Freight Volume Prediction of Coal Transportation from Xinjiang Based on Grey-Markov Chain Method

ICLEM 2010 ◽  
2010 ◽  
Author(s):  
Yang Yu ◽  
Wen Du ◽  
Lin Wang
Keyword(s):  
Markov Chain ◽  
Coal Transportation ◽  
Markov Chain Method ◽  
Freight Volume

UGLEO: A WEB BASED INTELLIGENCE CHATBOT FOR STUDENT ADMISSION PORTAL USING MEGAHAL STYLE

2018 ◽  
Vol 23 (3) ◽  
pp. 175-191
Author(s):  
Anneke Annassia Putri Siswadi ◽  
Avinanta Tarigan
Keyword(s):  
Markov Chain ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Information Need ◽  
Web Based ◽  
Markov Chain Method ◽  
Information Center

To fulfill the prospective student's information need about student admission, Gunadarma University has already many kinds of services which are time limited, such as website, book, registration place, Media Information Center, and Question Answering’s website (UG-Pedia). It needs a service that can serve them anytime and anywhere. Therefore, this research is developing the UGLeo as a web based QA intelligence chatbot application for Gunadarma University's student admission portal. UGLeo is developed by MegaHal style which implements the Markov Chain method. In this research, there are some modifications in MegaHal style, those modifications are the structure of natural language processing and the structure of database. The accuracy of UGLeo reply is 65%. However, to increase the accuracy there are some improvements to be applied in UGLeo system, both improvement in natural language processing and improvement in MegaHal style.

On the Markov Chain Simulation Method for Uniform Combinatorial Distributions and Simulated Annealing

1987 ◽  
Vol 1 (1) ◽  
pp. 33-46 ◽  
Author(s):  
David Aldous
Keyword(s):  
Markov Chain ◽  
Simulated Annealing ◽  
Simulated Annealing Algorithm ◽  
Simulation Method ◽  
Careful Analysis ◽  
Eigenvalue Estimate ◽  
Uniform Distributions ◽  
Markov Chain Method ◽  
Annealing Algorithm ◽  
Second Largest Eigenvalue

Uniform distributions on complicated combinatorial sets can be simulated by the Markov chain method. A condition is given for the simulations to be accurate in polynomial time. Similar analysis of the simulated annealing algorithm remains an open problem. The argument relies on a recent eigenvalue estimate of Alon [4]; the only new mathematical ingredient is a careful analysis of how the accuracy of sample averages of a Markov chain is related to the second-largest eigenvalue.

scholarly journals Implementation of Markov Chain in Detecting Opportunities for Natural Disasters in Klaten (Case Study: Number of Floods, Landslides, and Hurricanes 2019-2020)

2021 ◽  
Vol 1 (2) ◽  
pp. 58-67
Author(s):  
Afdelia Novianti ◽  
Dina Tri Utari
Keyword(s):  
Markov Chain ◽  
Natural Disasters ◽  
Human Life ◽  
The Other ◽  
Future Period ◽  
The Past ◽  
Current Period ◽  
Markov Chain Method ◽  
The Government

Java Island is one of the areas that is very fertile and densely populated, but on the other hand, Java Island is also one of the areas that is most frequently hit by natural disasters, one of which is Klaten Regency. Natural disaster itself is an event that threatens and disrupts human life caused by nature. Some of the natural disasters that often occur simultaneously in Klaten Regency are floods, landslides, and hurricanes. These three disasters usually occur during the rainy season. This of course makes the government need to take action by seeing the large chance of a disaster occurring in order to optimize disaster management. Then research will be carried out that aims to determine the chances of natural disasters occurring in the next few years. Forecasting will be carried out using the Markov chain method, with this method the probability value of the future period can be estimated using the current period probability value based on the characteristics of the past period. So that the value of the steady state chance of floods and landslides in period 36 (December 2023) and hurricanes in period 15 (March 2022) with the chances of a disaster are 34.21%, 15.38%, and 73.53%, respectively.Received August 31, 2021Revised October 27, 2021Accepted November 11, 2021

MARKOV CHAIN METHOD FOR COMPUTING THE RELIABILITY OF HAMMOCK NETWORKS

2020 ◽  
pp. 1-18
Author(s):  
Marilena Jianu ◽  
Daniel Ciuiu ◽  
Leonard Dăuş ◽  
Mihail Jianu
Keyword(s):  
Markov Chain ◽  
Lower Bound ◽  
Relative Error ◽  
Fundamental Matrix ◽  
Approximation Methods ◽  
Arbitrary Length ◽  
Reliability Polynomial ◽  
Absorbing Markov Chain ◽  
Markov Chain Method ◽  
The Mean

In this paper, we develop a new method for evaluating the reliability polynomial of a hammock network. The method is based on a homogeneous absorbing Markov chain and provides the exact reliability for networks of width less than 5 and arbitrary length. Moreover, it produces a lower bound for the reliability polynomial for networks of width greater than or equal to 5. To investigate how sharp this lower bound is, we compare our method with other approximation methods and it proves to be the most accurate in terms of absolute as well as relative error. Using the fundamental matrix, we also calculate the average time to absorption, which provides the mean length of a network that is expected to work.

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