scholarly journals An Efficient Image Downsampling Technique using Genetic Algorithm and Digital Curvelet Transform

2019 ◽  
Vol 8 (6S) ◽  
pp. 1000-1007
Keyword(s):  
Genetic Algorithm ◽  
Curvelet Transform ◽  
Original Version ◽  
Digital Curve ◽  
The Look ◽  
Generic Algorithms

Propelled pictures are used everywhere and are definitely not hard to manage and change in view of the availability of various picture getting ready and adjusting programming. Repeat the image to a lesser extent and change the look of the image. This can be useful at times when the original version of the original will give you a slim version of the film. There are several methods of image downsampling. This sheet uses performance capabilities for a collage based on digital curve transfers and generic algorithms. Genetic Algorithm (GA) is attached by the Digital Curvelet Transform (DCT). Originally DCT The length of the map decreases by using. Using this reduced map, gateways and entry worth are coordinated by the utilization of hereditary estimation. From the appraisal of results, it will when all is said in done be picked that the proposed method is quick and exact.

GENETIC ALGORITHMS FOR POLYGONAL APPROXIMATION OF DIGITAL CURVES

1999 ◽  
Vol 13 (07) ◽  
pp. 1061-1082 ◽  
Author(s):  
PENG-YENG YIN
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Learning Strategy ◽  
Approximation Error ◽  
Experimental Results ◽  
Polygonal Approximation ◽  
The Third ◽  
Digital Curve

In this paper, three polygonal approximation approaches using genetic algorithms are proposed. The first approach approximates the digital curve by minimizing the number of sides of the polygon and the approximation error should be less than a prespecified tolerance value. The second approach minimizes the approximation error by searching for a polygon with a given number of sides. The third approach, which is more practical, determines the approximating polygon automatically without any given condition. Moreover, a learning strategy for each of the proposed genetic algorithm is presented to improve the results. The experimental results show that the proposed approaches have better performances than those of existing methods.

scholarly journals Fraud Detection of Credit Card using Data Mining Techniques

2019 ◽  
Vol 8 (12) ◽  
pp. 4410-4413
Keyword(s):  
Neural Network ◽  
Data Mining ◽  
Genetic Algorithm ◽  
Credit Card ◽  
Hidden Markov ◽  
Fraud Detection ◽  
Network Clustering ◽  
Transaction Data ◽  
Using Data ◽  
The Look

The handling of credit card for online and systematic purchase is booming and scam associated with it. An industry of fraud detection where cumulative rise can have huge perk for banks and client. Numerous stylish techniques like data mining, genetic programming, neural network etc. are used in identify fraudulent transaction. In online transaction, Data mining acquire indispensable aspect in discovery of credit card counterfeit. This paper uses gradient boosted trees, neural network, clustering technique and genetic algorithm and hidden markov model for achieving upshot of the fraudulent transaction. These all model are emerging in identifying various credit card fraudulent detection. The indispensable aims to expose the fraudulent transaction and to corroborate test data for further use. This paper presents the look over techniques and pinpoint the top fraud cases.

scholarly journals Comparative Evaluation of Inversion Approaches of the Radiative Transfer Model for Estimation of Crop Biophysical Parameters

2015 ◽  
Vol 29 (2) ◽  
pp. 201-212 ◽  
Author(s):  
Nilimesh Mridha ◽  
Rabi N. Sahoo ◽  
Vinay K. Sehgal ◽  
Gopal Krishna ◽  
Sourabh Pargal ◽  
...  
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Chlorophyll Content ◽  
Estimation Accuracy ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Area Index ◽  
Leaf Chlorophyll ◽  
Look Up Table ◽  
The Look

Abstract The inversion of canopy reflectance models is widely used for the retrieval of vegetation properties from remote sensing. This study evaluates the retrieval of soybean biophysical variables of leaf area index, leaf chlorophyll content, canopy chlorophyll content, and equivalent leaf water thickness from proximal reflectance data integrated broadbands corresponding to moderate resolution imaging spectroradiometer, thematic mapper, and linear imaging self scanning sensors through inversion of the canopy radiative transfer model, PROSAIL. Three different inversion approaches namely the look-up table, genetic algorithm, and artificial neural network were used and performances were evaluated. Application of the genetic algorithm for crop parameter retrieval is a new attempt among the variety of optimization problems in remote sensing which have been successfully demonstrated in the present study. Its performance was as good as that of the look-up table approach and the artificial neural network was a poor performer. The general order of estimation accuracy for parameters irrespective of inversion approaches was leaf area index > canopy chlorophyll content > leaf chlorophyll content > equivalent leaf water thickness. Performance of inversion was comparable for broadband reflectances of all three sensors in the optical region with insignificant differences in estimation accuracy among them.

Behaviors of Composite Circular Struts in Space Environment: Analyses and Experiments

2004 ◽  
Vol 261-263 ◽  
pp. 797-802
Author(s):  
Chul Kim ◽  
Jong Heun Lee ◽  
J.H. Kim ◽  
Hoon Sang Choi
Keyword(s):  
Genetic Algorithm ◽  
Finite Element ◽  
Composite Materials ◽  
Optimal Design ◽  
Thermal Analyses ◽  
Space Environment ◽  
Stacking Sequence ◽  
Finite Element Analyses ◽  
Axial Deformation ◽  
Generic Algorithms

The optimal stacking sequence and wall thickness of the composite strut tubes were determined to minimize thermal strains during orbital operation using generic algorithms and finite element analyses. From the results of previous thermal analyses of composite struts with various stacking sequences, the axial deformation is a matter of prime importance. For this reason, the optimization focuses to minimize the axial strains. The balanced and symmetric stacking sequences are used to minimize the radial and the twisting deformations. The genetic algorithm is known to be very effective for the discrete optimization such as stacking sequences of composite materials. As a result, the thermal deformations of the strut with an optimal stacking sequence are almost zero. The optimal strut tube consists of 6 plies and the weight of a composite strut is 22.4% that of aluminum strut. Finite element analyses showed that the optimal design of composite strut tubes withstood combined launch loads without buckling and failure. To validate the analyses, four composite struts were fabricated and their thermal strains were measured under the temperature increase of 100°C. The thermal and vibration experiments showed excellent correlations with analytical results.

A GENETIC ALGORITHM-BASED APPROACH FOR DETECTION OF SIGNIFICANT VERTICES FOR POLYGONAL APPROXIMATION OF DIGITAL CURVES

2004 ◽  
Vol 04 (02) ◽  
pp. 223-239 ◽  
Author(s):  
BISWAJIT SARKAR ◽  
LOKENDRA KUMAR SINGH ◽  
DEBRANJAN SARKAR
Keyword(s):  
Genetic Algorithm ◽  
A Priori ◽  
Approximation Error ◽  
Compact Representation ◽  
Polygonal Approximation ◽  
Original Curve ◽  
Planar Curve ◽  
Digital Curve ◽  
Digital Planar Curve ◽  
Curve Form

A polygonal approximation captures the essential features of a digital planar curve and yields a compact representation. Those points of the digital curve that carry vital information about the shape of the curve form the vertices of the approximating polygon and are called significant vertices. In this paper, we present a genetic algorithm-based approach to locate a specified number of significant points, such that the approximation error between the original curve and its polygonal version obtained by joining the adjacent significant points is minimized. By using a priori knowledge about the shape of the curve we confine our search to only those points of the curve that have the potential of qualifying as significant points. We also incorporate chromosome differentiation to improve upon the effectiveness of the search in arriving at a near-optimal polygonal approximation. Finally, we show that the proposed method performs remarkably well when evaluated in terms of the metrics available for assessing the goodness of a polygonal approximation algorithm.

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