Context Similarity Measurement Based on Genetic Algorithm for Improved Recommendations

2017 ◽  
pp. 11-29
Author(s):  
Mohammed Wasid ◽  
Rashid Ali
Keyword(s):  
Genetic Algorithm ◽  
Similarity Measurement ◽  
Context Similarity

MATLAB code to estimate landslide volume from single remote sensed image using genetic algorithm and imagery similarity measurement

2014 ◽  
Vol 70 ◽  
pp. 238-247 ◽  
Author(s):  
Ting-Shiuan Wang ◽  
Teng-To Yu ◽  
Shing-Tsz Lee ◽  
Wen-Fei Peng ◽  
Wei-Ling Lin ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Similarity Measurement ◽  
Matlab Code ◽  
Landslide Volume

The ground state energy of the ±J spin glass from the genetic algorithm

1994 ◽  
Vol 4 (9) ◽  
pp. 1281-1285 ◽  
Author(s):  
P. Sutton ◽  
D. L. Hunter ◽  
N. Jan
Keyword(s):  
Genetic Algorithm ◽  
Spin Glass ◽  
Ground State Energy ◽  
Ground State ◽  
State Energy

A Comparative Study of Ant and Genetic Algorithms in Digital Mammography

2018 ◽  
pp. 194-200
Author(s):  
J. Magelin Mary ◽  
Chitra K. ◽  
Y. Arockia Suganthi
Keyword(s):  
Genetic Algorithm ◽  
Image Processing ◽  
Medical Image ◽  
Medical Image Processing ◽  
Processing Technique ◽  
Input Image ◽  
Image Processing Technique ◽  
Computational Time ◽  
Image Mining ◽  
The Individual

Image processing technique in general, involves the application of signal processing on the input image for isolating the individual color plane of an image. It plays an important role in the image analysis and computer version. This paper compares the efficiency of two approaches in the area of finding breast cancer in medical image processing. The fundamental target is to apply an image mining in the area of medical image handling utilizing grouping guideline created by genetic algorithm. The parameter using extracted border, the border pixels are considered as population strings to genetic algorithm and Ant Colony Optimization, to find out the optimum value from the border pixels. We likewise look at cost of ACO and GA also, endeavors to discover which one gives the better solution to identify an affected area in medical image based on computational time.

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