„Model-based software engineering“
Suchergebnisse
1.427 Treffer
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Model-based analysis of sEMG signals using Stockwell transform features under varied muscle fiber composition and conduction velocity
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Energy-efficient deep learning-based intrusion detection system for edge computing: a novel DNN-KDQ model
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Petri Nets for Modeling Complex Discrete-Event Systems – An Approach Based on GPenSIM
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Model-Based Software and Systems Engineering – 12th International Conference, MODELSWARD 2024, Rome, Italy, February 21–23, 2024, Revised Selected Papers
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Adaptive caching for operation-based versioning of models
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Hybrid segmentation model and CAViaR -based Xception Maxout network for brain tumor detection using MRI images
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Energy-efficient modeling in WSN-assisted IoT based on software defined network
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TraceME: A Traceability-Based Method for Conceptual Model Evolution – Model-Driven Techniques, Tools, Guidelines, and Open Challenges in Conceptual Model Evolution
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Decision-tree-based distributed learning for IoT devices
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An improved performance model for artificial intelligence-based diabetes prediction
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An optimal attack detection model based on optimized capsule networks with stacked auto encoder in IoT
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A hybrid ontology-based feature selection framework for enhancing predictive accuracy in regression models
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An unsupervised software bug count prediction model based on selected software metrics
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Model-Integrating Software Components – Engineering Flexible Software Systems
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Personalized similarity regression models based on maximum correntropy criterion for stock series prediction
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Supply chain optimization using model-based systems engineering and the Internet of Things
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Ontology-based NLP tool for tracing software requirements and conceptual models: an empirical study
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Ontology Engineering – 12th International Experiences and Directions Workshop on OWL, OWLED 2015, co-located with ISWC 2015, Bethlehem, PA, USA, October 9-10, 2015, Revised Selected Papers
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Information Science and Applications (ICISA) 2016
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Deep Learning-Based Crop Recommendation Model for Grape and Bean Yield Prediction Using Micro and Macronutrient Analysis