Search Results - (( java implementation mining algorithm ) OR ( programming testing selection algorithm ))
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1
Direct approach for mining association rules from structured XML data
Published 2012“…The thesis also provides a two different implementation of the modified FLEX algorithm using a java based parsers and XQuery implementation. …”
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Thesis -
2
Study and Implementation of Data Mining in Urban Gardening
Published 2019“…Using the J48 tree algorithm implemented through WEKA API on a Java Servlet, data provided is processed to derive a health index of the plant, with the possible outcomes set to “Good,” “Okay”, or “Bad”. …”
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3
Scalable approach for mining association rules from structured XML data
Published 2009“…Many techniques have been proposed to tackle the problem of mining XML data we study the various techniques to mine XML data and yet We presented a java based implementation of FLEX algorithm for mining XML data.…”
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4
Mining association rules from structured XML data
Published 2009“…Many techniques have been proposed to tackle the problem of mining XML data. We study the various techniques to mine XML data and yet We presented a java based implementation of FLEX algorithm for mining XML data.…”
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5
A web-based implementation of k-means algorithms
Published 2022“…This stinginess of proximity measures in data mining tools is stifling the performance of the algorithm. …”
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6
Image clustering comparison of two color segmentation techniques
Published 2010“…The clustering research is regarding the area of data mining and implementation of the clustering algorithms. …”
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7
Features selection for intrusion detection system using hybridize PSO-SVM
Published 2016“…Hybridize Particle Swarm Optimization (PSO) as a searching algorithm and support vector machine (SVM) as a classifier had been implemented to cope with this problem. …”
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8
Software regression test case prioritization for object-oriented programs using genetic algorithm with reduced-fitness severity
Published 2015“…This paper propose an optimized regression test case selection and prioritization for object-oriented software based on dependence graph model analysis of the source code and optimized the selected test case using Genetic Algorithm. …”
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9
Automated path testing using the negative selection algorithm
Published 2017“…Negative selection algorithm (NSA) has been used in this paper to generate test data for path testing automatically. …”
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10
A regression test case selection and prioritization for object-oriented programs using dependency graph and genetic algorithm
Published 2014“…The effective and efficient test case selection from the existing test suite becomes very critical issue in regression testing. …”
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11
The effect of replacement strategies of genetic algorithm in regression test case prioritization of selected test cases
Published 2015“…This study presents an optimized regression test case prioritization of selected test cases for object-oriented software using Genetic algorithm with different replacement strategies. …”
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12
Model structure selection for a discrete-time non-linear system using genetic algorithm
Published 2004“…The results show that the proposed algorithm can be employed as an algorithm to select the structure of the proposed model.…”
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13
Model structure selection for a discrete-time non-linear system using a genetic algorithm
Published 2004“…The results show that the proposed algorithm can be employed as an algorithm to select the structure of the proposed model.…”
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14
Model structure selection for a discrete-time non-linear system using a genetic algorithm
Published 2004“…The results show that the proposed algorithm can be employed as an algorithm to select the structure of the proposed model.…”
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15
Different mutation and crossover set of genetic programming in an automated machine learning
Published 2020“…One of the progressing works for automated machine learning improvement is the inclusion of evolutionary algorithm such as Genetic Programming. The function of Genetic Programming is to optimize the best combination of solutions from the possible pipelines of machine learning modelling, including selection of algorithms and parameters optimization of the selected algorithm. …”
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16
Different mutation and crossover set of genetic programming in an automated machine learning
Published 2020“…One of the progressing works for automated machine learning improvement is the inclusion of evolutionary algorithm such as Genetic Programming. The function of Genetic Programming is to optimize the best combination of solutions from the possible pipelines of machine learning modelling, including selection of algorithms and parameters optimization of the selected algorithm. …”
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17
Regression test case selection & prioritization using dependence graph and genetic algorithm
Published 2014“…Unfortunately, it is costly and time consuming to allow for the re-execution of all test cases during regression testing. The challenge in regression testing is the selection of best test cases from the existing test suite.This paper presents an evolutionary regression test case prioritization for object-oriented software based on extended system dependence graph model of the affected program using genetic algorithm. …”
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18
Case Slicing Technique for Feature Selection
Published 2004“…CST was compared to other selected classification methods based on feature subset selection such as Induction of Decision Tree Algorithm (ID3), Base Learning Algorithm K-Nearest Nighbour Algorithm (k-NN) and NaYve Bay~sA lgorithm (NB). …”
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19
Optimizing Ram Testing Method For Test Time Saving Using Automatic Test Equipment
Published 2017“…A memory Build-in Self-test (BIST) design with capability of algorithm failing sequence capture have been developed to implement in the Automate Test Equipment (ATE) flow for production screen. 3 selected algorithm have been tested on the 8 detect units in ATE flow to prove the concept of this method. …”
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20
Network reconfiguration and control for loss reduction using genetic algorithm
Published 2010“…Whereas the results obtained from both selection methods are similar to each other. Tests results show that Genetic Algorithm is a suitable algorithm as it is an optimization technique with, high accuracy, and it avoids local minimum by searching in several regions to arrive to the global optimum solution. …”
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