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Model</title> <meta name="description" content="Search results for: Markov Model"> <meta name="keywords" content="Markov Model"> <meta name="viewport" content="width=device-width, initial-scale=1, minimum-scale=1, maximum-scale=1, user-scalable=no"> <meta charset="utf-8"> <link href="https://cdn.waset.org/favicon.ico" type="image/x-icon" rel="shortcut icon"> <link href="https://cdn.waset.org/static/plugins/bootstrap-4.2.1/css/bootstrap.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/plugins/fontawesome/css/all.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/css/site.css?v=150220211555" rel="stylesheet"> </head> <body> <header> <div class="container"> <nav class="navbar navbar-expand-lg navbar-light"> <a class="navbar-brand" href="https://waset.org"> <img src="https://cdn.waset.org/static/images/wasetc.png" alt="Open Science Research Excellence" title="Open Science Research Excellence" /> </a> <button class="d-block d-lg-none navbar-toggler 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id="custom-search-input"> <div class="input-group"> <i class="fas fa-search"></i> <input type="text" class="search-query" name="q" placeholder="Author, Title, Abstract, Keywords" value="Markov Model"> <input type="submit" class="btn_search" value="Search"> </div> </div> </form> </div> </div> <div class="row mt-3"> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Commenced</strong> in January 2007</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Frequency:</strong> Monthly</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Edition:</strong> International</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Paper Count:</strong> 16844</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: Markov Model</h1> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">16844</span> Valuation of Caps and Floors in a LIBOR Market Model with Markov Jump Risks</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Shih-Kuei%20Lin">Shih-Kuei Lin</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The characterization of the arbitrage-free dynamics of interest rates is developed in this study under the presence of Markov jump risks, when the term structure of the interest rates is modeled through simple forward rates. We consider Markov jump risks by allowing randomness in jump sizes, independence between jump sizes and jump times. The Markov jump diffusion model is used to capture empirical phenomena and to accurately describe interest jump risks in a financial market. We derive the arbitrage-free model of simple forward rates under the spot measure. Moreover, the analytical pricing formulas for a cap and a floor are derived under the forward measure when the jump size follows a lognormal distribution. In our empirical analysis, we find that the LIBOR market model with Markov jump risk better accounts for changes from/to different states and different rates. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=arbitrage-free" title="arbitrage-free">arbitrage-free</a>, <a href="https://publications.waset.org/abstracts/search?q=cap%20and%20floor" title=" cap and floor"> cap and floor</a>, <a href="https://publications.waset.org/abstracts/search?q=Markov%20jump%20diffusion%20model" title=" Markov jump diffusion model"> Markov jump diffusion model</a>, <a href="https://publications.waset.org/abstracts/search?q=simple%20forward%20rate%20model" title=" simple forward rate model"> simple forward rate model</a>, <a href="https://publications.waset.org/abstracts/search?q=volatility%20smile" title=" volatility smile"> volatility smile</a>, <a href="https://publications.waset.org/abstracts/search?q=EM%20algorithm" title=" EM algorithm"> EM algorithm</a> </p> <a href="https://publications.waset.org/abstracts/11690/valuation-of-caps-and-floors-in-a-libor-market-model-with-markov-jump-risks" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/11690.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">421</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">16843</span> Maintenance Alternatives Related to Costs of Wind Turbines Using Finite State Markov Model</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Boukelkoul%20Lahcen">Boukelkoul Lahcen</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The cumulative costs for O&M may represent as much as 65%-90% of the turbine's investment cost. Nowadays the cost effectiveness concept becomes a decision-making and technology evaluation metric. The cost of energy metric accounts for the effect replacement cost and unscheduled maintenance cost parameters. One key of the proposed approach is the idea of maintaining the WTs which can be captured via use of a finite state Markov chain. Such a model can be embedded within a probabilistic operation and maintenance simulation reflecting the action to be done. In this paper, an approach of estimating the cost of O&M is presented. The finite state Markov model is used for decision problems with number of determined periods (life cycle) to predict the cost according to various options of maintenance. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=cost" title="cost">cost</a>, <a href="https://publications.waset.org/abstracts/search?q=finite%20state" title=" finite state"> finite state</a>, <a href="https://publications.waset.org/abstracts/search?q=Markov%20model" title=" Markov model"> Markov model</a>, <a href="https://publications.waset.org/abstracts/search?q=operation%20and%20maintenance" title=" operation and maintenance"> operation and maintenance</a> </p> <a href="https://publications.waset.org/abstracts/35860/maintenance-alternatives-related-to-costs-of-wind-turbines-using-finite-state-markov-model" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/35860.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">533</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">16842</span> The Realization of a System’s State Space Based on Markov Parameters by Using Flexible Neural Networks</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ali%20Isapour">Ali Isapour</a>, <a href="https://publications.waset.org/abstracts/search?q=Ramin%20Nateghi"> Ramin Nateghi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> — Markov parameters are unique parameters of the system and remain unchanged under similarity transformations. Markov parameters from a power series that is convergent only if the system matrix’s eigenvalues are inside the unity circle. Therefore, Markov parameters of a stable discrete-time system are convergent. In this study, we aim to realize the system based on Markov parameters by using Artificial Neural Networks (ANN), and this end, we use Flexible Neural Networks. Realization means determining the elements of matrices A, B, C, and D. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Markov%20parameters" title="Markov parameters">Markov parameters</a>, <a href="https://publications.waset.org/abstracts/search?q=realization" title=" realization"> realization</a>, <a href="https://publications.waset.org/abstracts/search?q=activation%20function" title=" activation function"> activation function</a>, <a href="https://publications.waset.org/abstracts/search?q=flexible%20neural%20network" title=" flexible neural network"> flexible neural network</a> </p> <a href="https://publications.waset.org/abstracts/119535/the-realization-of-a-systems-state-space-based-on-markov-parameters-by-using-flexible-neural-networks" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/119535.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">194</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">16841</span> Hidden Markov Model for the Simulation Study of Neural States and Intentionality</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=R.%20B.%20Mishra">R. B. Mishra</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Hidden Markov Model (HMM) has been used in prediction and determination of states that generate different neural activations as well as mental working conditions. This paper addresses two applications of HMM; one to determine the optimal sequence of states for two neural states: Active (AC) and Inactive (IA) for the three emission (observations) which are for No Working (NW), Waiting (WT) and Working (W) conditions of human beings. Another is for the determination of optimal sequence of intentionality i.e. Believe (B), Desire (D), and Intention (I) as the states and three observational sequences: NW, WT and W. The computational results are encouraging and useful. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=hiden%20markov%20model" title="hiden markov model">hiden markov model</a>, <a href="https://publications.waset.org/abstracts/search?q=believe%20desire%20intention" title=" believe desire intention"> believe desire intention</a>, <a href="https://publications.waset.org/abstracts/search?q=neural%20activation" title=" neural activation"> neural activation</a>, <a href="https://publications.waset.org/abstracts/search?q=simulation" title=" simulation"> simulation</a> </p> <a href="https://publications.waset.org/abstracts/31030/hidden-markov-model-for-the-simulation-study-of-neural-states-and-intentionality" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/31030.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">376</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">16840</span> Markov Characteristics of the Power Line Communication Channels in China</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ming-Yue%20Zhai">Ming-Yue Zhai</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Due to the multipath and pulse noise nature, power line communications(PLC) channel can be modelled as a memory one with the finite states Markov model(FSMC). As the most important parameter modelling a Markov channel,the memory order in an FSMC is not solved in PLC systems yet. In the paper, the mutual information is used as a measure of the dependence between the different symbols, treated as the received SNA or amplitude of the current channel symbol or that of previous symbols. The joint distribution probabilities of the envelopes in PLC systems are computed based on the multi-path channel model, which is commonly used in PLC. we confirm that given the information of the symbol immediately preceding the current one, any other previous symbol is independent of the current one in PLC systems, which means the PLC channels is a Markov chain with the first-order. The field test is also performed to model the received OFDM signals with the help of AR model. The results show that the first-order AR model is enough to model the fading channel in PLC systems, which means the amount of uncertainty remaining in the current symbol should be negligible, given the information corresponding to the immediately preceding one. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=power%20line%20communication" title="power line communication">power line communication</a>, <a href="https://publications.waset.org/abstracts/search?q=channel%20model" title=" channel model"> channel model</a>, <a href="https://publications.waset.org/abstracts/search?q=markovian" title=" markovian"> markovian</a>, <a href="https://publications.waset.org/abstracts/search?q=information%20theory" title=" information theory"> information theory</a>, <a href="https://publications.waset.org/abstracts/search?q=first-order" title=" first-order"> first-order</a> </p> <a href="https://publications.waset.org/abstracts/10405/markov-characteristics-of-the-power-line-communication-channels-in-china" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/10405.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">412</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">16839</span> Metamorphic Computer Virus Classification Using Hidden Markov Model</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Babak%20Bashari%20Rad">Babak Bashari Rad</a> </p> <p class="card-text"><strong>Abstract:</strong></p> A metamorphic computer virus uses different code transformation techniques to mutate its body in duplicated instances. Characteristics and function of new instances are mostly similar to their parents, but they cannot be easily detected by the majority of antivirus in market, as they depend on string signature-based detection techniques. The purpose of this research is to propose a Hidden Markov Model for classification of metamorphic viruses in executable files. In the proposed solution, portable executable files are inspected to extract the instructions opcodes needed for the examination of code. A Hidden Markov Model trained on portable executable files is employed to classify the metamorphic viruses of the same family. The proposed model is able to generate and recognize common statistical features of mutated code. The model has been evaluated by examining the model on a test data set. The performance of the model has been practically tested and evaluated based on False Positive Rate, Detection Rate and Overall Accuracy. The result showed an acceptable performance with high average of 99.7% Detection Rate. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=malware%20classification" title="malware classification">malware classification</a>, <a href="https://publications.waset.org/abstracts/search?q=computer%20virus%20classification" title=" computer virus classification"> computer virus classification</a>, <a href="https://publications.waset.org/abstracts/search?q=metamorphic%20virus" title=" metamorphic virus"> metamorphic virus</a>, <a href="https://publications.waset.org/abstracts/search?q=metamorphic%20malware" title=" metamorphic malware"> metamorphic malware</a>, <a href="https://publications.waset.org/abstracts/search?q=Hidden%20Markov%20Model" title=" Hidden Markov Model"> Hidden Markov Model</a> </p> <a href="https://publications.waset.org/abstracts/62795/metamorphic-computer-virus-classification-using-hidden-markov-model" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/62795.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">315</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">16838</span> Volatility Model with Markov Regime Switching to Forecast Baht/USD</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Nop%20Sopipan">Nop Sopipan</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, we forecast the volatility of Baht/USDs using Markov Regime Switching GARCH (MRS-GARCH) models. These models allow volatility to have different dynamics according to unobserved regime variables. The main purpose of this paper is to find out whether MRS-GARCH models are an improvement on the GARCH type models in terms of modeling and forecasting Baht/USD volatility. The MRS-GARCH is the best performance model for Baht/USD volatility in short term but the GARCH model is best perform for long term. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=volatility" title="volatility">volatility</a>, <a href="https://publications.waset.org/abstracts/search?q=Markov%20Regime%20Switching" title=" Markov Regime Switching"> Markov Regime Switching</a>, <a href="https://publications.waset.org/abstracts/search?q=forecasting" title=" forecasting"> forecasting</a>, <a href="https://publications.waset.org/abstracts/search?q=Baht%2FUSD" title=" Baht/USD"> Baht/USD</a> </p> <a href="https://publications.waset.org/abstracts/3942/volatility-model-with-markov-regime-switching-to-forecast-bahtusd" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/3942.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">302</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">16837</span> Markov-Chain-Based Optimal Filtering and Smoothing</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Garry%20A.%20Einicke">Garry A. Einicke</a>, <a href="https://publications.waset.org/abstracts/search?q=Langford%20B.%20White"> Langford B. White</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper describes an optimum filter and smoother for recovering a Markov process message from noisy measurements. The developments follow from an equivalence between a state space model and a hidden Markov chain. The ensuing filter and smoother employ transition probability matrices and approximate probability distribution vectors. The properties of the optimum solutions are retained, namely, the estimates are unbiased and minimize the variance of the output estimation error, provided that the assumed parameter set are correct. Methods for estimating unknown parameters from noisy measurements are discussed. Signal recovery examples are described in which performance benefits are demonstrated at an increased calculation cost. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=optimal%20filtering" title="optimal filtering">optimal filtering</a>, <a href="https://publications.waset.org/abstracts/search?q=smoothing" title=" smoothing"> smoothing</a>, <a href="https://publications.waset.org/abstracts/search?q=Markov%20chains" title=" Markov chains"> Markov chains</a> </p> <a href="https://publications.waset.org/abstracts/20256/markov-chain-based-optimal-filtering-and-smoothing" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/20256.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">317</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">16836</span> On the Use of Analytical Performance Models to Design a High-Performance Active Queue Management Scheme</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Shahram%20Jamali">Shahram Jamali</a>, <a href="https://publications.waset.org/abstracts/search?q=Samira%20Hamed"> Samira Hamed </a> </p> <p class="card-text"><strong>Abstract:</strong></p> One of the open issues in Random Early Detection (RED) algorithm is how to set its parameters to reach high performance for the dynamic conditions of the network. Although original RED uses fixed values for its parameters, this paper follows a model-based approach to upgrade performance of the RED algorithm. It models the routers queue behavior by using the Markov model and uses this model to predict future conditions of the queue. This prediction helps the proposed algorithm to make some tunings over RED's parameters and provide efficiency and better performance. Widespread packet level simulations confirm that the proposed algorithm, called Markov-RED, outperforms RED and FARED in terms of queue stability, bottleneck utilization and dropped packets count. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=active%20queue%20management" title="active queue management">active queue management</a>, <a href="https://publications.waset.org/abstracts/search?q=RED" title=" RED"> RED</a>, <a href="https://publications.waset.org/abstracts/search?q=Markov%20model" title=" Markov model"> Markov model</a>, <a href="https://publications.waset.org/abstracts/search?q=random%20early%20detection%20algorithm" title=" random early detection algorithm "> random early detection algorithm </a> </p> <a href="https://publications.waset.org/abstracts/33934/on-the-use-of-analytical-performance-models-to-design-a-high-performance-active-queue-management-scheme" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/33934.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">539</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">16835</span> Finite State Markov Chain Model of Pollutants from Service Stations</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Amina%20Boukelkoul">Amina Boukelkoul</a>, <a href="https://publications.waset.org/abstracts/search?q=Rahil%20Boukelkoul"> Rahil Boukelkoul</a>, <a href="https://publications.waset.org/abstracts/search?q=Leila%20Maachia"> Leila Maachia</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The cumulative vapors emitted from the service stations may represent a hazard to the environment and the population. Besides fuel spill and their penetration into deep soil layers are the main contributors to soil and ground-water contamination in the vicinity of the petrol stations. The amount of the effluents from the service stations depends on strategy of maintenance and the policy adopted by the management to reduce the pollution. One key of the proposed approach is the idea of managing the effluents from the service stations which can be captured via use of a finite state Markov chain. Such a model can be embedded within a probabilistic operation and maintenance simulation reflecting the action to be done. In this paper, an approach of estimating a probabilistic percentage of the amount of emitted pollutants is presented. The finite state Markov model is used for decision problems with number of determined periods (life cycle) to predict the amount according to various options of operation. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=environment" title="environment">environment</a>, <a href="https://publications.waset.org/abstracts/search?q=markov%20modeling" title=" markov modeling"> markov modeling</a>, <a href="https://publications.waset.org/abstracts/search?q=pollution" title=" pollution"> pollution</a>, <a href="https://publications.waset.org/abstracts/search?q=service%20station" title=" service station"> service station</a> </p> <a href="https://publications.waset.org/abstracts/35961/finite-state-markov-chain-model-of-pollutants-from-service-stations" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/35961.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">472</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">16834</span> Markov Switching of Conditional Variance</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Josip%20Arneric">Josip Arneric</a>, <a href="https://publications.waset.org/abstracts/search?q=Blanka%20Skrabic%20Peric"> Blanka Skrabic Peric</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Forecasting of volatility, i.e. returns fluctuations, has been a topic of interest to portfolio managers, option traders and market makers in order to get higher profits or less risky positions. Based on the fact that volatility is time varying in high frequency data and that periods of high volatility tend to cluster, the most common used models are GARCH type models. As standard GARCH models show high volatility persistence, i.e. integrated behaviour of the conditional variance, it is difficult the predict volatility using standard GARCH models. Due to practical limitations of these models different approaches have been proposed in the literature, based on Markov switching models. In such situations models in which the parameters are allowed to change over time are more appropriate because they allow some part of the model to depend on the state of the economy. The empirical analysis demonstrates that Markov switching GARCH model resolves the problem of excessive persistence and outperforms uni-regime GARCH models in forecasting volatility for selected emerging markets. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=emerging%20markets" title="emerging markets">emerging markets</a>, <a href="https://publications.waset.org/abstracts/search?q=Markov%20switching" title=" Markov switching"> Markov switching</a>, <a href="https://publications.waset.org/abstracts/search?q=GARCH%20model" title=" GARCH model"> GARCH model</a>, <a href="https://publications.waset.org/abstracts/search?q=transition%20probabilities" title=" transition probabilities"> transition probabilities</a> </p> <a href="https://publications.waset.org/abstracts/23987/markov-switching-of-conditional-variance" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/23987.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">455</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">16833</span> An Optimal Bayesian Maintenance Policy for a Partially Observable System Subject to Two Failure Modes</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Akram%20Khaleghei%20Ghosheh%20Balagh">Akram Khaleghei Ghosheh Balagh</a>, <a href="https://publications.waset.org/abstracts/search?q=Viliam%20Makis"> Viliam Makis</a>, <a href="https://publications.waset.org/abstracts/search?q=Leila%20Jafari"> Leila Jafari</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, we present a new maintenance model for a partially observable system subject to two failure modes, namely a catastrophic failure and a failure due to the system degradation. The system is subject to condition monitoring and the degradation process is described by a hidden Markov model. A cost-optimal Bayesian control policy is developed for maintaining the system. The control problem is formulated in the semi-Markov decision process framework. An effective computational algorithm is developed and illustrated by a numerical example. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=partially%20observable%20system" title="partially observable system">partially observable system</a>, <a href="https://publications.waset.org/abstracts/search?q=hidden%20Markov%20model" title=" hidden Markov model"> hidden Markov model</a>, <a href="https://publications.waset.org/abstracts/search?q=competing%20risks" title=" competing risks"> competing risks</a>, <a href="https://publications.waset.org/abstracts/search?q=multivariate%20Bayesian%20control" title=" multivariate Bayesian control"> multivariate Bayesian control</a> </p> <a href="https://publications.waset.org/abstracts/12740/an-optimal-bayesian-maintenance-policy-for-a-partially-observable-system-subject-to-two-failure-modes" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/12740.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">457</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">16832</span> Moral Hazard under the Effect of Bailout and Bailin Events: A Markov Switching Model</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Amira%20Kaddour">Amira Kaddour</a> </p> <p class="card-text"><strong>Abstract:</strong></p> To curb the problem of liquidity in times of financial crises, two cases arise; the Bailout or Bailin, two opposite choices that elicit the analysis of their effect on moral hazard. This paper attempts to empirically analyze the effect of these two types of events on the behavior of investors. For this end, we use the Emerging Market Bonds Index (EMBI-JP Morgan), and its excess of return, to detect the change in the risk premia through a Markov switching model. The results showed the transition to two types of regime and an effect on moral hazard; Bailout is an incentive of moral hazard, Bailin effectiveness remains subject of credibility. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Bailout" title="Bailout">Bailout</a>, <a href="https://publications.waset.org/abstracts/search?q=Bailin" title=" Bailin"> Bailin</a>, <a href="https://publications.waset.org/abstracts/search?q=Moral%20hazard" title=" Moral hazard"> Moral hazard</a>, <a href="https://publications.waset.org/abstracts/search?q=financial%20crisis" title=" financial crisis"> financial crisis</a>, <a href="https://publications.waset.org/abstracts/search?q=Markov%20switching" title=" Markov switching"> Markov switching</a> </p> <a href="https://publications.waset.org/abstracts/27085/moral-hazard-under-the-effect-of-bailout-and-bailin-events-a-markov-switching-model" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/27085.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">466</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">16831</span> Recognition of Voice Commands of Mentor Robot in Noisy Environment Using Hidden Markov Model</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Khenfer%20Koummich%20Fatma">Khenfer Koummich Fatma</a>, <a href="https://publications.waset.org/abstracts/search?q=Hendel%20Fatiha"> Hendel Fatiha</a>, <a href="https://publications.waset.org/abstracts/search?q=Mesbahi%20Larbi"> Mesbahi Larbi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper presents an approach based on Hidden Markov Models (HMM: Hidden Markov Model) using HTK tools. The goal is to create a human-machine interface with a voice recognition system that allows the operator to teleoperate a mentor robot to execute specific tasks as rotate, raise, close, etc. This system should take into account different levels of environmental noise. This approach has been applied to isolated words representing the robot commands pronounced in two languages: French and Arabic. The obtained recognition rate is the same in both speeches, Arabic and French in the neutral words. However, there is a slight difference in favor of the Arabic speech when Gaussian white noise is added with a Signal to Noise Ratio (SNR) equals 30 dB, in this case; the Arabic speech recognition rate is 69%, and the French speech recognition rate is 80%. This can be explained by the ability of phonetic context of each speech when the noise is added. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Arabic%20speech%20recognition" title="Arabic speech recognition">Arabic speech recognition</a>, <a href="https://publications.waset.org/abstracts/search?q=Hidden%20Markov%20Model%20%28HMM%29" title=" Hidden Markov Model (HMM)"> Hidden Markov Model (HMM)</a>, <a href="https://publications.waset.org/abstracts/search?q=HTK" title=" HTK"> HTK</a>, <a href="https://publications.waset.org/abstracts/search?q=noise" title=" noise"> noise</a>, <a href="https://publications.waset.org/abstracts/search?q=TIMIT" title=" TIMIT"> TIMIT</a>, <a href="https://publications.waset.org/abstracts/search?q=voice%20command" title=" voice command"> voice command</a> </p> <a href="https://publications.waset.org/abstracts/67988/recognition-of-voice-commands-of-mentor-robot-in-noisy-environment-using-hidden-markov-model" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/67988.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">386</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">16830</span> Hidden Markov Movement Modelling with Irregular Data</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Victoria%20Goodall">Victoria Goodall</a>, <a href="https://publications.waset.org/abstracts/search?q=Paul%20Fatti"> Paul Fatti</a>, <a href="https://publications.waset.org/abstracts/search?q=Norman%20Owen-Smith"> Norman Owen-Smith</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Hidden Markov Models have become popular for the analysis of animal tracking data. These models are being used to model the movements of a variety of species in many areas around the world. A common assumption of the model is that the observations need to have regular time steps. In many ecological studies, this will not be the case. The objective of the research is to modify the movement model to allow for irregularly spaced locations and investigate the effect on the inferences which can be made about the latent states. A modification of the likelihood function to allow for these irregular spaced locations is investigated, without using interpolation or averaging the movement rate. The suitability of the modification is investigated using GPS tracking data for lion (Panthera leo) in South Africa, with many observations obtained during the night, and few observations during the day. Many nocturnal predator tracking studies are set up in this way, to obtain many locations at night when the animal is most active and is difficult to observe. Few observations are obtained during the day, when the animal is expected to rest and is potentially easier to observe. Modifying the likelihood function allows the popular Hidden Markov Model framework to be used to model these irregular spaced locations, making use of all the observed data. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=hidden%20Markov%20Models" title="hidden Markov Models">hidden Markov Models</a>, <a href="https://publications.waset.org/abstracts/search?q=irregular%20observations" title=" irregular observations"> irregular observations</a>, <a href="https://publications.waset.org/abstracts/search?q=animal%20movement%20modelling" title=" animal movement modelling"> animal movement modelling</a>, <a href="https://publications.waset.org/abstracts/search?q=nocturnal%20predator" title=" nocturnal predator"> nocturnal predator</a> </p> <a href="https://publications.waset.org/abstracts/56744/hidden-markov-movement-modelling-with-irregular-data" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/56744.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">244</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">16829</span> The Combination of the Mel Frequency Cepstral Coefficients (MFCC), Perceptual Linear Prediction (PLP), JITTER and SHIMMER Coefficients for the Improvement of Automatic Recognition System for Dysarthric Speech</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Brahim-Fares%20Zaidi">Brahim-Fares Zaidi</a>, <a href="https://publications.waset.org/abstracts/search?q=Malika%20Boudraa"> Malika Boudraa</a>, <a href="https://publications.waset.org/abstracts/search?q=Sid-Ahmed%20Selouani"> Sid-Ahmed Selouani</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Our work aims to improve our Automatic Recognition System for Dysarthria Speech (ARSDS) based on the Hidden Models of Markov (HMM) and the Hidden Markov Model Toolkit (HTK) to help people who are sick. With pronunciation problems, we applied two techniques of speech parameterization based on Mel Frequency Cepstral Coefficients (MFCC's) and Perceptual Linear Prediction (PLP's) and concatenated them with JITTER and SHIMMER coefficients in order to increase the recognition rate of a dysarthria speech. For our tests, we used the NEMOURS database that represents speakers with dysarthria and normal speakers. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=hidden%20Markov%20model%20toolkit%20%28HTK%29" title="hidden Markov model toolkit (HTK)">hidden Markov model toolkit (HTK)</a>, <a href="https://publications.waset.org/abstracts/search?q=hidden%20models%20of%20Markov%20%28HMM%29" title=" hidden models of Markov (HMM)"> hidden models of Markov (HMM)</a>, <a href="https://publications.waset.org/abstracts/search?q=Mel-frequency%20cepstral%20coefficients%20%28MFCC%29" title=" Mel-frequency cepstral coefficients (MFCC)"> Mel-frequency cepstral coefficients (MFCC)</a>, <a href="https://publications.waset.org/abstracts/search?q=perceptual%20linear%20prediction%20%28PLP%E2%80%99s%29" title=" perceptual linear prediction (PLP’s)"> perceptual linear prediction (PLP’s)</a> </p> <a href="https://publications.waset.org/abstracts/143303/the-combination-of-the-mel-frequency-cepstral-coefficients-mfcc-perceptual-linear-prediction-plp-jitter-and-shimmer-coefficients-for-the-improvement-of-automatic-recognition-system-for-dysarthric-speech" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/143303.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">161</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">16828</span> Exploring the Activity Fabric of an Intelligent Environment with Hierarchical Hidden Markov Theory</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Chiung-Hui%20Chen">Chiung-Hui Chen</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The Internet of Things (IoT) was designed for widespread convenience. With the smart tag and the sensing network, a large quantity of dynamic information is immediately presented in the IoT. Through the internal communication and interaction, meaningful objects provide real-time services for users. Therefore, the service with appropriate decision-making has become an essential issue. Based on the science of human behavior, this study employed the environment model to record the time sequences and locations of different behaviors and adopted the probability module of the hierarchical Hidden Markov Model for the inference. The statistical analysis was conducted to achieve the following objectives: First, define user behaviors and predict the user behavior routes with the environment model to analyze user purposes. Second, construct the hierarchical Hidden Markov Model according to the logic framework, and establish the sequential intensity among behaviors to get acquainted with the use and activity fabric of the intelligent environment. Third, establish the intensity of the relation between the probability of objects’ being used and the objects. The indicator can describe the possible limitations of the mechanism. As the process is recorded in the information of the system created in this study, these data can be reused to adjust the procedure of intelligent design services. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=behavior" title="behavior">behavior</a>, <a href="https://publications.waset.org/abstracts/search?q=big%20data" title=" big data"> big data</a>, <a href="https://publications.waset.org/abstracts/search?q=hierarchical%20hidden%20Markov%20model" title=" hierarchical hidden Markov model"> hierarchical hidden Markov model</a>, <a href="https://publications.waset.org/abstracts/search?q=intelligent%20object" title=" intelligent object"> intelligent object</a> </p> <a href="https://publications.waset.org/abstracts/68970/exploring-the-activity-fabric-of-an-intelligent-environment-with-hierarchical-hidden-markov-theory" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/68970.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">233</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">16827</span> Optimal Maintenance and Improvement Policies in Water Distribution System: Markov Decision Process Approach</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Jong%20Woo%20Kim">Jong Woo Kim</a>, <a href="https://publications.waset.org/abstracts/search?q=Go%20Bong%20Choi"> Go Bong Choi</a>, <a href="https://publications.waset.org/abstracts/search?q=Sang%20Hwan%20Son"> Sang Hwan Son</a>, <a href="https://publications.waset.org/abstracts/search?q=Dae%20Shik%20Kim"> Dae Shik Kim</a>, <a href="https://publications.waset.org/abstracts/search?q=Jung%20Chul%20Suh"> Jung Chul Suh</a>, <a href="https://publications.waset.org/abstracts/search?q=Jong%20Min%20Lee"> Jong Min Lee</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The Markov Decision Process (MDP) based methodology is implemented in order to establish the optimal schedule which minimizes the cost. Formulation of MDP problem is presented using the information about the current state of pipe, improvement cost, failure cost and pipe deterioration model. The objective function and detailed algorithm of dynamic programming (DP) are modified due to the difficulty of implementing the conventional DP approaches. The optimal schedule derived from suggested model is compared to several policies via Monte Carlo simulation. Validity of the solution and improvement in computational time are proved. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Markov%20decision%20processes" title="Markov decision processes">Markov decision processes</a>, <a href="https://publications.waset.org/abstracts/search?q=dynamic%20programming" title=" dynamic programming"> dynamic programming</a>, <a href="https://publications.waset.org/abstracts/search?q=Monte%20Carlo%20simulation" title=" Monte Carlo simulation"> Monte Carlo simulation</a>, <a href="https://publications.waset.org/abstracts/search?q=periodic%20replacement" title=" periodic replacement"> periodic replacement</a>, <a href="https://publications.waset.org/abstracts/search?q=Weibull%20distribution" title=" Weibull distribution"> Weibull distribution</a> </p> <a href="https://publications.waset.org/abstracts/28043/optimal-maintenance-and-improvement-policies-in-water-distribution-system-markov-decision-process-approach" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/28043.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">424</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">16826</span> Computing Transition Intensity Using Time-Homogeneous Markov Jump Process: Case of South African HIV/AIDS Disposition</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=A.%20Bayaga">A. Bayaga</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This research provides a technical account of estimating Transition Probability using Time-homogeneous Markov Jump Process applying by South African HIV/AIDS data from the Statistics South Africa. It employs Maximum Likelihood Estimator (MLE) model to explore the possible influence of Transition Probability of mortality cases in which case the data was based on actual Statistics South Africa. This was conducted via an integrated demographic and epidemiological model of South African HIV/AIDS epidemic. The model was fitted to age-specific HIV prevalence data and recorded death data using MLE model. Though the previous model results suggest HIV in South Africa has declined and AIDS mortality rates have declined since 2002 – 2013, in contrast, our results differ evidently with the generally accepted HIV models (Spectrum/EPP and ASSA2008) in South Africa. However, there is the need for supplementary research to be conducted to enhance the demographic parameters in the model and as well apply it to each of the nine (9) provinces of South Africa. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=AIDS%20mortality%20rates" title="AIDS mortality rates">AIDS mortality rates</a>, <a href="https://publications.waset.org/abstracts/search?q=epidemiological%20model" title=" epidemiological model"> epidemiological model</a>, <a href="https://publications.waset.org/abstracts/search?q=time-homogeneous%20markov%20jump%20process" title=" time-homogeneous markov jump process"> time-homogeneous markov jump process</a>, <a href="https://publications.waset.org/abstracts/search?q=transition%20probability" title=" transition probability"> transition probability</a>, <a href="https://publications.waset.org/abstracts/search?q=statistics%20South%20Africa" title=" statistics South Africa"> statistics South Africa</a> </p> <a href="https://publications.waset.org/abstracts/35299/computing-transition-intensity-using-time-homogeneous-markov-jump-process-case-of-south-african-hivaids-disposition" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/35299.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">496</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">16825</span> Stability Bound of Ruin Probability in a Reduced Two-Dimensional Risk Model</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Zina%20Benouaret">Zina Benouaret</a>, <a href="https://publications.waset.org/abstracts/search?q=Djamil%20Aissani"> Djamil Aissani</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this work, we introduce the qualitative and quantitative concept of the strong stability method in the risk process modeling two lines of business of the same insurance company or an insurance and re-insurance companies that divide between them both claims and premiums with a certain proportion. The approach proposed is based on the identification of the ruin probability associate to the model considered, with a stationary distribution of a Markov random process called a reversed process. Our objective, after clarifying the condition and the perturbation domain of parameters, is to obtain the stability inequality of the ruin probability which is applied to estimate the approximation error of a model with disturbance parameters by the considered model. In the stability bound obtained, all constants are explicitly written. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Markov%20chain" title="Markov chain">Markov chain</a>, <a href="https://publications.waset.org/abstracts/search?q=risk%20models" title=" risk models"> risk models</a>, <a href="https://publications.waset.org/abstracts/search?q=ruin%20probabilities" title=" ruin probabilities"> ruin probabilities</a>, <a href="https://publications.waset.org/abstracts/search?q=strong%20stability%20analysis" title=" strong stability analysis"> strong stability analysis</a> </p> <a href="https://publications.waset.org/abstracts/85274/stability-bound-of-ruin-probability-in-a-reduced-two-dimensional-risk-model" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/85274.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">249</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">16824</span> A New Verification Based Congestion Control Scheme in Mobile Networks</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=P.%20K.%20Guha%20Thakurta">P. K. Guha Thakurta</a>, <a href="https://publications.waset.org/abstracts/search?q=Shouvik%20Roy"> Shouvik Roy</a>, <a href="https://publications.waset.org/abstracts/search?q=Bhawana%20Raj"> Bhawana Raj</a> </p> <p class="card-text"><strong>Abstract:</strong></p> A congestion control scheme in mobile networks is proposed in this paper through a verification based model. The model proposed in this work is represented through performance metric like buffer Occupancy, latency and packet loss rate. Based on pre-defined values, each of the metric is introduced in terms of three different states. A Markov chain based model for the proposed work is introduced to monitor the occurrence of the corresponding state transitions. Thus, the estimation of the network status is obtained in terms of performance metric. In addition, the improved performance of our proposed model over existing works is shown with experimental results. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=congestion" title="congestion">congestion</a>, <a href="https://publications.waset.org/abstracts/search?q=mobile%20networks" title=" mobile networks"> mobile networks</a>, <a href="https://publications.waset.org/abstracts/search?q=buffer" title=" buffer"> buffer</a>, <a href="https://publications.waset.org/abstracts/search?q=delay" title=" delay"> delay</a>, <a href="https://publications.waset.org/abstracts/search?q=call%20drop" title=" call drop"> call drop</a>, <a href="https://publications.waset.org/abstracts/search?q=markov%20chain" title=" markov chain"> markov chain</a> </p> <a href="https://publications.waset.org/abstracts/19020/a-new-verification-based-congestion-control-scheme-in-mobile-networks" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/19020.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">441</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">16823</span> An Automatic Speech Recognition Tool for the Filipino Language Using the HTK System</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=John%20Lorenzo%20Bautista">John Lorenzo Bautista</a>, <a href="https://publications.waset.org/abstracts/search?q=Yoon-Joong%20Kim"> Yoon-Joong Kim</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper presents the development of a Filipino speech recognition tool using the HTK System. The system was trained from a subset of the Filipino Speech Corpus developed by the DSP Laboratory of the University of the Philippines-Diliman. The speech corpus was both used in training and testing the system by estimating the parameters for phonetic HMM-based (Hidden-Markov Model) acoustic models. Experiments on different mixture-weights were incorporated in the study. The phoneme-level word-based recognition of a 5-state HMM resulted in an average accuracy rate of 80.13 for a single-Gaussian mixture model, 81.13 after implementing a phoneme-alignment, and 87.19 for the increased Gaussian-mixture weight model. The highest accuracy rate of 88.70% was obtained from a 5-state model with 6 Gaussian mixtures. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Filipino%20language" title="Filipino language">Filipino language</a>, <a href="https://publications.waset.org/abstracts/search?q=Hidden%20Markov%20Model" title=" Hidden Markov Model"> Hidden Markov Model</a>, <a href="https://publications.waset.org/abstracts/search?q=HTK%20system" title=" HTK system"> HTK system</a>, <a href="https://publications.waset.org/abstracts/search?q=speech%20recognition" title=" speech recognition"> speech recognition</a> </p> <a href="https://publications.waset.org/abstracts/10240/an-automatic-speech-recognition-tool-for-the-filipino-language-using-the-htk-system" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/10240.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">480</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">16822</span> Segmentation of Piecewise Polynomial Regression Model by Using Reversible Jump MCMC Algorithm</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Suparman">Suparman</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Piecewise polynomial regression model is very flexible model for modeling the data. If the piecewise polynomial regression model is matched against the data, its parameters are not generally known. This paper studies the parameter estimation problem of piecewise polynomial regression model. The method which is used to estimate the parameters of the piecewise polynomial regression model is Bayesian method. Unfortunately, the Bayes estimator cannot be found analytically. Reversible jump MCMC algorithm is proposed to solve this problem. Reversible jump MCMC algorithm generates the Markov chain that converges to the limit distribution of the posterior distribution of piecewise polynomial regression model parameter. The resulting Markov chain is used to calculate the Bayes estimator for the parameters of piecewise polynomial regression model. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=piecewise%20regression" title="piecewise regression">piecewise regression</a>, <a href="https://publications.waset.org/abstracts/search?q=bayesian" title=" bayesian"> bayesian</a>, <a href="https://publications.waset.org/abstracts/search?q=reversible%20jump%20MCMC" title=" reversible jump MCMC"> reversible jump MCMC</a>, <a href="https://publications.waset.org/abstracts/search?q=segmentation" title=" segmentation"> segmentation</a> </p> <a href="https://publications.waset.org/abstracts/46201/segmentation-of-piecewise-polynomial-regression-model-by-using-reversible-jump-mcmc-algorithm" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/46201.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">373</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">16821</span> Speed Breaker/Pothole Detection Using Hidden Markov Models: A Deep Learning Approach</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Surajit%20Chakrabarty">Surajit Chakrabarty</a>, <a href="https://publications.waset.org/abstracts/search?q=Piyush%20Chauhan"> Piyush Chauhan</a>, <a href="https://publications.waset.org/abstracts/search?q=Subhasis%20Panda"> Subhasis Panda</a>, <a href="https://publications.waset.org/abstracts/search?q=Sujoy%20Bhattacharya"> Sujoy Bhattacharya</a> </p> <p class="card-text"><strong>Abstract:</strong></p> A large proportion of roads in India are not well maintained as per the laid down public safety guidelines leading to loss of direction control and fatal accidents. We propose a technique to detect speed breakers and potholes using mobile sensor data captured from multiple vehicles and provide a profile of the road. This would, in turn, help in monitoring roads and revolutionize digital maps. Incorporating randomness in the model formulation for detection of speed breakers and potholes is crucial due to substantial heterogeneity observed in data obtained using a mobile application from multiple vehicles driven by different drivers. This is accomplished with Hidden Markov Models, whose hidden state sequence is found for each time step given the observables sequence, and are then fed as input to LSTM network with peephole connections. A precision score of 0.96 and 0.63 is obtained for classifying bumps and potholes, respectively, a significant improvement from the machine learning based models. Further visualization of bumps/potholes is done by converting time series to images using Markov Transition Fields where a significant demarcation among bump/potholes is observed. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=deep%20learning" title="deep learning">deep learning</a>, <a href="https://publications.waset.org/abstracts/search?q=hidden%20Markov%20model" title=" hidden Markov model"> hidden Markov model</a>, <a href="https://publications.waset.org/abstracts/search?q=pothole" title=" pothole"> pothole</a>, <a href="https://publications.waset.org/abstracts/search?q=speed%20breaker" title=" speed breaker"> speed breaker</a> </p> <a href="https://publications.waset.org/abstracts/121459/speed-breakerpothole-detection-using-hidden-markov-models-a-deep-learning-approach" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/121459.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">144</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">16820</span> Artificial Neural Networks and Hidden Markov Model in Landslides Prediction</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=C.%20S.%20Subhashini">C. S. Subhashini</a>, <a href="https://publications.waset.org/abstracts/search?q=H.%20L.%20Premaratne"> H. L. Premaratne </a> </p> <p class="card-text"><strong>Abstract:</strong></p> Landslides are the most recurrent and prominent disaster in Sri Lanka. Sri Lanka has been subjected to a number of extreme landslide disasters that resulted in a significant loss of life, material damage, and distress. It is required to explore a solution towards preparedness and mitigation to reduce recurrent losses associated with landslides. Artificial Neural Networks (ANNs) and Hidden Markov Model (HMMs) are now widely used in many computer applications spanning multiple domains. This research examines the effectiveness of using Artificial Neural Networks and Hidden Markov Model in landslides predictions and the possibility of applying the modern technology to predict landslides in a prominent geographical area in Sri Lanka. A thorough survey was conducted with the participation of resource persons from several national universities in Sri Lanka to identify and rank the influencing factors for landslides. A landslide database was created using existing topographic; soil, drainage, land cover maps and historical data. The landslide related factors which include external factors (Rainfall and Number of Previous Occurrences) and internal factors (Soil Material, Geology, Land Use, Curvature, Soil Texture, Slope, Aspect, Soil Drainage, and Soil Effective Thickness) are extracted from the landslide database. These factors are used to recognize the possibility to occur landslides by using an ANN and HMM. The model acquires the relationship between the factors of landslide and its hazard index during the training session. These models with landslide related factors as the inputs will be trained to predict three classes namely, ‘landslide occurs’, ‘landslide does not occur’ and ‘landslide likely to occur’. Once trained, the models will be able to predict the most likely class for the prevailing data. Finally compared two models with regards to prediction accuracy, False Acceptance Rates and False Rejection rates and This research indicates that the Artificial Neural Network could be used as a strong decision support system to predict landslides efficiently and effectively than Hidden Markov Model. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=landslides" title="landslides">landslides</a>, <a href="https://publications.waset.org/abstracts/search?q=influencing%20factors" title=" influencing factors"> influencing factors</a>, <a href="https://publications.waset.org/abstracts/search?q=neural%20network%20model" title=" neural network model"> neural network model</a>, <a href="https://publications.waset.org/abstracts/search?q=hidden%20markov%20model" title=" hidden markov model"> hidden markov model</a> </p> <a href="https://publications.waset.org/abstracts/21014/artificial-neural-networks-and-hidden-markov-model-in-landslides-prediction" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/21014.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">384</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">16819</span> A Semi-Markov Chain-Based Model for the Prediction of Deterioration of Concrete Bridges in Quebec</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Eslam%20Mohammed%20Abdelkader">Eslam Mohammed Abdelkader</a>, <a href="https://publications.waset.org/abstracts/search?q=Mohamed%20Marzouk"> Mohamed Marzouk</a>, <a href="https://publications.waset.org/abstracts/search?q=Tarek%20Zayed"> Tarek Zayed</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Infrastructure systems are crucial to every aspect of life on Earth. Existing Infrastructure is subjected to degradation while the demands are growing for a better infrastructure system in response to the high standards of safety, health, population growth, and environmental protection. Bridges play a crucial role in urban transportation networks. Moreover, they are subjected to high level of deterioration because of the variable traffic loading, extreme weather conditions, cycles of freeze and thaw, etc. The development of Bridge Management Systems (BMSs) has become a fundamental imperative nowadays especially in the large transportation networks due to the huge variance between the need for maintenance actions, and the available funds to perform such actions. Deterioration models represent a very important aspect for the effective use of BMSs. This paper presents a probabilistic time-based model that is capable of predicting the condition ratings of the concrete bridge decks along its service life. The deterioration process of the concrete bridge decks is modeled using semi-Markov process. One of the main challenges of the Markov Chain Decision Process (MCDP) is the construction of the transition probability matrix. Yet, the proposed model overcomes this issue by modeling the sojourn times based on some probability density functions. The sojourn times of each condition state are fitted to probability density functions based on some goodness of fit tests such as Kolmogorov-Smirnov test, Anderson Darling, and chi-squared test. The parameters of the probability density functions are obtained using maximum likelihood estimation (MLE). The condition ratings obtained from the Ministry of Transportation in Quebec (MTQ) are utilized as a database to construct the deterioration model. Finally, a comparison is conducted between the Markov Chain and semi-Markov chain to select the most feasible prediction model. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=bridge%20management%20system" title="bridge management system">bridge management system</a>, <a href="https://publications.waset.org/abstracts/search?q=bridge%20decks" title=" bridge decks"> bridge decks</a>, <a href="https://publications.waset.org/abstracts/search?q=deterioration%20model" title=" deterioration model"> deterioration model</a>, <a href="https://publications.waset.org/abstracts/search?q=Semi-Markov%20chain" title=" Semi-Markov chain"> Semi-Markov chain</a>, <a href="https://publications.waset.org/abstracts/search?q=sojourn%20times" title=" sojourn times"> sojourn times</a>, <a href="https://publications.waset.org/abstracts/search?q=maximum%20likelihood%20estimation" title=" maximum likelihood estimation"> maximum likelihood estimation</a> </p> <a href="https://publications.waset.org/abstracts/83317/a-semi-markov-chain-based-model-for-the-prediction-of-deterioration-of-concrete-bridges-in-quebec" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/83317.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">211</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">16818</span> A Fast, Reliable Technique for Face Recognition Based on Hidden Markov Model</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Sameh%20Abaza">Sameh Abaza</a>, <a href="https://publications.waset.org/abstracts/search?q=Mohamed%20Ibrahim"> Mohamed Ibrahim</a>, <a href="https://publications.waset.org/abstracts/search?q=Tarek%20Mahmoud"> Tarek Mahmoud</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Due to the development in the digital image processing, its wide use in many applications such as medical, security, and others, the need for more accurate techniques that are reliable, fast and robust is vehemently demanded. In the field of security, in particular, speed is of the essence. In this paper, a pattern recognition technique that is based on the use of Hidden Markov Model (HMM), K-means and the Sobel operator method is developed. The proposed technique is proved to be fast with respect to some other techniques that are investigated for comparison. Moreover, it shows its capability of recognizing the normal face (center part) as well as face boundary. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=HMM" title="HMM">HMM</a>, <a href="https://publications.waset.org/abstracts/search?q=K-Means" title=" K-Means"> K-Means</a>, <a href="https://publications.waset.org/abstracts/search?q=Sobel" title=" Sobel"> Sobel</a>, <a href="https://publications.waset.org/abstracts/search?q=accuracy" title=" accuracy"> accuracy</a>, <a href="https://publications.waset.org/abstracts/search?q=face%20recognition" title=" face recognition"> face recognition</a> </p> <a href="https://publications.waset.org/abstracts/60973/a-fast-reliable-technique-for-face-recognition-based-on-hidden-markov-model" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/60973.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">331</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">16817</span> Modeling Usage Patterns of Mobile App Service in App Market Using Hidden Markov Model</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Yangrae%20Cho">Yangrae Cho</a>, <a href="https://publications.waset.org/abstracts/search?q=Jinseok%20Kim"> Jinseok Kim</a>, <a href="https://publications.waset.org/abstracts/search?q=Yongtae%20Park"> Yongtae Park</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Mobile app service ecosystem has been abruptly emerged, explosively grown, and dynamically transformed. In contrast with product markets in which product sales directly cause increment in firm’s income, customer’s usage is less visible but more valuable in service market. Especially, the market situation with cutthroat competition in mobile app store makes securing and keeping of users as vital. Although a few service firms try to manage their apps’ usage patterns by fitting on S-curve or applying other forecasting techniques, the time series approaches based on past sequential data are subject to fundamental limitation in the market where customer’s attention is being moved unpredictably and dynamically. We therefore propose a new conceptual approach for detecting usage pattern of mobile app service with Hidden Markov Model (HMM) which is based on the dual stochastic structure and mainly used to clarify unpredictable and dynamic sequential patterns in voice recognition or stock forecasting. Our approach could be practically utilized for app service firms to manage their services’ lifecycles and academically expanded to other markets. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=mobile%20app%20service" title="mobile app service">mobile app service</a>, <a href="https://publications.waset.org/abstracts/search?q=usage%20pattern" title=" usage pattern"> usage pattern</a>, <a href="https://publications.waset.org/abstracts/search?q=Hidden%20Markov%20Model" title=" Hidden Markov Model"> Hidden Markov Model</a>, <a href="https://publications.waset.org/abstracts/search?q=pattern%20detection" title=" pattern detection"> pattern detection</a> </p> <a href="https://publications.waset.org/abstracts/40873/modeling-usage-patterns-of-mobile-app-service-in-app-market-using-hidden-markov-model" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/40873.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">336</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">16816</span> Part of Speech Tagging Using Statistical Approach for Nepali Text</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Archit%20Yajnik">Archit Yajnik</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Part of Speech Tagging has always been a challenging task in the era of Natural Language Processing. This article presents POS tagging for Nepali text using Hidden Markov Model and Viterbi algorithm. From the Nepali text, annotated corpus training and testing data set are randomly separated. Both methods are employed on the data sets. Viterbi algorithm is found to be computationally faster and accurate as compared to HMM. The accuracy of 95.43% is achieved using Viterbi algorithm. Error analysis where the mismatches took place is elaborately discussed. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=hidden%20markov%20model" title="hidden markov model">hidden markov model</a>, <a href="https://publications.waset.org/abstracts/search?q=natural%20language%20processing" title=" natural language processing"> natural language processing</a>, <a href="https://publications.waset.org/abstracts/search?q=POS%20tagging" title=" POS tagging"> POS tagging</a>, <a href="https://publications.waset.org/abstracts/search?q=viterbi%20algorithm" title=" viterbi algorithm"> viterbi algorithm</a> </p> <a href="https://publications.waset.org/abstracts/61160/part-of-speech-tagging-using-statistical-approach-for-nepali-text" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/61160.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">329</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">16815</span> Estimating Bridge Deterioration for Small Data Sets Using Regression and Markov Models</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Yina%20F.%20Mu%C3%B1oz">Yina F. Muñoz</a>, <a href="https://publications.waset.org/abstracts/search?q=Alexander%20Paz"> Alexander Paz</a>, <a href="https://publications.waset.org/abstracts/search?q=Hanns%20De%20La%20Fuente-Mella"> Hanns De La Fuente-Mella</a>, <a href="https://publications.waset.org/abstracts/search?q=Joaquin%20V.%20Fari%C3%B1a"> Joaquin V. Fariña</a>, <a href="https://publications.waset.org/abstracts/search?q=Guilherme%20M.%20Sales"> Guilherme M. Sales</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The primary approach for estimating bridge deterioration uses Markov-chain models and regression analysis. Traditional Markov models have problems in estimating the required transition probabilities when a small sample size is used. Often, reliable bridge data have not been taken over large periods, thus large data sets may not be available. This study presents an important change to the traditional approach by using the Small Data Method to estimate transition probabilities. The results illustrate that the Small Data Method and traditional approach both provide similar estimates; however, the former method provides results that are more conservative. That is, Small Data Method provided slightly lower than expected bridge condition ratings compared with the traditional approach. Considering that bridges are critical infrastructures, the Small Data Method, which uses more information and provides more conservative estimates, may be more appropriate when the available sample size is small. In addition, regression analysis was used to calculate bridge deterioration. Condition ratings were determined for bridge groups, and the best regression model was selected for each group. The results obtained were very similar to those obtained when using Markov chains; however, it is desirable to use more data for better results. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=concrete%20bridges" title="concrete bridges">concrete bridges</a>, <a href="https://publications.waset.org/abstracts/search?q=deterioration" title=" deterioration"> deterioration</a>, <a href="https://publications.waset.org/abstracts/search?q=Markov%20chains" title=" Markov chains"> Markov chains</a>, <a href="https://publications.waset.org/abstracts/search?q=probability%20matrix" title=" probability matrix"> probability matrix</a> </p> <a href="https://publications.waset.org/abstracts/43375/estimating-bridge-deterioration-for-small-data-sets-using-regression-and-markov-models" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/43375.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> 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