Showing 1 - 11 results of 11 for search 'models based mutation testing', query time: 0.07s Refine Results
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    Parallel Implementations for Eliminating Finite State Machine Mutants by Badawi, Emad Mohammad

    Published 2017
    Subjects: “…Model Based Testing…”
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    Machine learning based personalized drug response prediction for lung cancer patients by Rizwan Qureshi (15279193)

    Published 2022
    “…Each patient’s unique mutation status was modeled considering MD simulation to extract molecular-level geometric features. …”
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    Testing web applications by Mansour, Nashat

    Published 2006
    “…Traditional testing techniques are not adequate for web-based applications, since they miss their additional features such as their multi-tier nature, hyperlink-based structure, and event-driven feature. …”
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    MMPatho: Leveraging Multilevel Consensus and Evolutionary Information for Enhanced Missense Mutation Pathogenic Prediction by Fang Ge (1533166)

    Published 2023
    “…In this study, we propose a novel computational approach, called MMPatho, for enhancing missense mutation pathogenic prediction. First, we established a large-scale nonredundant MM benchmark data set based on the entire Ensembl database, complemented by a focused blind test set specifically for pathogenic GOF/LOF MM. …”
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    Integration of nonparametric fuzzy classification with an evolutionary-developmental framework to perform music sentiment-based analysis and composition by Abboud, Ralph

    Published 2019
    “…Unlike existing solutions, MUSEC is: (i) a hybrid crossover between supervised learning (SL, to learn sentiments from music) and evolutionary computation (for music composition, MC), where SL serves at the fitness function of MC to compose music that expresses target sentiments, (ii) extensible in the panel of emotions it can convey, producing pieces that reflect a target crisp sentiment (e.g., love) or a collection of fuzzy sentiments (e.g., 65% happy, 20% sad, and 15% angry), compared with crisp-only or two-dimensional (valence/arousal) sentiment models used in existing solutions, (iii) adopts the evolutionary-developmental model, using an extensive set of specially designed music-theoretic mutation operators (trille, staccato, repeat, compress, etc.), stochastically orchestrated to add atomic (individual chord-level) and thematic (chord pattern-level) variability to the composed polyphonic pieces, compared with traditional evolutionary solutions producing monophonic and non-thematic music. …”
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