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.

Customer Segment Prediction on Retail Transactional Data Using K-Means and Markov Model

Retailing is a dynamic business domain where commodities and goods are sold in small quantities directly to the customers.It deals with the end user customers of a supply-chain network and therefore has to accommodate the needs and desires of a large group of customers over varied utilities.The volume and volatility of the business makes it one of the prospectivefields for analytical study and data modeling.This is also why customer segmentation drives a key role in multiple retail business decisions such as marketing budgeting,customer targeting,customized offers,value proposition etc.The segmentation could be on various aspects such as demographics,historic behavior or preferences based on the use cases.In this paper,historic retail transactional data is used to segment the custo-mers using K-Means clustering and the results are utilized to arrive at a transition matrix which is used to predict the cluster movements over the time period using Markov Model algorithm.This helps in calculating the futuristic value a segment or a customer brings to the business.Strategic marketing designs and budgeting can be implemented using these results.The study is specifically useful for large scale marketing in domains such as e-commerce,insurance or retailers to segment,profile and measure the customer lifecycle value over a short period of time.

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.

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.

Land use change of Kitakyushu based on landscape ecology and Markov model

Based on four phases of TM images acquired in 1990, 1995, 2000 and 2005, this paper took Kitakyushu in Japan as a case study to analyze spatial change of land use landscape and corresponding effects on environmental issues guided by landscape ecology theory in virtue of combining technology of Remote Sensing with GIS. Firstly, land use types were divided into 6 classes （farmland, mountain, forestland, water body, urban land and unused land） according to national classification standard of land use, comprehensible ability of TM image and purpose of this study. Secondly, following the theory of landscape ecology analysis, 11 typical landscape indices were abstracted to evaluate the environmental effects and spatial feature changes of land use. Research results indicated that land use has grown more and more diversified and unbalanced, human activities have disturbed the landscape more seriously. Finally, transfer matrix of Markov was applied to forecast change process of land use in the future different periods, and then potential land use changes were also simulated from 2010 to 2050. Results showed that conversion tendency for all types of land use in Kitakyushu into urban construction land were enhanced. The study was anticipated to help local authorities better understand and address a complex land use system, and develop improved land use management strategies that could better balance urban expansion and ecological conservation.

Frame erasure concealment in wideband speech coding based on large hidden Markov model

Frame erasure concealment is studied to solve the problem of rapid speech quality reduction due to the loss of speech parameters during speech transmission. A large hidden Markov model is applied to model the immittance spectral frequency （ISF） parameters in AMR-WB codec to optimally estimate the lost ISFs based on the minimum mean square error （MMSE） rule. The estimated ISFs are weighted with the ones of their previous neighbors to smooth the speech, resulting in the actual concealed ISF vectors. They are used instead of the lost ISFs in the speech synthesis on the receiver. Comparison is made between the speech concealed by this algorithm and by Annex I of G. 722. 2 specification, and simulation shows that the proposed concealment algorithm can lead to better performance in terms of frequency-weighted spectral distortion and signal-to-noise ratio compared to the baseline method, with an increase of 2.41 dB in signal-to-noise ratio （SNR） and a reduction of 0. 885 dB in frequency-weighted spectral distortion.

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.

A Smart English Text Zero-Watermarking Approach Based on Third-Level Order and Word Mechanism of Markov Model

Text information is principally dependent on the natural languages.Therefore,improving security and reliability of text information exchanged via internet network has become the most difficult challenge that researchers encounter.Content authentication and tampering detection of digital contents have become a major concern in the area of communication and information exchange via the Internet.In this paper,an intelligent text Zero-Watermarking approach SETZWMWMM(Smart English Text Zero-Watermarking Approach Based on Mid-Level Order and Word Mechanism of Markov Model)has been proposed for the content authentication and tampering detection of English text contents.The SETZWMWMM approach embeds and detects the watermark logically without altering the original English text document.Based on Hidden Markov Model(HMM),Third level order of word mechanism is used to analyze the interrelationship between contexts of given English texts.The extracted features are used as a watermark information and integrated with digital zero-watermarking techniques.To detect eventual tampering,SETZWMWMM has been implemented and validated with attacked English text.Experiments were performed on four datasets of varying lengths under multiple random locations of insertion,reorder and deletion attacks.The experimental results show that our method is more sensitive and efficient for all kinds of tampering attacks with high level accuracy of tampering detection than compared methods.

Improved hidden Markov model for speech recognition and POS tagging

In order to overcome defects of the classical hidden Markov model (HMM), Markov family model (MFM), a new statistical model was proposed. Markov family model was applied to speech recognition and natural language processing. The speaker independently continuous speech recognition experiments and the part-of-speech tagging experiments show that Markov family model has higher performance than hidden Markov model. The precision is enhanced from 94.642% to 96.214% in the part-of-speech tagging experiments, and the work rate is reduced by 11.9% in the speech recognition experiments with respect to HMM baseline system.

Digital Twin-based Quality Management Method for the Assembly Process of Aerospace Products with the Grey-Markov Model and Apriori Algorithm

The assembly process of aerospace products such as satellites and rockets has the characteristics of single-or small-batch production,a long development period,high reliability,and frequent disturbances.How to predict and avoid quality abnormalities,quickly locate their causes,and improve product assembly quality and efficiency are urgent engineering issues.As the core technology to realize the integration of virtual and physical space,digital twin(DT)technology can make full use of the low cost,high efficiency,and predictable advantages of digital space to provide a feasible solution to such problems.Hence,a quality management method for the assembly process of aerospace products based on DT is proposed.Given that traditional quality control methods for the assembly process of aerospace products are mostly post-inspection,the Grey-Markov model and T-K control chart are used with a small sample of assembly quality data to predict the value of quality data and the status of an assembly system.The Apriori algorithm is applied to mine the strong association rules related to quality data anomalies and uncontrolled assembly systems so as to solve the issue that the causes of abnormal quality are complicated and difficult to trace.The implementation of the proposed approach is described,taking the collected centroid data of an aerospace product’s cabin,one of the key quality data in the assembly process of aerospace products,as an example.A DT-based quality management system for the assembly process of aerospace products is developed,which can effectively improve the efficiency of quality management for the assembly process of aerospace products and reduce quality abnormalities.

Hidden Markov model based epileptic seizure detection using tunable Q wavelet transform

Epilepsy is one of the most prevalent neurological disorders affecting 70 million people worldwide.The present work is focused on designing an efficient algorithm for automatic seizure detection by using electroencephalogram(EEG) as a noninvasive procedure to record neuronal activities in the brain.EEG signals' underlying dynamics are extracted to differentiate healthy and seizure EEG signals.Shannon entropy,collision entropy,transfer entropy,conditional probability,and Hjorth parameter features are extracted from subbands of tunable Q wavelet transform.Efficient decomposition level for different feature vector is selected using the Kruskal-Wallis test to achieve good classification.Different features are combined using the discriminant correlation analysis fusion technique to form a single fused feature vector.The accuracy of the proposed approach is higher for Q=2 and J=10.Transfer entropy is observed to be significant for different class combinations.Proposed approach achieved 100% accuracy in classifying healthy-seizure EEG signal using simple and robust features and hidden Markov model with less computation time.The proposed approach efficiency is evaluated in classifying seizure and non-seizure surface EEG signals.The system has achieved 96.87% accuracy in classifying surface seizure and nonseizure EEG segments using efficient features extracted from different J level.

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.

The Application of a Grey Markov Model to Forecasting Annual Maximum Water Levels at Hydrological Stations

Compared with traditional real-time forecasting,this paper proposes a Grey Markov Model(GMM) to forecast the maximum water levels at hydrological stations in the estuary area.The GMM combines the Grey System and Markov theory into a higher precision model.The GMM takes advantage of the Grey System to predict the trend values and uses the Markov theory to forecast fluctuation values,and thus gives forecast results involving two aspects of information.The procedure for forecasting annul maximum water levels with the GMM contains five main steps:1) establish the GM(1,1) model based on the data series;2) estimate the trend values;3) establish a Markov Model based on relative error series;4) modify the relative errors caused in step 2,and then obtain the relative errors of the second order estimation;5) compare the results with measured data and estimate the accuracy.The historical water level records(from 1960 to 1992) at Yuqiao Hydrological Station in the estuary area of the Haihe River near Tianjin,China are utilized to calibrate and verify the proposed model according to the above steps.Every 25 years' data are regarded as a hydro-sequence.Eight groups of simulated results show reasonable agreement between the predicted values and the measured data.The GMM is also applied to the 10 other hydrological stations in the same estuary.The forecast results for all of the hydrological stations are good or acceptable.The feasibility and effectiveness of this new forecasting model have been proved in this paper.

The simulation of LUCC based on Logistic-CA-Markov model in Qilian Mountain area, China

The Qilian mountain area was examined for using the Logistic-CA-Markov coupling model combined with GIS spatial analyst technology to research the transformation of LUCC, driving force system and simulate future tendency of variation. Results show that： （1） Woodland area decreased by 12.55%, while grassland, cultivated land, and settlement areas increased by 0.22%, 7.92%, and 0.03%, respectively, from 1986 to 2014. During the period of 1986 to 2000, forest degradation in the middle section of the mountain area decreased by 1,501.69 km2. Vegetation cover area improved, with a net increase of grassland area of 38.12 km2 from 2000 to 2014. （2） For constructing the system driving force, the best simulation scale was 210m×210m. Based on logistic regression analysis, the contribution （weight） of composite driving forces to land use and cover change was obtained, and the weight value was more objectively compared with AHP and MCE method. （3） In the natural scenarios, it is predicted that land use and cover distribution maps of Qilian mountain area in 2028 and 2042, and the Lee-Sallee index test was adopted. Over the next 27 years （2015-2042）, farmland, woodland, grassland, settlement areas show an increasing trend, especially settlements with an obvious change of 0.56%. The area of bare land will decrease by 0.89%. Without environmental degradation, tremendous structural change of LUCC will not occur, and typical characteristic of the vertical zone of the mountain would remain. Farmland and settlement areas will increase, but only in the vicinity of Qilian and Sunan counties.

FAULT DIAGNOSIS APPROACH BASED ON HIDDEN MARKOV MODEL AND SUPPORT VECTOR MACHINE

Aiming at solving the problems of machine-learning in fault diagnosis, a diagnosis approach is proposed based on hidden Markov model （HMM） and support vector machine （SVM）. HMM usually describes intra-class measure well and is good at dealing with continuous dynamic signals. SVM expresses inter-class difference effectively and has perfect classify ability. This approach is built on the merit of HMM and SVM. Then, the experiment is made in the transmission system of a helicopter. With the features extracted from vibration signals in gearbox, this HMM-SVM based diagnostic approach is trained and used to monitor and diagnose the gearbox＇s faults. The result shows that this method is better than HMM-based and SVM-based diagnosing methods in higher diagnostic accuracy with small training samples.

Grey-Markov Model for Road Accidents Forecasting

In order to improve the forecasting precision of road accidents, by introducing Markov chains forecasting method, a grey-Markov model for forecasting road accidents is established based on grey forecasting method. The model combines the advantages of both grey forecasting method and Markov chains forecasting method, overcomes the influence of random fluctuation data on forecasting precision and widens the application scope of the grey forecasting. An application example is conducted to evaluate the grey-Markov model, which shows that the precision of the grey-Markov model is better than that of grey model in forecasting road accidents.

Improving Language Translation Using the Hidden Markov Model

Translation software has become an important tool for communication between different languages.People’s requirements for translation are higher and higher,mainly reflected in people’s desire for barrier free cultural exchange.With a large corpus,the performance of statistical machine translation based on words and phrases is limited due to the small size of modeling units.Previous statistical methods rely primarily on the size of corpus and number of its statistical results to avoid ambiguity in translation,ignoring context.To support the ongoing improvement of translation methods built upon deep learning,we propose a translation algorithm based on the Hidden Markov Model to improve the use of context in the process of translation.During translation,our Hidden Markov Model prediction chain selects a number of phrases with the highest result probability to form a sentence.The collection of all of the generated sentences forms a topic sequence.Using probabilities and article sequences determined from the training set,our method again applies the Hidden Markov Model to form the final translation to improve the context relevance in the process of translation.This algorithm improves the accuracy of translation,avoids the combination of invalid words,and enhances the readability and meaning of the resulting translation.

Partial Improvement of Traditional Grey-Markov Model and Its Application on Fault Prediction

Modeling experiences of traditional grey-Markov show that the prediction results are not accurate when analyzed data are rare and fluctuated.So it is necessary to revise or improve the original modeling procedure of the grey-Markov(GM)model.Therefore,a new idea is brought forward that the Markov theory is used twice,where the first time is to extend the original data and the second to calculate and estimate the residual errors.Then by comparing the original data sequence from a fault prediction case with the simulation sequence produced by the use of GM(1,1) and the new GM method,results are conforming to the original data.Finally,an assumption of GM model is put forward as the future work.

2D-HIDDEN MARKOV MODEL FEATURE EXTRACTION STRATEGY OF ROTATING MACHINERY FAULT DIAGNOSIS

A new feature extraction method based on 2D-hidden Markov model（HMM） is proposed. Meanwhile the time index and frequency index are introduced to represent the new features. The new feature extraction strategy is tested by the experimental data that collected from Bently rotor experiment system. The results show that this methodology is very effective to extract the feature of vibration signals in the rotor speed-up course and can be extended to other non-stationary signal analysis fields in the future.

A Prediction of Future Land Use/Land Cover in Amman Area Using GIS-Based Markov Model and Remote Sensing

The paper aims to analyze land use/land cover (LULC) changes in western part and the populated area of Amman governorate and to identify the process of urbanization and urban expansion within the study area for the period of 1984-2014. It also aims to predict future LULC map for the year 2030 using Markov Model to provide city planners and decision makers with information about the past and current spatial dynamics of LULC change and strictly urban expansion towards successful management and better planning in the future. Images from Landsat 5-TM for the years 1984, 1999 and from Landsat 8-OLI for the year 2014 were used to investigate LULC within the study area during 1984-2014 and the resulted LULC maps in 1999 and 2014 were used to predict future LULC map based on Markov Model. The results indicated that the urban/built up area expanded by 147% during the period from 1984 to 2014 and predicted to expand by 43.9% from 2014 to 2030 based on Markov model predictions. The areas in the western, northwest and southwest parts of Amman as well as the areas of Marka and Uhud, the northeast of the study area, were predicted to witness the major urban expansion in 2030. And these are the areas where city planners and decision makers should take into consideration in future plans of Amman. The urban expansion was mainly attributed to the high population growth rate and large number of immigrants from neighboring countries and other socio-economic changes.