Bayesian network model constructed through a social network chain ( Varshney, Kumar, & Gupta, 2017 ), a maximum-likelihood adaptive neural system to predict microblog topics ( Gromov & Konev, 2017 ), and the method of identifying rumors in the public health field based on LDA ( Ye, Li, Yang, Lee, & Wu, 2018 ). Some scholars have constructed the “conductivity” attribute of topics, introduced the physical concept to microblog information prediction, and predicted whether the topic can be achieved by calculating the second derivative of conductance ( Bora, Singh, Sen
regarded as major factors in determining the quality of studies. There are multiple categories of statistical methods, such as inferential statistical methods, predictive statistical methods, parametric statistical methods, and nonparametric statistical methods. Each category contains a lot of statistical approaches. For instance, ANOVA test is an inferential statistical method, while Pearson’s correlation is a predictive statistical method. These various statistical methods have different characteristics and are used differently in studies. Therefore, it is meaningful
Studies of cultural heritages are invaluable in many disciplines including archeology, history, anthropology, linguistics, and religious studies. Among tangible cultural heritage (i.e., those with physical presence), many are non-movable such as monuments, architecture, and archeological sites. To observe non-movable heritages, people have to travel to the heritage site. Moreover, most non-movable heritages are exposed to the natural environment with limited protection, suffering from deformation and deterioration caused by weather or other
were the main transition patterns analyzed in this study. Because business markets are competitive, assuming proper control of patent characteristics related to transition patterns is desired by diverse types of stakeholders to potentially facilitate monitoring and predicting rivals’ transition patterns at early stages and to provide valuable assistance in adapting strategies ( Aharonson & Schilling, 2016 ).
A series of statistical testing procedures was used to conduct a preliminary study to gain insight into the significance and directionality of technology
Maria Esteva, Ramona L. Walls, Andrew B. Magill, Weijia Xu, Ruizhu Huang, James Carson and Jawon Song
ontological efforts to describe biological data ( Smith et al., 2007 ).
Our solution was to devise a generic data model to accommodate all our test cases and many more. Our generic data model ( Fig. 1A) has three key components: 1) processual entities, which include things such as collecting specimens, carrying out assays, and analyzing data; 2) material entities such as physical specimens, reagents, and probes; and 3) data entities, including both individual data objects and datasets. We include a project entity as the umbrella under which the other entities are grouped
Will R. Thomas, Benjamin Galewsky, Sandeep Puthanveetil Satheesan, Gregory Jansen, Richard Marciano, Shannon Bradley, Jong Lee, Luigi Marini and Kenton McHenry
folder number for the underlying physical record, filename and path assigned at the time of capture, SHA256 fixity information, and format information captured by the identity program in the ImageMagick suite.
Storage Pool. C = Cassandra
Expanded Storage Pool.
We rely on Apache Spark ( Zaharia et al., 2016 ) to distribute the training of the machine-learning models. Training and testing data are stored in Cassandra, as Spark integrates with it as a persistence layer. Although Spark has an ML library (Meng, et al., 2016), the
. Esfandiari et al. [ 11 ] gave a comprehensive review of the studies in this area. According to their summarization, most studies regarded diagnosis prediction as a classification task. For example, Yeh et al. [ 12 ] used the history of patients’ diseases, blood test results, and physical examination results as the features and used trained classifiers to predict the probability of getting a cerebrovascular disease. Besides, other studies tried to predict the diagnosis with regression methods, clustering methods, association rules, or hybrid systems [ 11 ]. For example
Liang Hong, Mengqi Luo, Ruixue Wang, Peixin Lu, Wei Lu and Long Lu
physical health data based on artificial ant colony optimization. This method is determined through testing to be an effective and efficient approach to clustering health and medical data for further analysis.
Paul and Hoque (2010) proposed to use the background knowledge of medical domain in the clustering process to predict the likelihood of diseases. The developed algorithm can handle both continuous and discrete data and perform clustering based on anticipated likelihood attributes with core attributes of disease in data point. In this paper, its effectiveness has
information sensitivity based on general information types, it is more appropriate to measure information sensitivity by taking users’ perception of sensitivity into account. Some scholars define information sensitivity as the perceived intimacy level of information ( Lwin, Wirtz, & Williams, 2007 ). More intimate information is perceived as riskier to disclose because it may lead to potential losses, including psychological (e.g., loss of self-esteem), physical (e.g., loss of health), and material (e.g., loss of property and assets) aspects ( Moon, 2000 ). Therefore, this