检索结果-内蒙古大学图书馆

Investigating the evolutionary dynamics of second-line Mycobacterium tuberculosis drug resistance in Tanzania using hypercubic modelling and the baum-welch algorithm

引用

SCIENTIFIC AFRICAN 2025年 28卷

作者： Leandry, Leonce Mujuni, Egbert Mureithi, Eunice W. Brun, Morten Mayige, Mary Univ Dar Es Salaam Dept Math Dar Es Salaam Tanzania Univ Bergen Dept Math Bergen Norway Jordan Univ Coll Dept Econ Math & Stat Morogoro Tanzania Natl Inst Med Res Dar Es Salaam Tanzania

Mycobacterium tuberculosis (MTB) continues to pose a significant threat to public health, particularly with the emergence of drug-resistant strains. Research shows that first-line antituberculosis drugs are increasingly failing, and second-line drugs are also showing resistance. This study investigates the evolutionary dynamics of second-line drugs used against MTB, specifically Bedaquiline, Delamanid, Linezolid, Clofazimine, and Levofloxacin. Data were collected from the Tanzania National Institute of Medical Research (NIMR) at the Central Tuberculosis Reference Laborator Muhimbili Centre (CTRL). The data were analysed using a 5-hypercubic model, with parameters estimated using the baum-welch algorithm. The findings show that the most probable drug-resistant acquisition, independent of other drugs analysed, is Bedaquiline with a probability of 0.8660 and Levofloxacin with a probability of 0.134. The evolutionary pattern begins with Bedaquiline, followed by Levofloxacin, then Clofazimine, and finally either Linezolid or Delamanid, each with an equal probability of occurring. This highlights the evolutionary patterns of drug resistance, providing insights that can inform health experts and policymakers in developing evidence-based, effective interventions to combat this growing public health challenge.

关键词： Hypercubes Tuberculosis baum-welch algorithm Second-line MTB drugs Hidden Markov model Drug resistance

来源：评论

学校读者我要写书评

暂无评论

Modelling the evolutionary dynamics of drug resistance in tuberculosis using hypercube and baum-welch algorithm inference: A case study of Tanzania

引用

Heliyon 2025年第8期11卷

作者： Leandry, Leonce Mujuni, Egbert Brun, Morten Mureithi, Eunice W. Senkoro, Mbazi Mfaume, Saidi Department of Mathematics University of Dar es Salaam Dar es Salaam Tanzania Department of Mathematics University of Bergen Bergen Norway Department of Economics Mathematics and Statistics Jordan University College Morogoro Tanzania National Institute for Medical Research Muhimbili Centre Dar es Salaam Tanzania Central Tuberculosis Reference Laboratory National Institute for Medical Research Dar es Salaam Tanzania

Drug resistance, particularly multidrug-resistant tuberculosis (MDR-TB) and extensively drug-resistant forms of tuberculosis (XDR-TB), amplifies the global health problem of tuberculosis (TB). To address some of these issues, the study investigated the evolutionary dynamics of drug resistance using a 6-hypercubic model, Hidden Markov Model (HMM) and the baum-welch algorithm for inference. Data used in this study were collected in Tanzania at the Central Tuberculosis Reference Laboratory (CTRL) from September 2021 to January 2024. The collected sample consisted of 185 individual samples. Of these, 142 (77%) tested positive for Mycobacterium tuberculosis (MTB), while 43 (23%) tested negative and were excluded from the analysis. The analysis was conducted on six drugs tested for resistance or sensitivity: isoniazid (INH), fluoroquinolones (FLQ), amikacin (AMK), kanamycin (KAN), capreomycin (CAP), and ethionamide (ETH). The findings indicate that the most probable drug-resistance acquisition, independent of other analysed drugs, occurs in INH, followed by FLQ, AMK, KAN, CAP, and finally ETH, highlighting the need for enhanced surveillance and targeted interventions to address the growing threat of drug-resistant TB. This is crucial for improving TB management and control efforts in resource constrained settings. © 2025 The Authors

关键词： Antimicrobial resistance baum-welch algorithm Hidden Markov Model (HMM) Hypercubes Tanzania Tuberculosis

来源：评论

学校读者我要写书评

暂无评论

baum-welch algorithm on directed acyclic graph for mixtures with latent Bayesian networks

引用

STAT 2017年第1期6卷 303-314页

作者： Li, Jia Lin, Lin Penn State Univ Dept Stat University Pk PA 16802 USA

We consider a mixture model with latent Bayesian network (MLBN) for a set of random vectors X-(t), X-(t) is an element of R-dt, t = 1, ..., T. Each X-(t) is associated with a latent state s(t), given which X-(t) is conditionally independent from other variables. The joint distribution of the states is governed by a Bayes net. Although specific types of MLBN have been used in diverse areas such as biomedical research and image analysis, the exact expectation-maximization (EM) algorithm for estimating the models can involve visiting all the combinations of states, yielding exponential complexity in the network size. A prominent exception is the baum-welch algorithm for the hidden Markov model, where the underlying graph topology is a chain. We hereby develop a new baum-welch algorithm on directed acyclic graph (BW-DAG) for the general MLBN and prove that it is an exact EM algorithm. BW-DAG provides insight on the achievable complexity of EM. For a tree graph, the complexity of BW-DAG is much lower than that of the brute-force EM. Copyright (c) 2017 John Wiley & Sons, Ltd.

关键词： baum-welch algorithm Bayesian network directed acyclic graph EM algorithm hidden Markov model maximum likelihood estimation

来源：评论

学校读者我要写书评

暂无评论

Hybrid approach of baum-welch algorithm and SVM for sensor fault diagnosis in healthcare monitoring system

引用

JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2022年第4期42卷 2979-2988页

作者： Anandhalekshmi, A., V Rao, V. Srinivasa Kanagachidambaresan, G. R. Vel Tech Rangarajan Dr Sagunthala R&D Inst Sci & Dept CSE Chennai Tamil Nadu India

Internet of Things (IoT) based healthcare monitoring system is becoming the present and the future of the medical field around the world. Here the monitoring system acquires the regular health details of hospital discharged patients like elderly patients, patients out of critical operations, and patients from remote areas, etc., and transmits it to the doctors. But the system is highly susceptible to sensor faults. Hence a data-driven hybrid approach of Hidden Markov Model (HMM) based on baum-welch algorithm with Support Vector Machine (SVM) is proposed to predict the abnormality caused by the medical sensors. The proposed work first perform the abnormality detection on the sensor data using the HMM based on baum-welch algorithm in which the normal data is separated from abnormal data followed by classifying the abnormal data as critical patient data or sensor fault data using the SVM. Here the proposed work efficiently performs fault diagnosis with an overall accuracy of 99.94% which is 0.59% better than the existing SVM model. And also a comparison is made between the hybrid approach and the existing ML algorithms in terms of recall and Fl-score where the proposed approach outperforms the other algorithms with a recall value of 100% and Fl -score of 99.7%.

关键词： Internet of Things Healthcare Fault diagnosis Hidden Markov Model baum-welch algorithm Support Vector Machine

来源：评论

学校读者我要写书评

暂无评论

A modified baum-welch algorithm for hidden Markov models with multiple observation spaces

引用

IEEE TRANSACTIONS ON SPEECH AND AUDIO PROCESSING 2001年第4期9卷 411-416页

作者： Baggenstoss, PM USN Undersea Warfare Ctr Newport RI 02841 USA

In this paper, we derive an algorithm similar to the well-known baum-welch algorithm for estimating the parameters of a hidden Markov model (HMM). The new algorithm allows the observation PDF of each state to be defined and estimated using: a different feature set, we show that estimating parameters in this manner is equivalent to maximizing the likelihood function for the standard parameterization of the HMM defined on the input data space, The processor becomes optimal if the state-dependent feature sets are sufficient statistics to distinguish each state individually from a common state.

关键词： baum-welch algorithm class-specific EM algorithm expectation-maximization Gaussian mixtures hidden Markov model (HMM) parameter estimation sufficient statistics

来源：评论

学校读者我要写书评

暂无评论

Classification of linear and non-linear modulations using the baum-welch algorithm and MCMC methods

引用

SIGNAL PROCESSING 2010年第12期90卷 3242-3255页

作者： Puengnim, Anchalee Thomas, Nathalie Tourneret, Jean-Yves Vidal, Josep IRIT ENSEEIHT TeSA F-31071 Toulouse 7 France Univ Politecn Cataluna ES-08034 Barcelona Spain

Satellite transmissions classically use constant amplitude linear modulation schemes, such as M-state phase shift keying (M-PSK), because of their high robustness to amplifier non-linearities. However, other modulation formats are interesting in a satellite transmission context. For instance, non-linear modulations such as Gaussian minimum shift keying (GMSK) present a higher spectral efficiency and appear in new standards for telemetry/telecommand satellite links. Another example is offset-QPSK (OQPSK) modulation that allows one to decrease the out-of-band interference due to band limiting and the non-linearity of the amplifier. To get a compromise between the robustness to amplifier non-linearities provided by MPSK modulation and the spectral efficiency given by QAM modulation, the recent broadcasting satellite standard (DVB-S2) proposes new modulation schemes called APSK. Obviously, all satellite systems that use various modulation schemes will have to co-exist. In this context, modulation recognition using the received communication signal is essential. In that context, this paper studies two Bayesian classifiers to recognize linear and non-linear modulations. These classifiers estimate the posterior probabilities of the received signal, given each possible modulation, and plug them into the optimal Bayes decision rule. Two algorithms are used for that purpose. The first one generates samples distributed according to the posterior distributions of the possible modulations using Markov chain Monte Carlo (MCMC) methods. The second algorithm estimates the posterior distribution of the possible modulations using the baum-welch (BW) algorithm. The performance of the resulting classifiers is assessed through several simulation results. (C) 2010 Elsevier B.V. All rights reserved.

关键词： Digital modulation classification Linear and non-linear modulations Bayesian classifiers Markov chain Monte Carlo methods baum-welch algorithm

来源：评论

学校读者我要写书评

暂无评论

Continuous Phase Modulation Classification via baum-welch algorithm

引用

IEEE COMMUNICATIONS LETTERS 2018年第7期22卷 1390-1393页

作者： Zhang, Jingwen Wang, Fanggang Zhong, Zhangdui Wang, Shilian Beijing Jiaotong Univ State Key Lab Rail Traff Control & Safety Beijing 100044 Peoples R China Natl Univ Def Technol Sch Elect Sci & Engn Changsha 410073 Hunan Peoples R China

Due to high spectral and power efficiency, continuous phase modulation (CPM) is widely adopted in wireless communications, such as satellite communications. In this letter, we investigate the classification of CPM signals in the presence of unknown fading channels. The time-varying phases of CPM are first formulated as a hidden Markov model (HMM) by observing its memorable property. Then, a likelihood-based classifier is proposed using the baum-welch algorithm, which is able to estimate the unknown parameters in the HMM. Simulation results show that the proposed algorithm outperforms the existing scheme using approximate entropy in terms of classification performance.

关键词： baum-welch algorithm continuous phase modulation modulation classification

来源：评论

学校读者我要写书评

暂无评论

Statistical and Computational Guarantees for the baum-welch algorithm

引用

JOURNAL OF MACHINE LEARNING RESEARCH 2017年第1期18卷 4528-4580页

作者： Yang, Fanny Balakrishnan, Sivaraman Wainwright, Martin J. Univ Calif Berkeley Dept Elect Engn & Comp Sci Berkeley CA 94720 USA Carnegie Mellon Univ Dept Stat Pittsburgh PA 15213 USA Univ Calif Berkeley Dept Stat Dept Elect Engn & Comp Sci Berkeley CA 94720 USA

The Hidden Markov Model (HMM) is one of the mainstays of statistical modeling of discrete time series, with applications including speech recognition, computational biology, computer vision and econometrics. Estimating an HMM from its observation process is often addressed via the baum-welch algorithm, which is known to be susceptible to local optima. In this paper, we first give a general characterization of the basin of attraction associated with any global optimum of the population likelihood. By exploiting this characterization, we provide non-asymptotic finite sample guarantees on the baum-welch updates and show geometric convergence to a small ball of radius on the order of the minimax rate around a global optimum. As a concrete example, we prove a linear rate of convergence for a hidden Markov mixture of two isotropic Gaussians given a suitable mean separation and an initialization within a ball of large radius around (one of) the true parameters. To our knowledge, these are the first rigorous local convergence guarantees to global optima for the baum-welch algorithm in a setting where the likelihood function is nonconvex. We complement our theoretical results with thorough numerical simulations studying the convergence of the baum-welch algorithm and illustrating the accuracy of our predictions.

关键词： Hidden Markov Models baum-welch algorithm EM algorithm non-convex optimization graphical models

来源：评论

学校读者我要写书评

暂无评论

Modeling of In-House CENELEC A-Band PLC Channel using Fritchman Model and baum-welch algorithm

Modeling of In-House CENELEC A-Band PLC Channel using Fritch...

引用

17th IEEE International Symposium on Power Line Communications and its Applications (ISPLC)

作者： Familua, Ayokunle Damilola Cheng, Ling Univ Witwatersrand Sch Elect & Informat Engn ZA-2050 Johannesburg South Africa

ISBN: (纸本)9781467360166;9781467360142

One of the many challenges which power line communication (PLC) has to overcome is noise and disturbances. Amidst the four frequency bands classified by the European CENELEC standard, the A-band is the most noise susceptible characterized by noise sources consisting of various electrical appliances. This paper aims at modeling the noise and disturbances present on the in-house CENELEC A-band based on experimental measurement. The outputs are the channel models of the three major types of noise present on the PLC. These models are achieved through the use of Fritchman model, to depict the power line channel and evaluate the noise impairment caused by the different types of noise. The baum-welch algorithm is implemented for the Fritchman model parameter estimation through likelihood evaluation by computing the probabilities of three different noise observation sequences obtained from the experimental measurement. These channel models can then be used to evaluate and optimize coding and modulation schemes for PLC.

关键词： baum-welch algorithm CENELEC Fritchman model noise measurement power line communications

来源：评论

学校读者我要写书评

暂无评论

Quality Control of Information Engineering Surveillance Based on baum-welch algorithm

引用

International Conference on Mechanical Engineering, Industry and Manufacturing Engineering (MEIME 2011)

作者： Liu, Hongzhi Gao, Li Beijing Technol & Business Univ Coll Comp & Informat Engn Beijing 100048 Peoples R China

ISBN: (纸本)9783037851371

A new method of Quality Control for Information Engineering Surveillance based on Hidden Markov Model (HMM) has been proposed and the related model been built by us. The process of information engineering quality surveillance can be seen as a two-layered random process. The five elements of HMM correspond with the process of quality surveillance through abstracting the characteristics of the surveillance process. Software quality can be estimated under the model. In this paper, we divided the five elements. Therefore, the model was improved from single dimension to multi-dimension, trained by baum-welch algorithm. Experimental results show that the proposed model proves to be feasible and real-time when it is used for quality control.

关键词： Information Engineering Surveillance Quality Control Hidden Markov Model (HMM) baum-welch 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：