scholarly journals Extracting accurate location information from a highly inaccurate traffic accident dataset: A methodology based on a string matching technique

2016 ◽  
Vol 68 ◽  
pp. 185-193 ◽  
Author(s):  
Mario Miler ◽  
Filip Todić ◽  
Marko Ševrović
1996 ◽  
Vol 39 (3) ◽  
pp. 1197-1202 ◽  
Author(s):  
A. Ghazanfari ◽  
J. Irudayaraj

2016 ◽  
Vol 18 (3) ◽  
pp. 339-350 ◽  
Author(s):  
Liping Zhao ◽  
Tao Lin ◽  
Kailun Zhou ◽  
Shuhui Wang ◽  
Xianyi Chen

2021 ◽  
Vol 6 (1) ◽  
pp. 88
Author(s):  
Muhamad Arief Yulianto ◽  
Nurhasanah Nurhasanah

The String-matching technique is part of the similarity technique. This technique can detect the similarity level of the text. The Rabin-Karp is an algorithm of string-matching type. The Rabin-Karp is capable of multiple patterns searching but does not match a single pattern. The Jaro-Winkler Distance algorithm can find strings within approximate string matching. This algorithm is very suitable and gives the best results on the matching of two short strings. This study aims to overcome the shortcomings of the Rabin-Karp algorithm in the single pattern search process by combining the Jaro-Winkler and Rabin-Karp algorithm methods. The merging process started from pre-processing and forming the K-Gram data. Then, it was followed by the calculation of the hash value for each K-Gram by the Rabin-Karp algorithm. The process of finding the same hash score and calculating the percentage level of data similarity used the Jaro-Winkler algorithm. The test was done by comparing words, sentences, and journal abstracts that have been rearranged. The average percentage of the test results for the similarity level of words in the combination algorithm has increased. In contrast, the results of the percentage test for the level of similarity of sentences and journal abstracts have decreased. The experimental results showed that the combination of the Jaro-Winkler algorithm on the Rabin-Karp algorithm can improve the similarity of text accuracy.


2009 ◽  
Vol 2009 ◽  
pp. 1-4
Author(s):  
Jia Gu ◽  
Rolf Wolters ◽  
Ulf Gustafsson

Temporal matching is applied in the frame of the formation of high-level entities in remote-controlled robotic surgery. The objective is to track tumor boundaries over time to improve the segmentation stage in each image of the sequence to facilitate the tracking and localization of the tumor. It makes use of an attributed string matching technique to find the correspondence between tumor boundaries over time. Relationships are then exploited to reconstitute the tumor boundaries and remove the inconsistencies coming from the detection errors. Input data are free form shapes of different length representing the tumor boundary, extracted at a previous stage.


Sign in / Sign up

Export Citation Format

Share Document