Generation of Replacement Vehicle Speed Cycles Based on Extensive Customer Data by Means of Markov Models and Threshold Accepting

2017 ◽  
Vol 6 (1) ◽  
pp. 165-173 ◽  
Author(s):  
Roman Liessner ◽  
Ansgar Dietermann ◽  
Bernard Bäker ◽  
Klaus Lüpkes
Keyword(s):  
Markov Models ◽  
Vehicle Speed ◽  
Threshold Accepting ◽  
Customer Data

Transition (markov) models for the analysis of survival times in clinical research

1999 ◽  
Vol 28 (1) ◽  
pp. 165-176
Author(s):  
Sati Mazumdar ◽  
Kenneth Liu ◽  
Sang Ahnn ◽  
Patricia R. Houck ◽  
Charles F. Reynolds
Keyword(s):  
Clinical Research ◽  
Markov Models ◽  
Survival Times

Markov models—Markov chains

2019 ◽  
Vol 16 (8) ◽  
pp. 663-664 ◽  
Author(s):  
Jasleen K. Grewal ◽  
Martin Krzywinski ◽  
Naomi Altman
Keyword(s):  
Markov Chains ◽  
Markov Models

Estimating Personality Impression from Speech Record Using Hidden Markov Models

2015 ◽  
Vol 135 (12) ◽  
pp. 1517-1523 ◽  
Author(s):  
Yicheng Jin ◽  
Takuto Sakuma ◽  
Shohei Kato ◽  
Tsutomu Kunitachi
Keyword(s):  
Hidden Markov Models ◽  
Markov Models ◽  
Hidden Markov

Hidden Markov Processes

2014 ◽  
Author(s):  
M. Vidyasagar
Keyword(s):  
Hidden Markov Models ◽  
Markov Processes ◽  
Viterbi Algorithm ◽  
Markov Models ◽  
Hidden Markov ◽  
Local Alignment ◽  
Biological Applications ◽  
Standard Material ◽  
Hidden Markov Processes ◽  
Genomics And Proteomics

This book explores important aspects of Markov and hidden Markov processes and the applications of these ideas to various problems in computational biology. It starts from first principles, so that no previous knowledge of probability is necessary. However, the work is rigorous and mathematical, making it useful to engineers and mathematicians, even those not interested in biological applications. A range of exercises is provided, including drills to familiarize the reader with concepts and more advanced problems that require deep thinking about the theory. Biological applications are taken from post-genomic biology, especially genomics and proteomics. The topics examined include standard material such as the Perron–Frobenius theorem, transient and recurrent states, hitting probabilities and hitting times, maximum likelihood estimation, the Viterbi algorithm, and the Baum–Welch algorithm. The book contains discussions of extremely useful topics not usually seen at the basic level, such as ergodicity of Markov processes, Markov Chain Monte Carlo (MCMC), information theory, and large deviation theory for both i.i.d and Markov processes. It also presents state-of-the-art realization theory for hidden Markov models. Among biological applications, it offers an in-depth look at the BLAST (Basic Local Alignment Search Technique) algorithm, including a comprehensive explanation of the underlying theory. Other applications such as profile hidden Markov models are also explored.

APPLICATION OF MULTI-STATE SEMI-MARKOV MODELS ON HIV/AIDS DISEASE PROGRESSION

2018 ◽  
Vol 7 (3) ◽  
pp. 30
Author(s):  
KALAYU MENGESHA SOLOMON ◽  
ALEMU GEBREMEDHN GEBREGEWERGIS ◽  
FEREDE TILAHUN ◽  
ATSMEGIORGIS CHERU ◽  
◽  
...  
Keyword(s):  
Disease Progression ◽  
Markov Models ◽  
Hiv Aids

Penerapan Metode Klasifikasi Decision Tree dan Algoritma C4.5 dalam Memprediksi Kriteria Nasabah Kredit Mega Auto Finance

2020 ◽  
Vol 7 (2) ◽  
pp. 200
Author(s):  
Puji Santoso ◽  
Rudy Setiawan
Keyword(s):  
Data Mining ◽  
Decision Tree ◽  
Microsoft Excel ◽  
Customer Data ◽  
Data Mining Techniques ◽  
C4.5 Algorithm ◽  
Marketing Costs ◽  
Excel Format ◽  
Data Mining Application

One of the tasks in the field of marketing finance is to analyze customer data to find out which customers have the potential to do credit again. The method used to analyze customer data is by classifying all customers who have completed their credit installments into marketing targets, so this method causes high operational marketing costs. Therefore this research was conducted to help solve the above problems by designing a data mining application that serves to predict the criteria of credit customers with the potential to lend (credit) to Mega Auto Finance. The Mega Auto finance Fund Section located in Kotim Regency is a place chosen by researchers as a case study, assuming the Mega Auto finance Fund Section has experienced the same problems as described above. Data mining techniques that are applied to the application built is a classification while the classification method used is the Decision Tree (decision tree). While the algorithm used as a decision tree forming algorithm is the C4.5 Algorithm. The data processed in this study is the installment data of Mega Auto finance loan customers in July 2018 in Microsoft Excel format. The results of this study are an application that can facilitate the Mega Auto finance Funds Section in obtaining credit marketing targets in the future

ANALYSIS OF VEHICLE SPEED ON THE BASIS OF IMAGE PROCESSING

2018 ◽  
Author(s):  
И. Гафиев ◽  
I. Gafiev ◽  
С. Ляшева ◽  
S. Lyasheva ◽  
Михаил Шлеймович ◽  
...  
Keyword(s):  
Image Processing ◽  
Vehicle Speed
Sign in / Sign up

Export Citation Format

Share Document