of time course data from single molecular measurements. Conclusions: The proposed algorithm based on the probabilistic model of indistinguishable Brownian particles provide accurate estimation of diffusion constants even in the regime where the traditional SPT methods underestimate them due to linking errors.

关键词： Brownian motion Diffusion constants expectation maximization algorithm Probabilistic model Single molecular measurement

来源：评论

学校读者我要写书评

暂无评论

3D variability analysis: Resolving continuous flexibility and discrete heterogeneity from single particle cryo-EM

引用

JOURNAL OF STRUCTURAL BIOLOGY 2021年第2期213卷 107702-107702页

作者： Punjani, Ali Fleet, David J. Univ Toronto Dept Comp Sci Toronto ON M5S 3G4 Canada Vector Inst 710661 Univ Ave Toronto ON M5G 1M1 Canada Struct Biotechnol Inc 129-100 Coll Ave Toronto ON M5G 1L5 Canada

Single particle cryo-EM excels in determining static structures of protein molecules, but existing 3D reconstruction methods have been ineffective in modelling flexible proteins. We introduce 3D variability analysis (3DVA), an algorithm that fits a linear subspace model of conformational change to cryo-EM data at high resolution. 3DVA enables the resolution and visualization of detailed molecular motions of both large and small proteins, revealing new biological insight from single particle cryo-EM data. Experimental results demonstrate the ability of 3DVA to resolve multiple flexible motions of a-helices in the sub-50 kDa transmembrane domain of a GPCR complex, bending modes of a sodium ion channel, five types of symmetric and symmetry-breaking flexibility in a proteasome, large motions in a spliceosome complex, and discrete conformational states of a ribosome assembly. 3DVA is implemented in the cryoSPARC software package.

关键词： Single particle analysis Electron microscopy Principle component analysis Continuous heterogeneity Image processing expectation maximization algorithm

来源：评论

学校读者我要写书评

暂无评论

ADAPTATIVE CONTEXT NORMALIZATION: A BOOST FOR DEEP LEARNING IN IMAGE PROCESSING

arXiv

引用

arXiv 2024年

作者： Faye, Bilal Azzag, Hanane Lebbah, Mustapha Bouchaffra, Djamel LIPN UMR CNRS 7030 Sorbonne Paris Nord University Villetaneuse France Paris-Saclay University UVSQ David Lab Versailles78035 France Center for Development of Advanced Technologies Algiers Algeria

Deep Neural network learning for image processing faces major challenges related to changes in distribution across layers, which disrupt model convergence and performance. Activation normalization methods, such as Batch Normalization (BN), have revolutionized this field, but they rely on the simplified assumption that data distribution can be modelled by a single Gaussian distribution. To overcome these limitations, Mixture Normalization (MN) introduced an approach based on a Gaussian Mixture Model (GMM), assuming multiple components to model the data. However, this method entails substantial computational requirements associated with the use of expectation-maximization algorithm to estimate parameters of each Gaussian components. To address this issue, we introduce Adaptative Context Normalization (ACN), a novel supervised approach that introduces the concept of "context", which groups together a set of data with similar characteristics. Data belonging to the same context are normalized using the same parameters, enabling local representation based on contexts. For each context, the normalized parameters, as the model weights are learned during the backpropagation phase. ACN not only ensures speed, convergence, and superior performance compared to BN and MN but also presents a fresh perspective that underscores its particular efficacy in the field of image processing. We release our code at https://***/b-faye/AdaptativeContext-Normalization. © 2024, CC BY.

关键词： expectation maximization algorithm

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：