scholarly journals The Reputation Evaluation Based on Optimized Hidden Markov Model in E-Commerce

2013 ◽  
Vol 2013 ◽  
pp. 1-11 ◽  
Author(s):  
Liu Chang ◽  
Yacine Ouzrout ◽  
Antoine Nongaillard ◽  
Abdelaziz Bouras ◽  
Zhou Jiliu
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Hidden Markov ◽  
Optimal Solution ◽  
Evaluation Framework ◽  
Learning Ability ◽  
Model Parameters ◽  
Analysis Tool ◽  
Practical Applications ◽  
Evaluation Mechanism

Nowadays, a large number of reputation systems have been deployed in practical applications or investigated in the literature to protect buyers from deception and malicious behaviors in online transactions. As an efficient Bayesian analysis tool, Hidden Markov Model (HMM) has been used into e-commerce to describe the dynamic behavior of sellers. Traditional solutions adopt Baum-Welch algorithm to train model parameters which is unstable due to its inability to find a globally optimal solution. Consequently, this paper presents a reputation evaluation mechanism based on the optimized Hidden Markov Model, which is called PSOHMM. The algorithm takes full advantage of the search mechanism in Particle Swarm Optimization (PSO) algorithm to strengthen the learning ability of HMM and PSO has been modified to guarantee interval and normalization constraints in HMM. Furthermore, a simplified reputation evaluation framework based on HMM is developed and applied to analyze the specific behaviors of sellers. The simulation experiments demonstrate that the proposed PSOHMM has better performance to search optimal model parameters than BWHMM, has faster convergence speed, and is more stable than BWHMM. Compared with Average and Beta reputation evaluation mechanism, PSOHMM can reflect the behavior changes of sellers more quickly in e-commerce systems.

scholarly journals Activity-Integrated Hidden Markov Model to Predict Calving Time

Animals ◽  
2021 ◽  
Vol 11 (2) ◽  
pp. 385
Author(s):  
Kosuke Sumi ◽  
Swe Zar Maw ◽  
Thi Thi Zin ◽  
Pyke Tin ◽  
Ikuo Kobayashi ◽  
...  
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
High Frequency ◽  
Hidden Markov ◽  
Dairy Farm ◽  
Integrated Approach ◽  
Production Cycle ◽  
Practical Applications ◽  
Standing Up ◽  
Behavioral Activities

Accurately predicting when calving will occur can provide great value in managing a dairy farm since it provides personnel with the ability to determine whether assistance is necessary. Not providing such assistance when necessary could prolong the calving process, negatively affecting the health of both mother cow and calf. Such prolongation could lead to multiple illnesses. Calving is one of the most critical situations for cows during the production cycle. A precise video-monitoring system for cows can provide early detection of difficulties or health problems, and facilitates timely and appropriate human intervention. In this paper, we propose an integrated approach for predicting when calving will occur by combining behavioral activities extracted from recorded video sequences with a Hidden Markov Model. Specifically, two sub-systems comprise our proposed system: (i) Behaviors extraction such as lying, standing, number of changing positions between lying down and standing up, and other significant activities, such as holding up the tail, and turning the head to the side; and, (ii) using an integrated Hidden Markov Model to predict when calving will occur. The experiments using our proposed system were conducted at a large dairy farm in Oita Prefecture in Japan. Experimental results show that the proposed method has promise in practical applications. In particular, we found that the high frequency of posture changes has played a central role in accurately predicting the time of calving.

On-line Estimation of Hidden Markov Model Parameters

2000 ◽  
pp. 155-169 ◽  
Author(s):  
Jun Mizuno ◽  
Tatsuya Watanabe ◽  
Kazuya Ueki ◽  
Kazuyuki Amano ◽  
Eiji Takimoto ◽  
...  
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Hidden Markov ◽  
Model Parameters ◽  
On Line

scholarly journals Detection of contributing object to driving operations based on hidden Markov model

2019 ◽  
Vol 16 (5) ◽  
pp. 172988141987679
Author(s):  
Kohjiro Hashimoto ◽  
Tetsuyasu Yamada ◽  
Takeshi Tsuchiya ◽  
Kae Doki ◽  
Yuki Funabora ◽  
...  
Keyword(s):  
Time Series ◽  
Markov Model ◽  
Hidden Markov Model ◽  
Cognitive Performance ◽  
Traffic Accidents ◽  
Hidden Markov ◽  
Model Parameters ◽  
Detection Accuracy ◽  
Cognitive Support ◽  
Object Based

With increase in the number of elderly people in the Japanese society, traffic accidents caused by elderly driver is considered problematic. The primary factor of the traffic accidents is a reduction in their driving cognitive performance. Therefore, a system that supports the cognitive performance of drivers can greatly contribute in preventing accidents. Recently, the development of devices for visually providing information, such as smart glasses or head up display, is in progress. These devices can provide more effective supporting information for cognitive performance. In this article, we focus on the selection problem of information to be presented for drivers to realize the cognitive support system. It has been reported that the presentation of excessive information to a driver reduces the judgment ability of the driver and makes the information less trustworthy. Thus, indiscriminate presentation of information in the vision of the driver is not an effective cognitive support. Therefore, a mechanism for determining the information to be presented to the driver based on the current driving situation is required. In this study, the object that contributes to execution of avoidance driving operation is regarded as the object that drivers must recognize and present for drivers. This object is called as contributing object. In this article, we propose a method that selects contributing objects among the appeared objects on the current driving scene. The proposed method expresses the relation between the time series change of an appeared object and avoidance operation of the driver by a mathematical model. This model can predict execution timing of avoidance driving operation and estimate contributing object based on the prediction result of driving operation. This model named as contributing model consisted of multi-hidden Markov models. Hidden Markov model is time series probabilistic model with high readability. This is because that model parameters express the probabilistic distribution and its statistics. Therefore, the characteristics of contributing model are that it enables the designer to understand the basis for the output decision. In this article, we evaluated detection accuracy of contributing object based on the proposed method, and readability of contributing model through several experiments. According to the results of these experiments, high detection accuracy of contributing object was confirmed. Moreover, it was confirmed that the basis of detected contributing object judgment can be understood from contributing model.

scholarly journals Pairwise correlations in layered close-packed structures

2015 ◽  
Vol 71 (4) ◽  
pp. 423-443 ◽  
Author(s):  
P. M. Riechers ◽  
D. P. Varn ◽  
J. P. Crutchfield
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Correlation Functions ◽  
Hidden Markov ◽  
Model Parameters ◽  
Pairwise Correlation ◽  
Pairwise Correlations ◽  
Analytical Expressions

Given a description of the stacking statistics of layered close-packed structures in the form of a hidden Markov model, analytical expressions are developed for the pairwise correlation functions between the layers. These may be calculated analytically as explicit functions of model parameters or the expressions may be used as a fast, accurate and efficient way to obtain numerical values. Several examples are presented, finding agreement with previous work as well as deriving new relations.

Confidence intervals for hidden Markov model parameters

2000 ◽  
Vol 53 (2) ◽  
pp. 317-327 ◽  
Author(s):  
Ingmar Visser ◽  
Maartje E. J. Raijmakers ◽  
Peter C. M. Molenaar
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Confidence Intervals ◽  
Hidden Markov ◽  
Model Parameters

Exploring More Representative States of Hidden Markov Model in Optical Character Recognition: A Clustering-Based Model Pre-Training Approach

2015 ◽  
Vol 29 (03) ◽  
pp. 1550014 ◽  
Author(s):  
Zhiwei Jiang ◽  
Xiaoqing Ding ◽  
Liangrui Peng ◽  
Changsong Liu
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Character Recognition ◽  
Optical Character Recognition ◽  
Hidden Markov ◽  
Recognition Performance ◽  
Optimization Methods ◽  
Model Parameters ◽  
Model Structure ◽  
Training Approach

Hidden Markov Model (HMM) is an effective method to describe sequential signals in many applications. As to model estimation issue, common training algorithm only focuses on the optimization of model parameters. However, model structure influences system performance as well. Although some structure optimization methods are proposed, they are usually implemented as an independent module before parameter optimization. In this paper, the clustering feature of states in HMM is discussed through comparing the mechanism of Quadratic Discriminant Function (QDF) classifier and HMM. Then, through the clustering effect of Viterbi training and Baum–Welch training, a novel clustering-based model pre-training approach is proposed. It can optimize model parameters and model structure by turns, until the representative states of all models are explored. Finally, the proposed approach is evaluated on two typical OCR applications, printed and handwritten Arabic text line recognition. And it is compared with some other optimization methods. The improvement of character recognition performance proves the proposed approach can make more precise state allocation. And the representative states are benefit to HMM decoding.

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