Identification of polynomial models of static load characteristics based on passive experiment results by N. L. Batseva, A. K. Zhuykov

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Deep Neural Network for Cure Fraction Survival Analysis Using Pseudo Values by Ola Abuelamayem

“…Also, it has the advantage of analyzing the data without the need of EM algorithm. Comparing the results with cox proportional model using EM algorithm, the proposed neural network performed better. …”
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Reconstruction of Highway Vehicle Paths Using a Two-Stage Model by Weifeng Yin, Junyong Zhai, Yongbo Yu

“…The results indicate that the improved EM algorithm achieved convergence in 17 iterations compared to 41 iterations for the traditional EM algorithm. …”
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Improving Bayesian Model Averaging for Ensemble Flood Modeling Using Multiple Markov Chains Monte Carlo Sampling by Tao Huang, Venkatesh Merwade

“…However, the uncertainty in BMA parameters with fixed values, which are usually obtained from Expectation‐Maximization (EM) algorithm, has not been adequately investigated in BMA‐related applications over the past few decades. …”
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A joint likelihood estimator of relatedness and allele frequencies from a small sample of individuals by Jinliang Wang

“…I propose an expectation maximization (EM) algorithm to update allele frequencies and the nine condensed identical by descent (IBD) coefficients (∆i,i=1,2,…,9) of each pair of sampled individuals iteratively till convergence. …”
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Cancer Patients Missing Pain Score Information:- Application with Imputation Techniques by Gajendra Vishwakarma, Atanu Bhattacharjee, Jesna Jose, Ramesh V

“…Interpretation and Conclusion: The EM algorithm shows the least percentage change from observed values in both visits followed by predictive mean matching method and MCMC methods. …”
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Sensor Node Deployment Based on Electromagnetism-Like Algorithm in Mobile Wireless Sensor Networks by Recep Özdağ, Ali Karcı

“…Simulation results have shown that the EM algorithm can be preferred in the dynamic deployment of mobile sensors within the wireless networks.…”
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Markov Observation Models and Deepfakes by Michael A. Kouritzin

“…Once models have been learnt through the EM algorithm, deepfakes are generated through simulation, while they are detected using the log-likelihood. …”
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STEME: a robust, accurate motif finder for large data sets. by John E Reid, Lorenz Wernisch

“…We recently published an efficient approximation (STEME) to the EM algorithm that is at the core of many motif finders such as MEME. …”
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Artificial Intelligent Techniques with Watermarking by Nada Saleem, Baydaa Khaleel, Shahbaa Khaleel

“…The second developed <em>(RBFW)</em> algorithm used <em>RBF</em> neural network for embedding and extracting of watermark based on intensity of whole image. …”
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Research on Adaptive Optics Image Restoration Algorithm by Improved Expectation Maximization Method by Lijuan Zhang, Dongming Li, Wei Su, Jinhua Yang, Yutong Jiang

“…To improve the effect of adaptive optics images’ restoration, we put forward a deconvolution algorithm improved by the EM algorithm which joints multiframe adaptive optics images based on expectation-maximization theory. …”
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Treatment of Missing Market Data: Case of bond Yield Curve Estimation by M. S. Makushkin, V. A. Lapshin

“…We compare three methods of missing data imputation — last observation carried forward, Kalman filtering and EM–algorithm — with a simple strategy of ignoring missing observations. …”
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A receive-coherent detector for airborne distributed coherent aperture radar under heterogeneous clutter and random phase errors by Xiaochuan Liu, Dongming Zhou, Hongwei Gao, Yaobing Lu

“…Then, a two-step RC generalized likelihood ratio test (GLRT) detector with a simplified expectation maximization (EM) algorithm (2S-RVEM-G) is proposed, which iteratively solves for the unknown parameters under the alternative hypotheses by the EM algorithm to achieve target detection of ADCAR under heterogeneous clutter and RPE. …”
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Leveraging mixed-effects regression trees for the analysis of high-dimensional longitudinal data to identify the low and high-risk subgroups: simulation study with application to g... by Mina Jahangiri, Anoshirvan Kazemnejad, Keith S. Goldfeld, Maryam S. Daneshpour, Mehdi Momen, Shayan Mostafaei, Davood Khalili, Mahdi Akbarzadeh

“…Additionally, the Ev-RE-EM algorithm generated smaller trees than the unbiased RE-EM algorithm, enhancing tree interpretability. …”
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An Improved Gaussian Mixture Model-Based Data Normalization Method for Removing Environmental Effects on Damage Detection of Structures by Xue-Yang Pei, Hai-Bin Huang, Peng Cao

“…Through the application of the EM algorithm, the GMM is constructed simply and efficiently through the determined initial parameters. …”
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Estimating multiplicity of infection, haplotype frequencies, and linkage disequilibria from multi-allelic markers for molecular disease surveillance. by Henri Christian Junior Tsoungui Obama, Kristan Alexander Schneider

“…Estimates are based on maximum likelihood using the expectation-maximization (EM)-algorithm. The estimates can be used as plug-ins to construct pairwise linkage disequilibrium (LD) maps. …”
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Risk-adaptive access control model for big data in healthcare by Zhen HUI, Hao LI, Min ZHANG, Deng-guo FENG

“…While dealing with the big data in healthcare,it was difficult for a policy maker to foresee what information a doctor may need,even to make an accurate access control policy.To deal with it,a risk-based access control model that regulates doctors’ access rights adaptively was proposed to protect patient privacy.This model analyzed the history of access,applies the EM algorithm and the information entropy technique to quantify the risk of privacy violation.Using the quantified risk,the model can detect and control the over-accessing and exceptional accessing of patients’ data.Experimental results show that this model is effective and more accurate than other models.…”
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GMM-based localization algorithm under NLOS conditions by Wei CUI, Cheng-dong WU, Yun-zhou ZHANG, Zi-xi JIA, Long CHENG

“…Aiming at indoor node localizations of WSN,a node localization algorithm,where priori-knowledge is not necessary,was proposed.on basis of analyzing the error model,combined with Gaussian mixture model (GMM).By training the distance measurements containing NLOS errors,the more accurate range estimations can be obtained.For higher localization accuracy,the particle swarm optimization (PSO) was introduced to optimize the expectation-maximization (EM)algorithm.Finally,by using the residual weighting algorithm to estimate the distance,the estimation coordinates of target nodes can be determined.The proposed algorithm was proved to be effective through simulation experiments.…”
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Risk-adaptive access control model for big data in healthcare by Zhen HUI, Hao LI, Min ZHANG, Deng-guo FENG

“…While dealing with the big data in healthcare,it was difficult for a policy maker to foresee what information a doctor may need,even to make an accurate access control policy.To deal with it,a risk-based access control model that regulates doctors’ access rights adaptively was proposed to protect patient privacy.This model analyzed the history of access,applies the EM algorithm and the information entropy technique to quantify the risk of privacy violation.Using the quantified risk,the model can detect and control the over-accessing and exceptional accessing of patients’ data.Experimental results show that this model is effective and more accurate than other models.…”
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Estimation of Parameters of Finite Mixture of Rayleigh Distribution by the Expectation-Maximization Algorithm by Noor Mohammed, Fadhaa Ali

“…This paper aims to infer model parameters by the expectation-maximization (EM) algorithm through the maximum likelihood function. …”
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