„software system models“
Suchergebnisse
1.395 Treffer
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Evaluating Voting Systems with Probability Models – Essays by and in Honor of William Gehrlein and Dominique Lepelley
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Advancing the Impact of Design Science: Moving from Theory to Practice – 9th International Conference, DESRIST 2014, Miami, FL, USA, May 22-24, 2014. Proceedings
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Utilizing architecture models for secure distributed web applications and services
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Computational Intelligence, Cyber Security and Computational Models. Emerging Trends in Computational Models, Intelligence and Security Systems – 6th International Conference, ICC3 2023, Coimbatore, India, December 14–16, 2023, Revised Selected Papers
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Bildverarbeitung für die Medizin 2018 – Algorithmen - Systeme - Anwendungen. Proceedings des Workshops vom 11. bis 13. März 2018 in Erlangen
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Application of deep learning in magnetic spherule detection: a combined method of YOLOv8 and U-Net models
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Conceptual Models – Core to the Design of Interactive Applications
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Transformer-based advances in sarcasm detection: a study of contextual models and methodologies
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Einführung in die Softwaretechnik
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Mathematical Models of Software Failures of Digital Control Systems
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Estimating saltwater wedge length in sloping coastal aquifers using explainable machine learning models
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Enhanced wildfire detection and semantic segmentation via fine-tuned deep learning models
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Shaping the Digital Enterprise – Trends and Use Cases in Digital Innovation and Transformation
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Explaining Artificial Inelligence as a Service: Metodology of Assessment and Quality Models
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Übertragbarkeit von Leistungsindikatoren dokumentgetriebener und agiler Systeme
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Predicting groundwater levels in coastal aquifers using deep learning models: a comparative study of sedimentary and metamorphic aquifers in nova scotia
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The impact of large language models on computer science student writing
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Theme section on models and evolution
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Methods of dynamic spectral analysis by self-exciting autoregressive moving average models and their application to analysing biosignals
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Enhancing chlorophyll-a predictions using optimal machine learning models and field spectral reflectance