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Prediction of State of Wireless Network Using Markov and Hidden Markov Model  [cached]
MD. Osman Gani,Hasan Sarwar,Chowdhury Mofizur Rahman
Journal of Networks , 2009, DOI: 10.4304/jnw.4.10.976-984
Abstract: Optimal resource allocation and higher quality of service is a much needed requirement in case of wireless networks. In order to improve the above factors, intelligent prediction of network behavior plays a very important role. Markov Model (MM) and Hidden Markov Model (HMM) are proven prediction techniques used in many fields. In this paper, we have used Markov and Hidden Markov prediction tools to predict the number of wireless devices that are connected to a specific Access Point (AP) at a specific instant of time. Prediction has been performed in two stages. In the first stage, we have found state sequence of wireless access points (AP) in a wireless network by observing the traffic load sequence in time. It is found that a particular choice of data may lead to 91% accuracy in predicting the real scenario. In the second stage, we have used Markov Model to find out the future state sequence of the previously found sequence from first stage. The prediction of next state of an AP performed by Markov Tool shows 88.71% accuracy. It is found that Markov Model can predict with an accuracy of 95.55% if initial transition matrix is calculated directly. We have also shown that O(1) Markov Model gives slightly better accuracy in prediction compared to O(2) MM for predicting far future.
Hidden Markov Models as a Process Monitor in Robotic Assembly
Geir E. Hovland,Brenan J. McCarragher
Modeling, Identification and Control , 1999, DOI: 10.4173/mic.1999.4.2
Abstract: A process monitor for robotic assembly based on hidden Markov models (HMMs) is presented. The HMM process monitor is based on the dynamic force/torque signals arising from interaction between the workpiece and the environment. The HMMs represent a stochastic, knowledge-based system in which the models are trained off-line with the Baum-Welch reestimation algorithm. The assembly task is modeled as a discrete event dynamic system in which a discrete event is defined as a change in contact state between the workpiece and the environment. Our method (1) allows for dynamic motions of the workpiece, (2) accounts for sensor noise and friction, and (3) exploits the fact that the amount of force information is large when there is a sudden change of discrete state in robotic assembly. After the HMMs have been trained, the authors use them on-line in a 2D experimental setup to recognize discrete events as they occur. Successful event recognition with an accuracy as high as 97with a training set size of only 20 examples for each discrete event.
Subspace estimation and prediction methods for hidden Markov models  [PDF]
Sofia Andersson,Tobias Rydén
Statistics , 2009, DOI: 10.1214/09-AOS711
Abstract: Hidden Markov models (HMMs) are probabilistic functions of finite Markov chains, or, put in other words, state space models with finite state space. In this paper, we examine subspace estimation methods for HMMs whose output lies a finite set as well. In particular, we study the geometric structure arising from the nonminimality of the linear state space representation of HMMs, and consistency of a subspace algorithm arising from a certain factorization of the singular value decomposition of the estimated linear prediction matrix. For this algorithm, we show that the estimates of the transition and emission probability matrices are consistent up to a similarity transformation, and that the $m$-step linear predictor computed from the estimated system matrices is consistent, i.e., converges to the true optimal linear $m$-step predictor.
Hidden Markov Model Based Visual Perception Filtering in Robotic Soccer
Can Kavaklioglu,H. Levent Akin
International Journal of Advanced Robotic Systems , 2009,
Abstract: Autonomous robots can initiate their mission plans only after gathering sufficient information about the environment. Therefore reliable perception information plays a major role in the overall success of an autonomous robot. The Hidden Markov Model based post-perception filtering module proposed in this paper aims to identify and remove spurious perception information in a given perception sequence using the generic metapose definition. This method allows representing uncertainty in more abstract terms compared to the common physical representations. Our experiments with the four legged AIBO robot indicated that the proposed module improved perception and localization performance significantly.
Hidden markov model for the prediction of transmembrane proteins using MATLAB  [cached]
Navaneet Chaturvedi*,Vinay Kumar Singh3,Sudhanshu Shanker2,Dhiraj Sinha4
Bioinformation , 2011,
Abstract: Since membranous proteins play a key role in drug targeting therefore transmembrane proteins prediction is active and challenging area of biological sciences. Location based prediction of transmembrane proteins are significant for functional annotation of protein sequences. Hidden markov model based method was widely applied for transmembrane topology prediction. Here we have presented a revised and a better understanding model than an existing one for transmembrane protein prediction. Scripting on MATLAB was built and compiled for parameter estimation of model and applied this model on amino acid sequence to know the transmembrane and its adjacent locations. Estimated model of transmembrane topology was based on TMHMM model architecture. Only 7 super states are defined in the given dataset, which were converted to 96 states on the basis of their length in sequence. Accuracy of the prediction of model was observed about 74 %, is a good enough in the area of transmembrane topology prediction. Therefore we have concluded the hidden markov model plays crucial role in transmembrane helices prediction on MATLAB platform and it could also be useful for drug discovery strategy.
Analysis of an optimal hidden Markov model for secondary structure prediction
Juliette Martin, Jean-Fran?ois Gibrat, Fran?ois Rodolphe
BMC Structural Biology , 2006, DOI: 10.1186/1472-6807-6-25
Abstract: Our HMM is designed without prior knowledge. It is chosen within a collection of models of increasing size, using statistical and accuracy criteria. The resulting model has 36 hidden states: 15 that model α-helices, 12 that model coil and 9 that model β-strands. Connections between hidden states and state emission probabilities reflect the organization of protein structures into secondary structure segments. We start by analyzing the model features and see how it offers a new vision of local structures. We then use it for secondary structure prediction. Our model appears to be very efficient on single sequences, with a Q3 score of 68.8%, more than one point above PSIPRED prediction on single sequences. A straightforward extension of the method allows the use of multiple sequence alignments, rising the Q3 score to 75.5%.The hidden Markov model presented here achieves valuable prediction results using only a limited number of parameters. It provides an interpretable framework for protein secondary structure architecture. Furthermore, it can be used as a tool for generating protein sequences with a given secondary structure content.Predicting the secondary structure of a protein is often a first step toward 3D structure prediction of a particular protein. In comparative modeling, secondary structure prediction is used to refine sequence alignments, or to improve the detection of distant homologs [1]. Moreover, it is of prime importance when prediction is made without a template [2]. For all these reasons protein secondary structure prediction has remained an active field for years. Virtually all statistical and learning methods have been applied to this task. Nowadays, the best methods achieve prediction rate of about 80% using homologous sequence information. A survey of the Eva on-line evaluation [3] shows that the top performing methods include several approaches based on neural networks, e.g. PSIPRED by Jones et al [4], PROFsec and PHDpsi by Rost et al [5]. Recentl
Real Time Intrusion Prediction based on Optimized Alerts with Hidden Markov Model  [cached]
Alireza Shameli Sendi,Michel Dagenais,Masoume Jabbarifar,Mario Couture
Journal of Networks , 2012, DOI: 10.4304/jnw.7.2.311-321
Abstract: Cyber attacks and malicious activities are rapidlybecoming a major threat to proper secure organization.Many security tools may be installed in distributed systemsand monitor all events in a network. Security managers oftenhave to process huge numbers of alerts per day, produced bysuch tools. Intrusion prediction is an important technique tohelp response systems reacting properly before the networkis compromised. In this paper, we propose a frameworkto predict multi-step attacks before they pose a serioussecurity risk. Hidden Markov Model (HMM) is used toextract the interactions between attackers and networks.Since alerts correlation plays a critical role in prediction,a modulated alert severity through correlation concept isused instead of just individual alerts and their severity.Modulated severity generates prediction alarms for the mostinteresting steps of multi-step attacks and improves theaccuracy. Our experiments on the Lincoln Laboratory 2000data set show that our algorithm perfectly predicts multi-step attacks before they can compromise the network.
NLStradamus: a simple Hidden Markov Model for nuclear localization signal prediction
Alex N Nguyen Ba, Anastassia Pogoutse, Nicholas Provart, Alan M Moses
BMC Bioinformatics , 2009, DOI: 10.1186/1471-2105-10-202
Abstract: In this paper, we present an analysis of characterized NLSs in yeast, and find, despite the large number of nuclear import pathways, that NLSs seem to show similar patterns of amino acid residues. We test current prediction methods and observe a low true positive rate. We therefore suggest an approach using hidden Markov models (HMMs) to predict novel NLSs in proteins. We show that our method is able to consistently find 37% of the NLSs with a low false positive rate and that our method retains its true positive rate outside of the yeast data set used for the training parameters.Our implementation of this model, NLStradamus, is made available at: http://www.moseslab.csb.utoronto.ca/NLStradamus/ webciteEukaryotic cells are defined by the presence of their nucleus. The nuclear membrane enclosing the genetic material of the cell is selective in its import of material through its nuclear pores and this translocation is mediated by cellular mechanisms [1,2].Proteins entering the nucleus must do so through proteins forming the nuclear pores: the nuclear pore complex [3,4]. The pores allow the passive diffusion of small proteins, but bigger proteins entering the nucleus are usually bound by karyopherin complexes on their nuclear localization signal [5]. Although there are many nuclear import pathways in eukaryotic cells, most of these have not been characterized in detail. The best understood is the classical NLS pathway. The recognition of classical NLSs on nuclear proteins is done by the importin-α subunit which in turn is recognized by the importin-β subunit. This trimer (cargo, importin-α and importin-β) is then imported to the nucleus after series of enzymatic steps [1,6]. Other families of NLSs are independent of importin-α, and may bind directly to one of the members of the importin-β superfamily [1].Classical NLSs show characteristic patterns of basic residues loosely matching two consensus sequences, K(K/R)X(K/R) and KRX10–12KRXK, termed the 'monopartite' and 'bip
Prediction of protein binding sites in protein structures using hidden Markov support vector machine
Bin Liu, Xiaolong Wang, Lei Lin, Buzhou Tang, Qiwen Dong, Xuan Wang
BMC Bioinformatics , 2009, DOI: 10.1186/1471-2105-10-381
Abstract: In this study, we introduce a novel machine learning model hidden Markov support vector machine for protein binding site prediction. The model treats the protein binding site prediction as a sequential labelling task based on the maximum margin criterion. Common features derived from protein sequences and structures, including protein sequence profile and residue accessible surface area, are used to train hidden Markov support vector machine. When tested on six data sets, the method based on hidden Markov support vector machine shows better performance than some state-of-the-art methods, including artificial neural networks, support vector machines and conditional random field. Furthermore, its running time is several orders of magnitude shorter than that of the compared methods.The improved prediction performance and computational efficiency of the method based on hidden Markov support vector machine can be attributed to the following three factors. Firstly, the relation between labels of neighbouring residues is useful for protein binding site prediction. Secondly, the kernel trick is very advantageous to this field. Thirdly, the complexity of the training step for hidden Markov support vector machine is linear with the number of training samples by using the cutting-plane algorithm.Identification of protein binding site has significant impact on understanding protein function. Development of fast and accurate computational methods for protein binding site prediction is helpful in identifying functionally important amino acid residues and facilitating experimental efforts to catalogue protein interactions. It also plays a key role in enhancing computational docking studies, drug design and functional annotation for the structurally determined proteins with unknown function [1].Protein binding site prediction has been studied for decades [2-4]. Several machine learning methods have been applied in this field. These methods can be split into two groups: classificati
Profile Hidden Markov Model for Detection and Prediction of Hepatitis C Virus Mutation  [PDF]
Mohamed El Nahas,Samar Kassim,Nabila Shikoun
International Journal of Computer Science Issues , 2012,
Abstract: Hepatitis C virus (HCV) is a widely spread disease all over the world. HCV has very high mutation rate that makes it resistant to antibodies. Modeling HCV to identify the virus mutation process is essential to its detection and predicting its evolution. This paper presents a model of HCV based on profile hidden Markov model (PHMM) architecture. An iterative model learning procedure is proposed and applied to both full-length sequence of virus and its very high variation (mutation) zone called NS5A. A pilot study on HCV dataset of type 4 is conducted which is of special concern in Egypt
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