Application of Monte Carlo Search for Performance Improvement of Web Page Prediction

Prediction in web mining is one of the most complex tasks which will reduce web user latency. The main objective of this research work is to reduce web user latency by predicting and prefetching the users future request page. Web user activities were analyzed and monitored from the web server log file. The present work consists of two phases. In the first phase a directed graph is constructed for web user navigation with the reduction of repeated path. In the second phase, Monte Carlo search is applied on the constructed graph to predict the future request and prefetch the page. This work is successfully implemented and the prediction technique gives a better accuracy. This implementation paves a new way to prefetch the predicted pages at user end to reduce the user latency. Proposed Monte Carlo Prediction (MCP) Algorithm is compared with the existing algorithm Hidden Markov model. Proposed algorithm achieved better accuracy than the Hidden Markov Model. Accuracy is measured for the predicted web pages and achieved the optimal results.

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Application of Monte Carlo Markov chain to determination of hidden Markov model for mobile satellite channels

2004 IEEE 59th Vehicular Technology Conference. VTC 2004-Spring (IEEE Cat. No.04CH37514) ◽

10.1109/vetecs.2004.1387939 ◽

2005 ◽

Cited By ~ 1

Author(s):

C. Alasseur ◽

L. Husson ◽

F. Perez-Fontan

Keyword(s):

Monte Carlo ◽

Markov Chain ◽

Markov Model ◽

Hidden Markov Model ◽

Hidden Markov ◽

Monte Carlo Markov Chain ◽

Satellite Channels ◽

Mobile Satellite

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An Enhanced Hidden Semi-Markov model for Outlier Detection in Multivariate Datasets

10.22541/au.162685414.44316319/v1 ◽

2021 ◽

Author(s):

G Manoharan ◽

K Sivakumar

Keyword(s):

Markov Model ◽

Hidden Markov Model ◽

Outlier Detection ◽

Hidden Markov ◽

Research Work ◽

Detection Accuracy ◽

Detection Techniques ◽

Detection Model ◽

Huge Data ◽

Proposed Model

Outlier detection in data mining is an important arena where detection models are developed to discover the objects that do not confirm the expected behavior. The generation of huge data in real time applications makes the outlier detection process into more crucial and challenging. Traditional detection techniques based on mean and covariance are not suitable to handle large amount of data and the results are affected by outliers. So it is essential to develop an efficient outlier detection model to detect outliers in the large dataset. The objective of this research work is to develop an efficient outlier detection model for multivariate data employing the enhanced Hidden Semi-Markov Model (HSMM). It is an extension of conventional Hidden Markov Model (HMM) where the proposed model allows arbitrary time distribution in its states to detect outliers. Experimental results demonstrate the better performance of proposed model in terms of detection accuracy, detection rate. Compared to conventional Hidden Markov Model based outlier detection the detection accuracy of proposed model is obtained as 98.62% which is significantly better for large multivariate datasets.

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Parallel sequential Monte Carlo samplers and estimation of the number of states in a Hidden Markov Model

Annals of the Institute of Statistical Mathematics ◽

10.1007/s10463-014-0450-4 ◽

2014 ◽

Vol 66 (3) ◽

pp. 553-575 ◽

Cited By ~ 2

Author(s):

Christopher F. H. Nam ◽

John A. D. Aston ◽

Adam M. Johansen

Keyword(s):

Monte Carlo ◽

Markov Model ◽

Hidden Markov Model ◽

Sequential Monte Carlo ◽

Hidden Markov

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Particle Swarm Optimization For Hidden Markov Model

International Journal of Knowledge and Systems Science ◽

10.4018/ijkss.2015040101 ◽

2015 ◽

Vol 6 (2) ◽

pp. 1-15

Author(s):

Nabil M. Hewahi

Keyword(s):

Particle Swarm Optimization ◽

Markov Model ◽

Hidden Markov Model ◽

Intelligent Systems ◽

Hidden Markov ◽

Particle Swarm ◽

Structure Design ◽

Second Phase ◽

Good Representation ◽

Swarm Optimization

Hidden Markov Model (HMM) is a very well known method as a statistical model used for intelligent systems applications. Due to its involvement in various applications, it would be very important to have a good representation of HMM for the given problem to achieve good results. In this paper, we propose a theoretical approach that can be followed to obtain the best structure of HMM based on Particle Swarm Optimization (PSO) concepts. Given a set of comprehensive visible and invisible states, we propose a method based on PSO concepts to evolve an optimum HMM structure design. The proposed approach deals with two factors related to HMM, generating new states and updating probability values. The main steps followed in the proposed approach involve three main phases, the first phase is generating randomly a population of HMMs, the second phase is converting the generated HMM to PSO required format and the third phase is the application of PSO to find out the optimum HMM . The importance of the proposed approach over other previous approaches is that other approaches deal only with probability updating.

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