Emoti-Shing: Detecting Vishing Attacks by Learning Emotion Dynamics through Hidden Markov Models

This study examines vishing, a form of social engineering scam using voice communication to deceive individuals into revealing sensitive information or losing money. With the rise of smartphone usage, people are more susceptible to vishing attacks. The proposed Emoti-Shing model analyzes potential victims’ emotions using Hidden Markov Models to track vishing scams by examining the emotional content of phone call audio conversations. This approach aims to detect vishing scams using biological features of humans, specifically emotions, which cannot be easily masked or spoofed. Experimental results on 30 generated emotions indicate the potential for increased vishing scam detection through this approach.

Morpho-Syntactic Tagging of Text in “Baoule” Language Based on Hidden Markov Models (HMM)

The label text is a very important tool for the automatic processing of language. It is used in several applications such as morphological and syntactic text analysis, index-ing, retrieval, finished networks deterministic (in which all combinations of words that are accepted by the grammar are listed) or by statistical grammars (e.g., an n-gram in which the probabilities of sequences of n words in a specific order are given), etc. In this article, we developed a morphosyntactic labeling system language “Baoule” using hidden Markov models. This will allow us to build a tagged reference corpus and rep-resent major grammatical rules faced “Baoule” language in general. To estimate the parameters of this model, we used a training corpus manually labeled using a set of morpho-syntactic labels. We then proceed to an improvement of the system through the re-estimation procedure parameters of this model.

An Intrusion Detection Method Based on Hierarchical Hidden Markov Models

This paper presents an anomaly detection approach to detect intrusions into computer systems. In this approach, a hierarchical hidden Markov model （HHMM） is used to represent a temporal profile of normal behavior in a computer system. The HHMM of the norm profile is learned from historic data of the system＇s normal behavior. The observed behavior of the system is analyzed to infer the probability that the HHMM of the norm profile supports the observed behavior. A low probability of support indicates an anomalous behavior that may result from intrusive activities. The model was implemented and tested on the UNIX system call sequences collected by the University of New Mexico group. The testing results showed that the model can clearly identify the anomaly activities and has a better performance than hidden Markov model.

An Examination of Male and Female Monthly Employment Rates over Time in Canada and the United States Using Hidden Markov Probability Models

In this paper, we will illustrate the use and power of Hidden Markov models in analyzing multivariate data over time. The data used in this study was obtained from the Organization for Economic Co-operation and Development (OECD. Stat database url: https://stats.oecd.org/) and encompassed monthly data on the employment rate of males and females in Canada and the United States (aged 15 years and over;seasonally adjusted from January 1995 to July 2018). Two different underlying patterns of trends in employment over the 23 years observation period were uncovered.

Hidden Markov Models to Estimate the Lagged Effects of Weather on Stroke and Ischemic Heart Disease

The links between low temperature and the incidence of disease have been studied by many researchers. What remains still unclear is the exact nature of the relation, especially the mechanism by which the change of weather effects on the onset of diseases. The existence of lag period between exposure to temperature and its effect on mortality may reflect the nature of the onset of diseases. Therefore, to assess lagged effects becomes potentially important. The most of studies on lags used the method by Lag-distributed Poisson Regression, and neglected extreme case as random noise to get correlations. In order to assess the lagged effect, we proposed a new approach, i.e., Hidden Markov Model by Self Organized Map (HMM by SOM) apart from well-known regression models. HMM by SOM includes the randomness in its nature and encompasses the extreme cases which were neglected by auto-regression models. The daily data of the number of patients transported by ambulance in Nagoya, Japan, were used. SOM was carried out to classify the meteorological elements into six classes. These classes were used as “states” of HMM. HMM was used to describe a background process which might produce the time series of the incidence of diseases. The background process was considered to change randomly weather states, classified by SOM. We estimated the lagged effects of weather change on the onset of both cerebral infarction and ischemic heart disease. This fact is potentially important in that if one could trace a path in the chain of events leading from temperature change to death, one might be able to prevent it and avert the fatal outcome.

Fully Polarimetric Land Cover Classification Based on Hidden Markov Models Trained with Multiple Observations

A land cover classification procedure is presented utilizing the information content of fully polarimetric SAR images. The Cameron coherent target decomposition (CTD) is employed to characterize each pixel, using a set of canonical scattering mechanisms in order to describe the physical properties of the scatterer. The novelty of the proposed classification approach lies on the use of Hidden Markov Models (HMM) to uniquely characterize each type of land cover. The motivation to this approach is the investigation of the alternation between scattering mechanisms from SAR pixel to pixel. Depending on the observations-scattering mechanisms and exploiting the transitions between the scattering mechanisms we decide upon the HMM-land cover type. The classification process is based on the likelihood of observation sequences been evaluated by each model. The performance of the classification approach is assessed my means of fully polarimetric SLC SAR data from the broader area of Vancouver, Canada and was found satisfactory, reaching a success from 87% to over 99%.

Hidden Markov Models for Automatic Speech Recognition

In this paper the authors look into the problem of Hidden Markov Models （HMM）： the evaluation, the decoding and the learning problem. The authors have explored an approach to increase the effectiveness of HMM in the speech recognition field. Although hidden Markov modeling has significantly improved the performance of current speech-recognition systems, the general problem of completely fluent speaker-independent speech recognition is still far from being solved. For example, there is no system which is capable of reliably recognizing unconstrained conversational speech. Also, there does not exist a good way to infer the language structure from a limited corpus of spoken sentences statistically. Therefore, the authors want to provide an overview of the theory of HMM, discuss the role of statistical methods, and point out a range of theoretical and practical issues that deserve attention and are necessary to understand so as to further advance research in the field of speech recognition.

The on-line direct fitting of low signal-noise ratio single ion channel recordings based on hidden Markov models

Many kinds of channel currents are especially weak and the background noise dominates in the patch clamp recordings. This makes the threshold detection fail during estimating of the transition probabilities. So direct fitting of the patch clamp recording, not of the histogram coming from the recordings, is a desirable way to estimate the transition probabilities. Iterative batch EM algorithm based on hidden markov model has been used in this field but which has the "curse of dimensionality" and besides cant keep tracking the varying of the parameters. A new on line sequential iterative one is proposed here, which needs fewer computational efforts and can adaptively keep tracking the varying of parameters. Simulations suggest its robust, effective and convenient.

Hidden Markov Models and Self-Organizing Maps Applied to Stroke Incidence

Several studies were devoted to investigate the effects of meteorological factors on the occurrence of stroke. Regression models had been mostly used to assess the correlation between weather and stroke incidence. However, these methods could not describe the process proceeding in the back-ground of stroke incidence. The purpose of this study was to provide a new approach based on Hidden Markov Models (HMMs) and self-organizing maps (SOM), interpreting the background from the viewpoint of weather variability. Based on meteorological data, SOM was performed to classify weather patterns. Using these classes by SOM as randomly changing “states”, our Hidden Markov Models were constructed with “observation data” that were extracted from the daily data of emergency transport at Nagoya City in Japan. We showed that SOM was an effective method to get weather patterns that would serve as “states” of Hidden Markov Models. Our Hidden Markov Models provided effective models to clarify background process for stroke incidence. The effectiveness of these Hidden Markov Models was estimated by stochastic test for root mean square errors (RMSE). “HMMs with states by SOM” would serve as a description of the background process of stroke incidence and were useful to show the influence of weather on stroke onset. This finding will contribute to an improvement of our understanding for links between weather variability and stroke incidence.

Application of Hidden Markov Models in Stock Forecasting

In this paper,we tested our methodology on the stocks of four representative companies:Apple,Comcast Corporation(CMCST),Google,and Qualcomm.We compared their performance to several stocks using the hidden Markov model(HMM)and forecasts using mean absolute percentage error(MAPE).For simplicity,we considered four main features in these stocks:open,close,high,and low prices.When using the HMM for forecasting,the HMM has the best prediction for the daily low stock price and daily high stock price of Apple and CMCST,respectively.By calculating the MAPE for the four data sets of Google,the close price has the largest prediction error,while the open price has the smallest prediction error.The HMM has the largest prediction error and the smallest prediction error for Qualcomm’s daily low stock price and daily high stock price,respectively.

Ontology mapping based on hidden Markov model

The existing ontology mapping methods mainly consider the structure of the ontology and the mapping precision is lower to some extent. According to statistical theory, a method which is based on the hidden Markov model is presented to establish ontology mapping. This method considers concepts as models, and attributes, relations, hierarchies, siblings and rules of the concepts as the states of the HMM, respectively. The models corresponding to the concepts are built by virtue of learning many training instances. On the basis of the best state sequence that is decided by the Viterbi algorithm and corresponding to the instance, mapping between the concepts can be established by maximum likelihood estimation. Experimental results show that this method can improve the precision of heterogeneous ontology mapping effectively.

基于GRA-ISM-HMM的广州市肉及肉制品安全风险评估

该研究旨在利用广州食品安全风险监测2015年至2020年针对肉及肉制品样本的检测数据,构建肉及肉制品的安全风险评估模型,从而了解广州市该段时间内肉及肉制品的食品安全风险及其时变特点。该研究采取灰色关联分析方法和解释结构模型建立风险指数,并基于该指标值作为隐马尔可夫模型的观测值探讨观测背后的隐含风险状态。分析结果显示,2015~2020年所有样本综合风险指数结果都在[0,0.45]之间,总体风险都较小,其中2019年风险最高;将风险指数进行等级划分,显示2015~2020年风险等级为[1,2,2,2,3,1];通过HMM分析得到这六年的隐藏风险等级为[0,1,1,1,2,0],与观测风险结果一致,且HMM预测2021年风险等级为1,即表明广州肉及肉制品风险往良好态势发展。最后,进行风险差异原因分析,发现各肉制品分类之间有差异,其中腊肠、鸡肉和腊肉的风险指数较高于其他种类,而2019年增加腊肠和腊肉的检测是风险增加的一个原因。总体来说,广州肉及肉制品风险较小,但依旧需要监督改善。

Optimal state and branch sequence based parameter estimation of continuous hidden Markov model

A parameter estimation algorithm of the continuous hidden Markov model isintroduced and the rigorous proof of its convergence is also included. The algorithm uses theViterbi algorithm instead of K-means clustering used in the segmental K-means algorithm to determineoptimal state and branch sequences. Based on the optimal sequence, parameters are estimated withmaximum-likelihood as objective functions. Comparisons with the traditional Baum-Welch and segmentalK-means algorithms on various aspects, such as optimal objectives and fundamentals, are made. Allthree algorithms are applied to face recognition. Results indicate that the proposed algorithm canreduce training time with comparable recognition rate and it is least sensitive to the training set.So its average performance exceeds the other two.

基于PE-HMM的渡槽结构运行状态评价

随着远距离、高流量、大跨度渡槽工程的不断发展,渡槽运行状态监测与评价日益重要。以广东省罗定市长岗坡渡槽工程为例,基于渡槽泄流振动位移数据,提出一种基于排列熵算法(PE)和隐马尔可夫模型(HMM)的渡槽运行状态评价方法。首先,运用排列熵算法和K-means法提取振动位移数据基本特征,形成HMM模型的观测状态序列。其次,运用HMM算法训练模型参数,以平均误差百分比为指标,筛选出最佳模型参数,并以该参数为初值再次训练得到渡槽运行期隐状态的概率分布。最后,结合渡槽运行期隐状态对应的分值等级及概率值,求得渡槽运行状态期望值,从而量化评价渡槽运行状态。结果表明,基于PE-HMM法的渡槽运行状态评价结果与实地勘察结论一致,可见PE-HMM法能够从渡槽振动位移数据角度出发,真实反映渡槽结构运行状态,具有较高的评判精度与工程指导意义。

基于DHMM-ESUM的露天矿运输系统车铲比优化研究

在露天矿生产中,如何利用现有的资源条件对电铲与卡车进行合理配比,充分发挥设备的效率,是提高矿山企业生产效益的关键。以安家岭露天矿生产系统收集的数据为基础,首先运用排队理论构建以电铲为采掘中心,卡车为运输工具的闭合排队网络模型,将运输系统分为4个排队子系统,分析各项服务时间的概率分布;基于离散隐马尔可夫模型(discrete hidden Markov model,DHMM)与拓展求和(extended summation,ESUM)算法分析系统作业性能,获取电铲设备的运行状态和班次生产能力。然后,应用蒙特卡洛法建立随机动态规划模型,以产量期望值最大化为目标对露天矿的车铲比进行优化。最后,对生产系统进行仿真建模分析,结果验证了基于DHMM-ESUM对车铲比进行优化的有效性和准确性。

基于HMM-MLP的泵站监测健康诊断系统研究

为实现泵站工程在生产运行过程中有效预测设备潜在故障风险,提升泵站设备运行效率,在数字孪生水利工程数据底板基础上,基于现有硬件设备,以结构故障机理为导向,提出了一种HMM-MLP的泵站设备故障预测方法。先由连续小波包变换处理设备的运行信号,然后通过HMM模型生成设备运行状态序列作为MLP网络的输入预测设备故障,最后通过仿真实验表明,HMM-MLP模型可有效提高泵站设备故障的预测准确率。同时,依托在线监测数据和离线检查与试验数据,建立了设备健康评价指标体系,并开发了泵站监测健康诊断系统,协助运行管理人员充分了解和掌握机组设备的“健康”状态,提升设备管理的信息化水平。结果表明:该研究可为泵站健康系统建设提供实际案例指导与经验启示。

基于GWO-HMM的空中交通网络流系统态势预测研究

针对空中交通流量管理部门如何更高效地实施流量管理的问题,本文将态势感知理论应用于空中交通网络流系统(ATNFS,air traffic network flow system),建立空中交通网络流系统的运行态势预测模型。首先,给出了空中交通网络流系统的态势感知过程,从节点和航线的角度筛选出航线饱和度、不正常航班率、节点饱和度、节点延误架次比、节点航班取消率5个态势要素,使用态势值作为态势理解的指标;其次,分析隐马尔可夫模型(HMM,hidden Markov model)的优势与不足,建立了基于灰狼优化(GWO,grey wolf optimization)算法和改进隐马尔可夫模型的态势预测模型;最后,使用某空中交通网络流系统的实际运行数据进行算例验证。结果表明,改进后的预测模型相较于原本的隐马尔可夫预测模型精度更高,预测结果更准确。

基于HMM的逆雷达辐射源状态识别推理方法

雷达对抗场景中雷达方和干扰方相互感知、识别以及博弈对抗.针对雷达方对干扰方系统内部状态的非合作识别推理的问题,提出了一种对干扰系统中雷达辐射源状态识别模块处理结果进行逆向估计的方法.建立了逆状态识别任务模型,任务中的干扰系统根据雷达工作状态识别结果对其干扰动作进行优化,雷达方则基于对干扰方干扰样式序列的观测,估计干扰方对雷达工作状态的识别结果;设计了基于隐马尔可夫模型(HMM)的逆状态识别任务求解方法,具体包括通过自适应粒子群算法进行模型参数初始化,采取多观测序列的鲍姆-韦尔奇算法进行模型参数估计,采用对数维特比算法估计干扰方的雷达状态识别结果;通过典型雷达对抗场景设定下的数字仿真验证了所给逆状态识别方法的可行性和有效性.

基于简化HMM和时间分段的非侵入式负荷分解算法

针对现有非侵入式负荷分解算法需要以过去时刻的分解结果为依据,从而造成误差累积的问题,提出一种基于简化的隐马尔可夫模型和时间分段的非侵入式负荷分解算法,以实现居民家庭的负荷分解。对负荷的低频功率信号进行分层抽样和聚类分析,构建负荷功率模板并利用独热码对超状态进行编码表示。基于简化的隐马尔可夫模型和普遍生活规律对家庭用电时间段进行划分,在每个时间段内单独训练参数。结合总线数据和各时间段参数实现对各时刻负荷功率的独立求解。基于2种国外公开数据集的测试结果验证了所提算法的准确性和实时性。

On-line Fault Diagnosis in Industrial Processes Using Variable Moving Window and Hidden Markov Model

An integrated framework is presented to represent and classify process data for on-line identifying abnormal operating conditions. It is based on pattern recognition principles and consists of a feature extraction step, by which wavelet transform and principal component analysis are used to capture the inherent characteristics from process measurements, followed by a similarity assessment step using hidden Markov model (HMM) for pattern comparison. In most previous cases, a fixed-length moving window was employed to track dynamic data, and often failed to capture enough information for each fault and sometimes even deteriorated the diagnostic performance. A variable moving window, the length of which is modified with time, is introduced in this paper and case studies on the Tennessee Eastman process illustrate the potential of the proposed method.