Search Results - (( code application learning algorithms ) OR ( _ applications out algorithm ))
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Enhanced Q-Learning algorithm for potential actions selection in automated graphical user interface testing
Published 2023“…We utilized the enhanced Q-Learning algorithm to compare actions, including context-based actions, to effectively achieve higher code coverage. …”
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Static code analysis of permission-based features for android malware classification using apriori algorithm with particle swarm optimization
Published 2015“…In this method, permission-based features were extracted from Android applications byte-code through static code analysis, selected and were used to train supervised classifiers. …”
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Phishing image spam classification research trends: Survey and open issues
Published 2020“…Achieving the study’s target, we carried out a broad survey and analysis to identify the domains where spam classification was applied. …”
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Android Malware classification using static code analysis and Apriori algorithm improved with particle swarm optimization
Published 2014“…In this method, features were extracted from Android applications byte-code through static code analysis, selected and were used to train supervised classifiers. …”
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Kodepoly: an engaging approach to blended futuristic learning in coding
Published 2024“…By combining the strategic elements of Monopoly with a curriculum comprised of coding challenges, debugging exercises, and algorithmic puzzles, Kodepoly aims to render the learning process both enjoyable and substantial in content. …”
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Operational structural damage identification using de-noised modal feature in machine learning / Chen Shilei
Published 2021“…By integrating ISMA, both supervised and unsupervised machine learning algorithms were investigated to develop real-time damage identification schemes. …”
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Improving the exploration strategy of an automated android GUI testing tool based on the Q-Learning algorithm by selecting potential actions
Published 2022“…We utilise the Q-Learning algorithm to compare actions, including context-based actions, to effectively detect crashes and achieve a higher code coverage.…”
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Efficient Malware Detection And Response Model Using Enhanced Parallel Deep Learning (EPDL-MDR)
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Development Of Machine Learning User Interface For Pump Diagnostics
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Algorithm visualization design guidelines for mobile leaning
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