Patent AU-2020286320-A1 (AU)
Title: Multi-granularity spark super trust fuzzy method applied to large-scale brain medical record segmentation
Inventor: DING WEIPING
IPC: G16H10/60
Filed: 2020-06-03
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(54) Title: MULTI-GRANULATION SPARK-BASED SUPER-TRUST FUZZY METHOD FOR LARGE-SCALE BRAIN MEDI
CAL RECORD SEGMENTATION 
-=(54)&TP t rrZr Fti n fl !-+tiSpark ~fl JMftM)t4 
AA BB CC DD AA Divide a large-scale brain medical record data attribute set into multi-granulation evolutionary 
SBB Design a multi-granulation Spark-based super-trust model to construct trust relations between 
Ittt~9 - - l1F7& U 
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vFl jJ~Tsuper elitists 
CC Adjustiamulti-granulation Spark-based super-trust center threshold 
H DID Execute supe elitist balance dynamicadustment strategytoenhance the optimal balanced 
~k~] cnsistencofmulti-granulationsubopulaions 
t~i~san. t~ F Store abrain medical recorddiagnosis caracteristic set on aSpark cloud platform 
Tonassess the brainmeica record attriutecharacteristic segmentation precision or not? 
EE FF GG GG Obtain the optimal large-scale 
brain medical record characteristic set 
HH Yes 
11 No 
1I) 
(57) Abstract: A multi-granulation Spark-based super-trust fuzzy method for large-scale brain medical record segmentation, compris
ing: first, segmenting a large-scale brain medical record data attribute set into different multi-granulation evolutionary subpopulations 
(Granu-populationi) on a Spark cloud platform; designing a multi-granulation Spark-based super-trust model to construct trust between 
different super elitists in multi-granulation populations; adjusting a multi-granulation center threshold, and dynamically updating the 
super elitists using a multi-granulation subpopulation balance adjustment strategy, performing global search segmentation and local 
refinement segmentation on large-scale brain medical records, wherein super elitists can collaboratively extract knowledge reduction 
subsets in respective regions; and finally, obtaining the optimal large-scale brain medical record segmentation characteristic set and 
storing same on the Spark cloud platform. By means of the present method, stable segmentation can be implemented on large-scale 
brain medical record knowledge reduction sets to provide important diagnostic basis for intelligent diagnosis and auxiliary treatment 
of brain diseases.  
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