scholarly journals A Cross-domain Deceptive Opinion Detection by Genetic Algorithm

2016 ◽  
Author(s):  
Qiao-jing Tang ◽  
Wei-hua Li ◽  
Jing Zhao
Keyword(s):  
Genetic Algorithm ◽  
Cross Domain

A computational creativity approach from music to images

2021 ◽  
Vol 14 (2) ◽  
pp. 1-5
Author(s):  
Luís Aleixo H. Sofía Pinto ◽  
Nuno Correia
Keyword(s):  
Genetic Algorithm ◽  
Creative Process ◽  
Sound Art ◽  
Color Theory ◽  
Computational Creativity ◽  
Online Surveys ◽  
Art And Design ◽  
Cross Domain ◽  
Visual Characteristics ◽  
Aesthetic Preferences

Our system generates abstract images from music that serve as inspiration for the creative process. We developed one of many possible approaches for a cross-domain association between the musical and visual domains, by extracting features from MIDI music files and associating them to visual characteristics. The associations were led by the authors' aesthetic preferences and some experimentation. Three different approaches were pursued, two with direct or random associations and a third using a genetic algorithm that considers music and color theory while searching for better results. The resulting images were evaluated through online surveys, which confirmed that not only they were abstract, but also that there was a relationship with the music that served as the basis for the association process. Moreover, the majority of the participants ranked highest the images improved with the genetic algorithm. This newsletter contribution summarizes the full version of the article, which was presented at EvoMUSART 2021 (the 10th International Conference on Artificial Intelligence in Music, Sound, Art and Design).

scholarly journals Robustious Feature Selection Based Genetic Algorithm (RFS-GA) For Cross Domain Opinion Mining

2019 ◽  
Vol 8 (2) ◽  
pp. 6267-6279
Keyword(s):  
Genetic Algorithm ◽  
Feature Selection ◽  
Sentiment Analysis ◽  
Opinion Mining ◽  
Performance Metrics ◽  
Research Work ◽  
Features Selection ◽  
Cross Domain ◽  
Multiple Challenges ◽  
Day By Day

Day by day the requirement of information for processing the sentiment analysis is getting increased multiple times. For these kind of reasons, feature selection is utilized to detect the opinion among different reviews and comments. Sentiment analysis is becoming like phenomenon due to increase of social media’s popularity. Currently, significant advancements are shown in this research domain, but still multiple challenges are to be solved – i.e., sentiment analysis in cross domains. In this paper rumbustious feature selection based genetic algorithm is proposed to address the problem of analyzing the sentiments in cross domain. It performs classification based optimistic-class and pessimistic-class. The dataset used to this research work includes books, DVDs, gadgets and kitchen appliances. Initially the features selection is performed and opinion mining is performed by Genetic Algorithm. Benchmark performance metrics are selected for measuring the performance of proposed work against existing method. Results depict that the proposed work has better performance than that of the existing work as far as chosen performance metrics.

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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