Prediction Model of Sports Results Base on Knowledge Discovery in Data-Base

Air Pollution and Hospital Admissions: Looking for Spatial-Time Association by Technologies of Knowledge Discovery and Delivery (KDD) in Data Base.

Epidemiology ◽

10.1097/00001648-200611001-01363 ◽

2006 ◽

Vol 17 (Suppl) ◽

pp. S507 ◽

Cited By ~ 3

Author(s):

A Uva ◽

M A Vigotti ◽

A M Romanelli ◽

M Raciti ◽

M A Protti ◽

...

Keyword(s):

Air Pollution ◽

Knowledge Discovery ◽

Data Base ◽

Hospital Admissions

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Full-Scale Pipe Strain Test Quality and Safety Factor Determination for Strain-Based Engineering Critical Assessment

Volume 2: Pipeline Safety Management Systems; Project Management, Design, Construction and Environmental Issues; Strain Based Design; Risk and Reliability; Northern Offshore and Production Pipelines ◽

10.1115/ipc2016-64191 ◽

2016 ◽

Cited By ~ 1

Author(s):

Douglas P. Fairchild ◽

Justin M. Crapps ◽

Wentao Cheng ◽

Huang Tang ◽

Svetlana Shafrova

Keyword(s):

Prediction Model ◽

Data Base ◽

Model Validation ◽

Safety Factor ◽

Accuracy Assessment ◽

Critical Assessment ◽

Full Scale ◽

Lessons Learned ◽

Test Quality ◽

New Model

Generating a tensile strain capacity (TSC) prediction model is a difficult challenge in applied mechanics. Because current models are relatively new and extensive strain-based design (SBD) pipeline service experience does not exist, rigorous model validation using full-scale tests (FSTs) is paramount. The lessons learned from 159 FSTs were presented previously and the data base has grown to 173 tests. This data base is used to assess the accuracy of a relatively new TSC prediction model. The new model simulates a single, surface breaking weld flaw; however, some of the FSTs contained interacting or embedded flaws or unintentional weld defects, while others failed by brittle fracture, and still others experienced welding problems rendering them unsuitable for model validation. Of 173 tests, a smaller number (122, 101, or 89 depending on the goal) is used for comparison to the new model. This paper describes (1) the importance of reliable FSTs, (2) how the 173 tests were judged for suitability in model accuracy assessment, and (3) the use of the FST data to develop a safety factor for strain-based engineering critical assessment (SBECA). The safety factor is generated from a 95% upper confidence limit on the ratio of predicted-to-measured TSC. The safety factor is 1.88. Using the new model and this safety factor, a TSC prediction equation is provided for use in SBECA. The practical meaning of this is that if either TSC or tolerable defect size is calculated using the new model, then the probability of being non-conservative is estimated to be 5%.

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A BERT-Based Approach for Extracting Prerequisite Relations among Wikipedia Concepts

Mathematical Problems in Engineering ◽

10.1155/2021/3510402 ◽

2021 ◽

Vol 2021 ◽

pp. 1-8

Author(s):

Youheng Bai ◽

Yan Zhang ◽

Kui Xiao ◽

Yuanyuan Lou ◽

Kai Sun

Keyword(s):

Prediction Model ◽

Knowledge Discovery ◽

State Of The Art ◽

The Other ◽

Basic Knowledge ◽

Common Task ◽

Concept Pair ◽

Relation Prediction ◽

Multiple Languages ◽

Better Than

Concept prerequisite relation prediction is a common task in the field of knowledge discovery. Concept prerequisite relations can be used to rank learning resources and help learners plan their learning paths. As the largest Internet encyclopedia, Wikipedia is composed of many articles edited in multiple languages. Basic knowledge concepts in a variety of subjects can be found on Wikipedia. Although there are many knowledge concepts in each field, the prerequisite relations between them are not clear. When we browse pages in an area on Wikipedia, we do not know which page to start. In this paper, we propose a BERT-based Wikipedia concept prerequisite relation prediction model. First, we created two types of concept pair features, one is based on BERT sentence embedding and the other is based on the attributes of Wikipedia articles. Then, we use these two types of concept pair features to predict the prerequisite relations between two concepts. Experimental results show that our proposed method performs better than state-of-the-art methods for English and Chinese datasets.

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