Search Results - (( program case selection algorithm ) OR ( java application learning algorithm ))
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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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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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A regression test case selection and prioritization for object-oriented programs using dependency graph and genetic algorithm
Published 2014“…This paper presents an evolutionary regression test case prioritization for object-oriented software based on dependence graph model analysis of the affected program using Genetic Algorithm. …”
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The effect of replacement strategies of genetic algorithm in regression test case prioritization of selected test cases
Published 2015“…Design-based regression testing approaches have been proposed to address changes at higher levels of abstraction, these approaches may not detect changes in the method body and several of the code based addresses procedural programs. 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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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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An Educational Tool Aimed at Learning Metaheuristics
Published 2020“…In this paper, we introduce an education tool for learning metaheuristic algorithms that allows displaying the convergence speed of the corresponding metaheuristic upon setting/changing the dependable parameters. …”
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A Feature Ranking Algorithm in Pragmatic Quality Factor Model for Software Quality Assessment
Published 2013“…The methodology used consists of theoretical study, design of formal framework on intelligent software quality, identification of Feature Ranking Technique (FRT), construction and evaluation of FRA algorithm. The assessment of quality attributes has been improved using FRA algorithm enriched with a formula to calculate the priority of attributes and followed by learning adaptation through Java Library for Multi Label Learning (MULAN) application. …”
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Firefly algorithm for optimal sizing of Standalone Photovoltaic System / Nurizzati Abdul Aziz
Published 2016“…In addition, sizing algorithm with FA was also discovered to outperform sizing algorithm with selected computational intelligence, i.e. …”
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Firefly algorithm for optimal sizing of stand-alone photovoltaic system / Nur Izzati Abdul Aziz
Published 2016“…In addition, sizing algorithm with FA was also discovered to outperform sizing algorithm with selected computational intelligence, i.e. …”
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Using GA and KMP algorithm to implement an approach to learning through intelligent framework documentation
Published 2023Conference paper -
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Towards a better feature subset selection approach
Published 2010“…The selection of the optimal features subset and the classification has become an important issue in the data mining field.We propose a feature selection scheme based on slicing technique which was originally proposed for programming languages.The proposed approach called Case Slicing Technique (CST).Slicing means that we are interested in automatically obtaining that portion 'features' of the case responsible for specific parts of the solution of the case at hand.We show that our goal should be to eliminate the number of features by removing irrelevant once.Choosing a subset of the features may increase accuracy and reduce complexity of the acquired knowledge.Our experimental results indicate that the performance of CST as a method of feature subset selection is better than the performance of the other approaches which are RELIEF with Base Learning Algorithm (C4.5), RELIEF with K-Nearest Neighbour (K-NN), RELIEF with Induction of Decision Tree Algorithm (ID3) and RELIEF with Naïve Bayes (NB), which are mostly used in the feature selection task.…”
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Adoption of machine learning algorithm for analysing supporters and non supporters feedback on political posts / Ogunfolajin Maruff Tunde
Published 2022“…This thesis is based on the application of sentiment classification algorithm to tweet data with the goal of classifying messages based on the polarity of sentiment towards a particular topic (or subject matter). …”
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An integrated algorithm of analytical network process with case-based reasoning to support the selection of an ideal football team formation and players
Published 2021“…Then, the system will select the appropriate players to be placed in the specified formation. …”
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An efficient and effective case classification method based on slicing
Published 2006“…The paper also studies the comparison between the proposed method and the two selected classification algorithms using several domains.…”
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Development Of Machine Learning User Interface For Pump Diagnostics
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AI powered asthma prediction towards treatment formulation: an android app approach
Published 2022“…TensorFlow is utilized to integrate machine learning with an Android application. We accomplished asthma therapy using an Android application developed in Java and running on the Android Studio platform.…”
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A novel N-input voting algorithm for X-by-wire fault-tolerant systems
Published 2014“…These systems include N-modular redundant (NMR) hardware systems and diversely designed software systems based on N-version programming (NVP). Depending on the characteristics of the application and the type of selected voter, the voting algorithms can be implemented for either hardware or software systems. …”
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