Testing and Assessment of Protocols and Systems Modeled as Extended Finite State Machines حسب Salameh, Tariq Mohammad

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Assessing test suites of extended finite state machines against model and code based faults حسب El-Fakih, Khaled

الموضوعات: "…Model-based testing…"
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On Studying the Effectiveness of Extended Finite State Machine Based Test Selection Criteria حسب Jadoon, Noshad Khan

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Parallel Implementations for Eliminating Finite State Machine Mutants حسب Badawi, Emad Mohammad

الموضوعات: "…Model Based Testing…"
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A Formal Assisted Approach for Modeling and Testing Security Attacks in IoT Edge Devices حسب Bhanpurawala, Alifiya

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Fault Coverage and Diagnosis of Protocols and Systems Modeled As Extended Finite State Machines حسب Hassoun, Mark Habib

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Machine learning based personalized drug response prediction for lung cancer patients حسب Rizwan Qureshi (15279193)

"…Each patient’s unique mutation status was modeled considering MD simulation to extract molecular-level geometric features. …"

Testing web applications حسب Mansour, Nashat

"…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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Testing web applications. (c2004) حسب Houri, Manal Azzam

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MMPatho: Leveraging Multilevel Consensus and Evolutionary Information for Enhanced Missense Mutation Pathogenic Prediction حسب Fang Ge (1533166)

"…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. …"

Integration of nonparametric fuzzy classification with an evolutionary-developmental framework to perform music sentiment-based analysis and composition حسب Abboud, Ralph

"…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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