Morphometric patterns in Middle Triassic Neogondolella mombergensis (Conodonta), Fossil Hill, Nevada

1989 ◽  
Vol 63 (2) ◽  
pp. 233-245 ◽  
Author(s):  
Scott M. Ritter
Keyword(s):  
Time Series ◽  
Random Walk ◽  
Morphometric Analysis ◽  
Middle Triassic ◽  
Random Walk Model ◽  
Regional Correlation ◽  
Diverse Species ◽  
Individual Character ◽  
Morphological Stasis ◽  
Conodont Evolution

The Fossil Hill Member of the Prida Formation (Fossil Hill, Nevada) yields one of the most continuous records of Middle Triassic conodont evolution currently known. Because of different taxonomic viewpoints, this record has been alternately interpreted to represent either morphological stasis or gradual, biostratigraphically significant morphogenesis. Univariate and multivariate morphometric analysis of 18 successive Neogondolella Pa element populations suggests that the majority of specimens at Fossil Hill (including N. constricta emend. sensu Nicora and Kovacs, 1984) belong to a single, morphologically diverse species, Neogondolella mombergensis (Tatge). Time series of individual character means from 18 stratigraphic horizons in the Fossil Hill display nondirectional morphologic trends for which a random walk model cannot be rejected. Time series of transformed multivariate means constitute biologs that may prove useful in regional correlation.

Time Series Model Identification in the Frame of Random Walk Model

2018 ◽  
Vol 37 (1) ◽  
pp. 1-17
Author(s):  
Cheolyong Park ◽  
Kim Tae Yoon ◽  
하정철 ◽  
김슬기 ◽  
Park, Jincheol
Keyword(s):  
Time Series ◽  
Random Walk ◽  
Model Identification ◽  
Time Series Model ◽  
Random Walk Model

A Multi-Label Classification Algorithm Based on Random Walk Model

2010 ◽  
Vol 33 (8) ◽  
pp. 1418-1426 ◽  
Author(s):  
Wei ZHENG ◽  
Chao-Kun WANG ◽  
Zhang LIU ◽  
Jian-Min WANG
Keyword(s):  
Random Walk ◽  
Classification Algorithm ◽  
Random Walk Model

Quartile histogram assessment of glioma malignancy using high b ‐value diffusion MRI with a continuous‐time random‐walk model

2021 ◽  
Vol 34 (4) ◽  
Author(s):  
M. Muge Karaman ◽  
Jiaxuan Zhang ◽  
Karen L. Xie ◽  
Wenzhen Zhu ◽  
Xiaohong Joe Zhou
Keyword(s):  
Random Walk ◽  
Continuous Time ◽  
Diffusion Mri ◽  
B Value ◽  
Continuous Time Random Walk ◽  
Random Walk Model ◽  
High B Value

The prediction model of personality in social networks by using data mining deep learning algorithm and random walk model

2020 ◽  
pp. 002072092093683
Author(s):  
Yu Zhu
Keyword(s):  
Data Mining ◽  
Deep Learning ◽  
Social Network ◽  
Random Walk ◽  
Prediction Method ◽  
Random Walk Model ◽  
Personality Characteristics ◽  
User Personality ◽  
Network Platform ◽  
Personality Prediction

The objective is to predict and analyze the behaviors of users in the social network platform by using the personality theory and computational technologies, thereby acquiring the personality characteristics of social network users more effectively. First, social network data are analyzed, which finds that the type of text data marks the majority. By using data mining technology, the raw data of numerous social network users can be obtained. Based on the random walk model, the data information of the text status of social network users is analyzed, and a user personality prediction method integrating multi-label learning is proposed. In addition, the online social network platform Weibo is taken as the research object. The blog information of Weibo users is obtained through crawler technology. Then, the users are labeled in accordance with personality characteristics. The Pearson correlation coefficient is used to evaluate the relation between the user personality characteristics and the user behavior characteristics of the Weibo users. The correlation between the network behaviors and personality characteristics of Weibo users is analyzed, and the scientificity of the prediction method is verified by the Big Five Model of Personality. By applying relevant technologies and algorithms of data mining and deep learning, the learning ability of neural networks on data characteristics can be improved. In terms of performance on analyzing text information of social network users, the user personality prediction method of integrated multi-label learning based on the random walk model has a large advantage. For the problem of personality prediction of social network users, through combining data mining technology and deep neural network technology in deep learning, the data processing results of social network user behaviors are more accurate.

Tracking method to random walk model based on orientation code matching

2008 ◽  
Author(s):  
Kazuhiro Kagoike ◽  
Satoru Takahashi ◽  
Hidenori Takauji ◽  
Shun'ichi Kaneko
Keyword(s):  
Random Walk ◽  
Random Walk Model ◽  
Tracking Method ◽  
Model Based
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