Novel Adaptive Hidden Markov Model Utilizing Expectation–Maximization Algorithm for Advanced Pipeline Leak Detection by Omid Zadehbagheri, Mohammad Reza Salehizadeh, Seyed Vahid Naghavi, Mazda Moattari, Behzad Moshiri

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Identifying pesticide mixtures at country-wide scale by Cairo, Milena, Monnet, Anne-Christine, Robin, Stéphane, Porcher, Emmanuelle, Fontaine, Colin

Subjects: “…Active substances, Cluster, mixture model, expectation-maximization algorithm, risk assessment…”
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Optimizing Parameters for Enhanced Iterative Image Reconstruction Using Extended Power Divergence by Takeshi Kojima, Yusaku Yamaguchi, Omar M. Abou Al-Ola, Tetsuya Yoshinaga

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Joint symbol detection and channel estimation for MIMO-OFDM systems via the variational Bayes EM algorithm by ZHANG Xiao-ying, WEI Ji-bo, WANG De-gang, XIONG Chun-lin

Subjects: “…variational Bayes expectation maximization algorithm…”
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Extended target tracking with mobility based on GPR-AUKF by Renli Zhang, Yan Zhang, Jintao Chen, Ziwen Sun, Jing Li, Zhuangbin Tan, Zhongxing Jiao

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Bounded multivariate contaminated normal mixture model with applications to skin cancer detection by Abbas Mahdavi

“…A feasible expectation-maximization algorithm is developed to compute the maximum likelihood estimates of the model parameters using a selection mechanism. …”
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Joint user activity and signal detection for massive multiple-input multiple-output by Xiaoqun SONG, Ming JIN, Zhongjie JIA

“…In uplink grant-free massive multiple-input multiple-output (mMIMO) systems, the performance of available methods for joint user activity and signal detection deteriorates when the correlation of receiving antennas or the number of active devices increases.Moreover, the available methods require the knowledge of noise power, which is often practically unknown.To address the above issues, combining approximate message passing with unitary transformation and expectation maximization algorithm to jointly implement user activity and signal detection was proposed.Different from the conventional approximate message passing algorithm, the proposed one assumes that the noise power was unknown.Firstly, by exploiting the approximate message passing algorithm with unitary transform, the distribution of transmitted symbols together with the distribution of noise power was obtained.Secondly, expectation maximization algorithm was applied to estimate the user activity.Finally, the signal detection was implemented by deriving the posterior distribution of the decoupled signal belongs.Simulation results show that the proposed method is better than the traditional method in joint user activity and signal detection.…”
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A robust transfer learning approach for high-dimensional linear regression to support integration of multi-source gene expression data. by Lulu Pan, Qian Gao, Kecheng Wei, Yongfu Yu, Guoyou Qin, Tong Wang

“…Data integration, Variable selection, T distribution, Expectation maximization algorithm, Genotype-Tissue Expression, Cross validation.…”
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Stochastic Modeling with Poisson Hidden Markov in Hepatitis B Cases by Ersya Nurul Fairuz, Rina Widyasari, Rima Aprilia

“…The aim to be achieved in this research is to model Hepatitis B disease at the Medan Haji Hospital using the Poisson Hidden Markov model and to find parameter estimates using the Expectation Maximization Algorithm. This type of research uses quantitative research methods. …”
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A prediction algorithm of telecom customer churn based on Bayesian network parameters learning under incomplete data by Yuxiang ZHAO, Guangyue LU, Hanglong WANG, Siwei LI

“…Aiming at prediction of telecom customer churn,a novel method was proposed to increase the prediction accuracy with the missing data based on the Bayesian network.This method used k-nearest neighbor algorithm to fill the missing data and adds two types of monotonic influence constraints into the process of learning Bayesian network parameter.Simulations and actual data analysis demonstrate that the proposed algorithm obtains higher prediction accuracy of churn customers with the loss of less cost prediction accuracy of loyal customers,outperforms the classic expectation maximization algorithm.…”
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A distributed expectation maximization-principal component analysis monitoring scheme for the large-scale industrial process with incomplete information by Xuanyue Wang, Xu Yang, Jian Huang, Xianzhong Chen

“…Then, the missing information of variables is estimated by expectation maximization algorithm in the principal component analysis framework, then the expectation maximization-principal component analysis method is applied for fault detection to each sub-block. …”
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A Bivariate Extension of Type-II Generalized Crack Distribution for Modeling Heavy-Tailed Losses by Taehan Bae, Hanson Quarshie

“…For practical applications, three specific distributions, GCR2-Generalized Gaussian, GCR2-Student’s <i>t</i>, and GCR2-Logistic, are considered for marginals. The expectation-maximization algorithm is implemented to estimate the parameters in the bivariate GCR2 models. …”
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Brain MR image segmentation for tumor detection based on Riesz probability distributions by Mariem Tounsi, Mouna Zitouni

“…We used the Expectation-Maximization algorithm to estimate the mixture parameters. …”
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PEM-PCA: A Parallel Expectation-Maximization PCA Face Recognition Architecture by Kanokmon Rujirakul, Chakchai So-In, Banchar Arnonkijpanich

“…Thus, this research presents an alternative approach utilizing an Expectation-Maximization algorithm to reduce the determinant matrix manipulation resulting in the reduction of the stages’ complexity. …”
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A Bayesian Network Approach to Causation Analysis of Road Accidents Using Netica by Xin Zou, Wen Long Yue

“…By taking Adelaide Central Business District (CBD) in South Australia as a case, the Bayesian network structure was established by integrating K2 algorithm with experts’ knowledge, and Expectation-Maximization algorithm that could process missing data was adopted to conduct the parameter learning in Netica, thereby establishing the Bayesian network model for the causation analysis of road accidents. …”
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An Optimization Method of Multiclass Price Railway Passenger Transport Ticket Allocation under High Passenger Demand by Bin Wang, Shaoquan Ni, Fucai Jin, Zixu Huang

“…First, for the “censored data” problem in the railway passenger demand forecast, we constructed an unconstrained model of railway passenger demand and solved the unconstrained demand through an expectation-maximization algorithm. Then, on this basis, we use gray neural networks (GNNs) to predict the passenger demand of different origins and destinations (ODs), and according to the prediction results, we propose two ticket allocation methods based on operation and capacity control: accurate predivided model and fuzzy predivided model. …”
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First in Vivo SPECT Imaging of Mouse Femorotibial Cartilage Using Tc-NTP 15-5 by Elisabeth Miot-Noirault, Aurélien Vidal, Philippe Auzeloux, Jean-Claude Madelmont, Jean Maublant, Nicole Moins

“…Tomographic reconstruction of SPECT data was performed with a three-dimensional ordered subset expectation maximization algorithm, and slices were reconstructed in three axes. 99m Tc-NTP 15-5 rapidly accumulated in the joint, with a peak of radioactivity being reached from 5 minutes postinjection and maintained for at least 90 minutes. …”
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An Extendable Gaussian Mixture Model for Lane-Based Queue Length Estimation Based on License Plate Recognition Data by Chaofeng Tan, Hao Wu, Keshuang Tang, Chaopeng Tan

“…Then, the two-dimensional Gaussian distributions of queued vehicles and nonqueued vehicles were fitted, and the expectation-maximization algorithm was adopted to solve the distribution parameters. …”
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Effective Lock Detectors Based on Costas Loop Output for SBPSK Mobile Communications by D. Ferramola, T. Casilli, S. Pradella, G. Giunta, D. Orlando

“…Such estimates are obtained through an iterative strategy relying on the Expectation Maximization algorithm. Moreover, when possible, we provide exact statistical characterizations of the considered decision schemes (including the competitors) or, in the other case, suitable approximations that allow for a straightforward performance evaluation. …”
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Transformers deep learning models for missing data imputation: an application of the ReMasker model on a psychometric scale by Monica Casella, Nicola Milano, Pasquale Dolce, Davide Marocco

“…Traditional methods like mean imputation or regression, commonly used to handle missing data, rely upon assumptions that may not hold on psychological data and can lead to distorted results.MethodsThis study aims to evaluate the effectiveness of transformer-based deep learning for missing data imputation, comparing ReMasker, a masking autoencoding transformer model, with conventional imputation techniques (mean and median imputation, Expectation–Maximization algorithm) and machine learning approaches (K-nearest neighbors, MissForest, and an Artificial Neural Network). …”
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