Application Of Hidden Markov Models Research Articles

A sticky Poisson Hidden Markov Model for spike data.

Fitting a hidden Markov Model (HMM) to neural data is a powerful method to segment a spatiotemporal stream of neural activity into sequences of discrete hidden states. Application of HMM has allowed to uncover hidden states and signatures of neural dynamics that seem relevant for sensory and cognitive processes. This has been accomplished especially in datasets comprising ensembles of simultaneously recorded cortical spike trains. However, the HMM analysis of spike data is involved and requires a careful handling of model selection. Two main issues are: (i) the cross-validated likelihood function typically increases with the number of hidden states; (ii) decoding the data with an HMM can lead to very rapid state switching due to fast oscillations in state probabilities. The first problem is related to the phenomenon of over-segmentation and leads to overfitting. The second problem is at odds with the empirical fact that hidden states in cortex tend to last from hundred of milliseconds to seconds. Here, we show that we can alleviate both problems by regularizing a Poisson-HMM during training so as to enforce large self-transition probabilities. We call this algorithm the 'sticky Poisson-HMM' (sPHMM). When used to-gether with the Bayesian Information Criterion for model selection, the sPHMM successfully eliminates rapid state switching, outperforming an alternative strategy based on an HMM with a large prior on the self-transition probabilities. The sPHMM also captures the ground truth in surrogate datasets built to resemble the statistical properties of the experimental data.

Exploring Hidden Markov Models in the Context of Genetic Disorders, and Related Conditions: A Systematic Review

The application of Hidden Markov Models (HMMs) in the study of genetic and neurological disorders has shown significant potential in advancing our understanding and treatment of these conditions. This review assesses 77 papers selected from a pool of 1,105 records to evaluate the use of HMMs in disease research. After the exclusion of duplicate and irrelevant records, the papers were analyzed for their focus on HMM applications and regional representation. A notable deficiency was identified in research across regions such as Africa, South America, and Oceania, emphasizing the need for more diverse and inclusive studies in these areas. Additionally, many studies did not adequately address the role of genetic mutations in the onset and progression of these diseases, revealing a critical research gap that warrants further investigation. Future research efforts should prioritize the examination of mutations to deepen our understanding of how these changes impact the development and progression of genetic and neurological disorders. By addressing these gaps, the scientific community can facilitate the development of more effective and personalized treatments, ultimately enhancing health outcomes on a global scale. Overall, this review highlights the importance of HMMs in this area of research and underscores the necessity of broadening the scope of future studies to include a wider variety of geographical regions and a more comprehensive investigation of genetic mutations.

Genome Mining of Fungal Unique Trichodiene Synthase-like Sesquiterpene Synthases.

Sesquiterpenoids served as an important source for natural product drug discovery. Although genome mining approaches have revealed numerous novel sesquiterpenoids and biosynthetic enzymes, the comprehensive landscape of fungal sesquiterpene synthases (STSs) remains elusive. In this study, 123 previously reported fungal STSs were subjected to phylogenetic analysis, resulting in the identification of a fungi-specific STS family known as trichodiene synthase-like sesquiterpene synthases (TDTSs). Subsequently, the application of hidden Markov models allowed the discovery of 517 TDTSs from our in-house fungi genome library of over 400 sequenced genomes, and these TDTSs were defined into 79 families based on a sequence similarity network. Based on the novelty of protein sequences and the completeness of their biosynthetic gene clusters, 23 TDTS genes were selected for heterologous expression in Aspergillus oryzae. In total, 10 TDTSs were active and collectively produced 12 mono- and sesquiterpenes, resulting in the identification of the first chamipinene synthase, as well as the first fungi-derived cedrene, sabinene, and camphene synthases. Additionally, with the guidance of functionally characterized TDTSs, we found that TDTSs in Family 1 could produce bridged-cyclic sesquiterpenes, while those in Family 2 could synthesize spiro- and bridged-cyclic sesquiterpenes. Our research presents a new avenue for the genome mining of fungal sesquiterpenoids.

Tip of the Spear: Developing Predictive Military Planning Tools Using Hidden Markov Models

The evolution of the modern battlefield is increasingly complex as new technologies emerge. However, the nature of the battlefield still can be explained by Baron De Jomini’s “Grand Tactics.” Military planners’ success is in their ability to develop synergy through layering effects of a complex system at a decisive point on the battlefield. Synergistic effects require subject matter experts (SME) working in planning cells to integrate systems and units in time and space. These planning cells have large footprints and become prioritized in the opposition’s targeting cycle. Without these SMEs, planners are unable to identify the latent variables to achieve the massing of forces at the decisive point. This paper explores the application of Hidden Markov Models (HMMs) to enhance existing Correlation of Forces and Means (COFM) calculators as predictive tools in military planning. Current tools focus on a 3 to 1 force ratio for an offensive operation. They get improved through applying a force equivalent factor. The force equivalent factors are scalars that adjust relative combat power from a single main battle tank to a mechanized infantry vehicle. These tools lack the ability to identify the physics of the battlefield in time and space. The utilization of wargames during planning and training provides a venue for serious games to improve planning tools. It allows a visualization of target pairing and dynamics that a linear equation would miss. This study employs scenarios generated in OneSAF, ranging from simple platoon-level ambushes to combined arms maneuver featuring rotary wing assets requiring a shaping effort to ensure favorable COFMs. The focus of the research lies in leveraging HMMs to establish a time-series indicator of success probability using potentially observable data, with an emphasis on communication dynamics. In this study, we examine observable communication by data generated through both visual and direct contact. Through observation of contact from the friendly and opposition forces will provide a predictor of the hidden state of relative advantage, in time and space. With the data generated by the OneSAF simulation, the HMM determines the states of the operation and probability of success while minimizing required presence inside the units.

Hidden Markov Models for early detection of cardiovascular diseases

Introduction: This article, developed between 2022 and 2023 within the framework of Applied Stochastic Processes by the SciBas group at the Universidad Distrital Francisco José de Caldas, focuses on the role of Hidden Markov Models (HMM) in predicting cardiovascular diseases. Problem: The addressed issue is the need to enhance the early detection of heart diseases, emphasizing how HMM can address uncertainty in clinical data and detect complex patterns. Objective: To evaluate the use of Hidden Markov Models (HMM) in the analysis of electrocardiograms (ECG) for the early detection of cardiovascular diseases. Methodology: The methodology comprises a literature review concerning the relationship between HMM and cardiovascular diseases, followed by the application of HMM to prevent heart attacks and address uncertainty in clinical data. Results: The findings indicate that HMM is effective in preventing heart diseases, yet its effectiveness is contingent upon data quality. These results are promising but not universally applicable. Conclusions: In summary, this study underscores the utility of HMM in early infarction detection and its statistical approach in medicine. It is emphasized that HMM is not infallible and should be complemented with other clinical options and assessment methods in real-world situations. Originality: This work stands out for its statistical and probabilistic approach in the application of Hidden Markov Models (HMM) in medical analysis, offering an innovative perspective and enhancing the understanding of their utility in the field of medicine. Limitations: It is recognized that there are limitations, such as dependence on data quality and variable applicability in clinical cases. These limitations should be considered in the context of their implementation in medical practice.

Bayesian change point prediction for downhole drilling pressures with hidden Markov models

AbstractIn the drilling of oil wells, the need to accurately detect downhole formation pressure transitions has long been established as critical for safety and economics. In this article, we examine the application of Hidden Markov Models (HMMs) to oilwell drilling processes with a focus on the real time evolution of downhole formation pressures in its partially observed state. The downhole drilling pressure system can be viewed as a nonlinear, non‐degrading stochastic process whose optimum performance is in a region in its warning state prior to random failure in time. The differential pressure system is modeled as a hidden 3 state continuous time Markov process. States 0 and 1 are not observable and represent the normally pressured (initiating ) and abnormally pressured or warning (reducing ) states respectively. State 2 is the observable failure state (from negative and loss of well control). The signal process of the evolution of differential pressure is identified in the changes in the observable rate of penetration (ROP) encoded in drilling performance data. The state and observation parameters of the HMM are estimated using the Expectation Maximization (EM) algorithm and we show, for a univariate system with a depth dependent time relationship, that the model parameter updates of the EM algorithm equation have explicit solutions. A Bayesian inference model, to determine the safety threshold of the system and early failure prediction at each sampling epoch, is thereafter proposed. The application of our stochastic model of the dynamic evolution of downhole pressures in operational time is illustrated with a hindcast case example. The analysis showed strong early indication of probable failure in real time and was validated in the field post drilling system failure that resulted in significant recovery costs. The potential to predict the differential pressure state transitions ahead of the bit represents a capability not currently available in the industry. This opens up significant opportunity for value creation from optimizing drilling operations to deliver substantial savings in well construction costs.

Estimating Hidden Markov Models (HMMs) of the cognitive process in strategic thinking using eye-tracking

Hidden Markov Models (HMMs) are used to study language, sleep, macroeconomic states, and other processes that reflect probabilistic transitions between states that can't be observed directly. This paper applies HMMs to data from location-based game theory experiments. In these location games, players choose a pixel location from an image. These players either have a common goal (choose a matching location), or competing goals, to mismatch (hide) or match (seek) in hider-seeker games. We use eye-tracking to record where players look throughout the experimental decision. Each location's numerical salience is predicted using an accurate, specialized vision science-based neural network [the Saliency Attentive Model (SAM)]. The HMM shows the pattern of transitioning from hidden states corresponding to either high or low-salience locations, combining the eye-tracking and salience data. The transitions vary based on the player's strategic goal. For example, hiders transition more often to low-salience states than seekers do. The estimated HMM is then used to do two useful things. First, a continuous-time HMM (cHMM) predicts the salience level of each player's looking over several seconds. The cHMM can then be used to predict what would happen if the same process was truncated by time pressure: This calculation makes a specific numerical prediction about how often seekers will win, and it predicts an increase in win rate but underestimates the size of the change. Second, a discrete-time HMM (dHMM) can be used to infer levels of strategic thinking from high-to-low salience eye-tracking transitions. The resulting estimates are more plausible than some maximum-likelihood models, which underestimate strategic sophistication in these games. Other applications of HMM in experimental economics are suggested.

Retracted: Application of Hidden Markov Model in Financial Time Series Data

Retracted: Application of Hidden Markov Model in Financial Time Series Data

Nanocatalysts as fast and powerful medical intervention: Bridging cochlear implant therapies and advanced modelling using Hidden Markov Models (HMMs) for effective treatment of inner ear infections

As human population growth and waste from technologically advanced industries threaten to destabilise our delicate ecological equilibrium, the global spotlight intensifies on environmental contamination and climate-related changes. These challenges extend beyond our external environment and have significant effects on our internal ecosystems. The inner ear, which is responsible for balance and auditory perception, is a prime example. When these sensory mechanisms are impaired, disorders such as deafness can develop. Traditional treatment methods, including systemic antibiotics, are frequently ineffective due to inadequate inner ear penetration. Conventional techniques for administering substances to the inner ear fail to obtain adequate concentrations as well. In this context, cochlear implants laden with nanocatalysts emerge as a promising strategy for the targeted treatment of inner ear infections. Coated with biocompatible nanoparticles containing specific nanocatalysts, these implants can degrade or neutralise contaminants linked to inner ear infections. This method enables the controlled release of nanocatalysts directly at the infection site, thereby maximising therapeutic efficacy and minimising adverse effects. In vivo and in vitro studies have demonstrated that these implants are effective at eliminating infections, reducing inflammation, and fostering tissue regeneration in the ear. This study investigates the application of hidden Markov models (HMMs) to nanocatalyst-loaded cochlear implants. The HMM is trained on surgical phases in order to accurately identify the various phases associated with implant utilisation. This facilitates the precision placement of surgical instruments within the ear, with a location accuracy between 91% and 95% and a standard deviation between 1% and 5% for both sites. In conclusion, nanocatalysts serve as potent medicinal instruments, bridging cochlear implant therapies and advanced modelling utilising hidden Markov models for the effective treatment of inner ear infections. Cochlear implants loaded with nanocatalysts offer a promising method to combat inner ear infections and enhance patient outcomes by addressing the limitations of conventional treatments.

Prediction of stock price by Hidden Markov model

Hidden Markov model is widely used in different fields, such as speech decoding, weather prediction, biometric, medical diagnosis and prediction. This paper explores the application of hidden Markov model in quantitative investment in the financial field, mainly in the field of securities trading. The main idea of this paper is to crack the different market states reflected in the price changes of securities. Price changes are affected by different market states at the same time, but this state is hidden and needs to be inferred. If people can calculate the relevant parameters of the state and its transition matrix, people can predict the stock price trend and establish the own trading strategy at the same time. The hidden Markov model is used as a theoretical basis in this paper, through the analysis of the fit between the stock market and the model, as well as various statistical knowledge as a tool. Through EM algorithm and machine learning as the programming basis, people finally selected Pingan and Moutai as the research objects, predicted the stock price changes through training. Therefore, when the training set is sufficiently large, hidden Markov model will have a bright performance in stock price prediction.

Likelihood Ratio Search Procedure for Optimum Number of States in a Hidden Markov Manpower Model

This paper aims at obtaining the optimum number of states for a hidden Markov manpower model, which, hitherto, has been chosen arbitrarily. A search procedure that attains this optimum number after a few steps across a series of N hidden Markov manpower models is proposed. The likelihood ratio statistic is employed to conduct pairwise model comparison tests on the N hidden Markov manpower models ordered according to their level of parsimony. The illustration shows the usefulness of the procedure in choosing the right number of states for a hidden Markov manpower model to avoid wrong specification of such models. The proposed procedure can be useful in other areas of research, such as in biological, medical and social sciences, where application of hidden Markov model may require the determination of number of hidden states based on unobserved data with latent heterogeneity. The procedure has a straightforward formulation and its application in other areas requires mainly the adaptation of the model specifications for the new area’s system dynamics.

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.

Research on the effect of generative adversarial network based on wavelet transform hidden Markov model on face creation and classification

Abstract Identity authentication and identification as the main activities in the modern human daily life, in the development of social economy and technological innovation of science and technology, information security and public security and other fields of identity recognition technology put forward higher request, not only to accurately record the relevant data information, and use of advanced technology concept advanced authentication technology is put forward. Knowing the living habits of current social residents, they need to choose appropriate ways to prove their identity, whether they enter the computer system or participate in financial services. For example, passwords, keys, ID cards and other contents are traditional means of identity authentication. As the reliability of traditional identity authentication means is getting lower and lower in today’s social network environment, in order to meet the needs of increasingly innovative network platforms and enhance the security and reliability of identity authentication technology, researchers propose to use biometric identification, among which face recognition is the most common. This recognition method is more direct and effective than palmprint, retina, fingerprint and so on, and occupies an important position in the current biometric research topic. After understanding the application of hidden Markov model (HMM) based on wavelet transform and its improved model in the field of face authentication, this paper analyzes the influence of various observation vector optimization methods on the authentication probability, and proposes a dynamic threshold method combined with the authentication model, in order to obtain a better authentication probability. The final design and implementation of the face information as the core of the identity authentication system. The improved hidden Markov model can effectively represent the correlation between face organs, so it has a positive role in the application of face modeling and authentication technology.

Application of Hidden Markov Model in Financial Time Series Data

Financial time series have typical characteristics such as outliers, trends, and mean reversion. The existence of outliers will affect the effectiveness of the unknown parameter estimation in the financial time series forecasting model, so that the forecasting error of the model will be larger. Quantitative forecasting methods are divided into causal forecasting method and time series forecasting method. The causal forecasting method uses the causal relationship between the predictor variable and other variables to predict, and the time series forecasting method infers the future value of the predictor variable based on the structure of the historical data of the predictor. Therefore, this paper proposes a hidden Markov model prediction method based on the observation vector sequence, which can simultaneously consider the influence of the variable sequence structure and related factors.

Application of Hidden Markov Models for Fault Detection in Automotive Engines

One of widely the used applications of engine test-bed systems is the estimation of engine parameters and assessment of controllers in early design phase. Optimized efficiently, controller parameters can assist in fulfillment of fuel consumption and driving comfort requirements. Usually the results of coordinating experiments differ with major cause of that being due to some external disturbances or faults. The primary purpose of this paper is to use hidden Markov models for fault detection in test beds in order to enhance the engine design process by performing a reduction in costs and time of the design process and its control concept. The results have been demonstrated on an industrial engine test bed and the performance of these methods is discussed

Modeling operator self-assessment in human-autonomy teaming settings

The need to design for appropriate human-autonomy teaming has become increasingly important as systems grow in complexity, especially those that require time-pressured interactions like in unmanned aerial vehicle (UAV) operations. However, it is not always clear whether operators develop effective strategies for computer-based technologies. When operators are given such tools, their performances can be statistically compared but often such assessments only provide summative information. These comparisons do not indicate how and why technology influenced people's strategies and actions. To fill this gap, we utilized Hidden Markov Models (HMMs) to represent strategies employed by operators in first-person control of UAVs for inspection tasks. The resulting models captured differences in strategies for people who both succeeded and crashed, as well as those who were overconfident in their self-assessments, and those who were not. People who were not overconfident exhibited less risky strategies and were more successful. These findings were further strengthened by a quantitative state similarity metric, which indicated where and for who possible interventions could improve outcomes. This application of HMMs to operator strategy representation could help to identify effective operator strategy development in the use of computer-based technologies, and what kind of interventions could be the most effective in improving outcomes.

Can customer perceived service quality fluctuations predict the performance of retail service supply chain?

PurposeBased on the analysis of the dissipative structure of the retail service supply chain (RSSC), this paper divides the system into two internal and external dissipative mechanisms, including the internal performance dissipation mechanism and the perceived quality dissipation mechanism outside the system. Based on the prediction of RSSC performance, this paper aims to discuss the application of Hidden Markov Model (HMM) in this field and puts forward a set of complete process of forecasting the service supply chain (SSC) performance based on HMM model.Design/methodology/approachBased on the theory of dissipative structure, this paper selects the RSSC as the research object, analyzes the system characteristics of the dissipation structure of RSSC from three aspects, such as system opening type, distance from equilibrium state and nonlinear order and describes the quality fluctuation process of RSSC as a Hidden Markov process. Taking the RSSC of J Company as an example, this paper makes use of the observed state value of customer perceived service quality from 1997 to 2016, predicts the performance status of the enterprise's RSSC.FindingsThe research results show that: RSSC is a dissipative structure system, and its performance is the internal entropy flow of the system, and the customer perceived service quality is external, their interaction determines the dynamic evolution of the system dissipation structure, and the Markov property between supply chain performance and perceived service quality. There is a Markov property between supply chain performance and perceived service quality. Using the perceived service quality observation state data of the external consumers of the system can effectively predict the implicit state of RSSC performance. Based on this prediction result, the strategy adjustment and optimization of the action mechanism of internal and external entropy flow in the dissipative structure system can be carried out to promote the sustainable development of the RSSC.Originality/valueThis paper thinks that RSSC is a dissipative structure system and the SSC performance and customer perceived service quality are the internal and external entropy flow of the system, which determines the dynamic evolution of the system dissipation structure. There is a Markov property between supply chain performance and perceived service quality. The hidden state of SSC performance can be predicted effectively by using a hidden Markov model and observing state data of perceived service quality from consumers outside the system.

An Intra-Subject Approach Based on the Application of HMM to Predict Concentration in Educational Contexts from Nonintrusive Physiological Signals in Real-World Situations.

Previous research has proven the strong influence of emotions on student engagement and motivation. Therefore, emotion recognition is becoming very relevant in educational scenarios, but there is no standard method for predicting students’ affects. However, physiological signals have been widely used in educational contexts. Some physiological signals have shown a high accuracy in detecting emotions because they reflect spontaneous affect-related information, which is fresh and does not require additional control or interpretation. Most proposed works use measuring equipment for which applicability in real-world scenarios is limited because of its high cost and intrusiveness. To tackle this problem, in this work, we analyse the feasibility of developing low-cost and nonintrusive devices to obtain a high detection accuracy from easy-to-capture signals. By using both inter-subject and intra-subject models, we present an experimental study that aims to explore the potential application of Hidden Markov Models (HMM) to predict the concentration state from 4 commonly used physiological signals, namely heart rate, breath rate, skin conductance and skin temperature. We also study the effect of combining these four signals and analyse their potential use in an educational context in terms of intrusiveness, cost and accuracy. The results show that a high accuracy can be achieved with three of the signals when using HMM-based intra-subject models. However, inter-subject models, which are meant to obtain subject-independent approaches for affect detection, fail at the same task.

Trend shifts in road traffic collisions: An application of Hidden Markov Models and Generalised Additive Models to assess the impact of the 20 mph speed limit policy in Edinburgh

Empirical study of road traffic collision (RTCs) rates is challenging at small geographies due to the relative rarity of collisions and the need to account for secular and seasonal trends. In this paper, we demonstrate the successful application of Hidden Markov Models (HMMs) and Generalised Additive Models (GAMs) to describe RTCs time series using monthly data from the city of Edinburgh (STATS19) as a case study. While both models have comparable level of complexity, they bring different advantages. HMMs provide a better interpretation of the data-generating process, whereas GAMs can be superior in terms of forecasting. In our study, both models successfully capture the declining trend and the seasonal pattern with a peak in the autumn and a dip in the spring months. Our best fitting HMM indicates a change in a fast-declining-trend state after the introduction of the 20 mph speed limit in July 2016. Our preferred GAM explicitly models this intervention and provides evidence for a significant further decline in the RTCs. In a comparison between the two modelling approaches, the GAM outperforms the HMM in out-of-sample forecasting of the RTCs for 2018. The application of HMMs and GAMs to routinely collected data such as the road traffic data may be beneficial to evaluations of interventions and policies, especially natural experiments, that seek to impact traffic collision rates.

Hidden Markov Modeling of the Cognitive Process in Strategic Thinking

Hidden Markov models (HMMs) are used to study language, sleep, and other processes that reflect probabilistic transitions between states that cannot be observed directly. We apply HMMs to data from experiments on visual location games. In these games, people choose a pixel from an image. They either have a common goal to match locations or have different goals in hider-seeker games. Eyetracking records where they look at a fine-grained time scale. Numerical salience of different locations is predicted, a priori, from a specialized vision science based neural network. The HMM shows the pattern of transitioning from hidden states corresponding to either high or low salience locations, using the eye-tracking and salience data. The transitions vary based on the player’s strategic goal; for example, hiders transition more often to low-salience states than seekers do. The estimated HMM is then used to do two useful things. First, a continuous-time HMM (cHMM) predicts the salience level of each player’s looking over the course of several seconds. The cHMM is then used to predict what would happen if the same process was truncated by time pressure choosing in two seconds instead of six, cHMM predicts seekers will match hiders 12% of the time; they actually match 15%. Second, dHMM is used to infer levels of strategic thinking from high-to-low transitions (a la Costa-Gomes et al. 2001 and others). The resulting estimates are more plausible than some maximum-likelihood procedures and models which appear to grossly underestimate strategic sophistication. Other applications of HMM in experimental economics are suggested.