Tesi etd-12142023-140640 |
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Tipo di tesi
Tesi di laurea magistrale
URN
etd-12142023-140640
Titolo
Using machine learning for automatic classification of the layout quality of UML class diagrams
Dipartimento
INGEGNERIA DELL'INFORMAZIONE
Corso di studi
ARTIFICIAL INTELLIGENCE AND DATA ENGINEERING
Relatori
.
relatore Cimino, Mario Giovanni Cosimo Antonio
relatore Alfeo, Antonio Luca
relatore Fruzzetti, Chiara
relatore Alfeo, Antonio Luca
relatore Fruzzetti, Chiara
Parole chiave
- artificial intelligence
- computer vision
- data mining
- deep learning
- UML quality AI
- UML schema
- YOLO
Data inizio appello
13/02/2024
Consultabilità
Non consultabile
Data di rilascio
13/02/2094
Riassunto (Inglese)
Riassunto (Italiano)
This thesis focuses on the quality assessment of UML diagram layouts employing cutting-edge machine learning and computer vision techniques. The developed software, following a meticulous training phase, demonstrates the capability to assign a quality grade and furnish constructive feedback to designers upon submitting their schemas. State-of-the-art methodologies were employed, and the achieved results stand as a benchmark in addressing the challenges inherent to UML diagram layout assessment.
The framework leverages advanced tools such as YOLO, PyTorch, OpenCV, and Detecto, including the Faster R-CNN architecture. This amalgamation of frameworks contributes to the robustness and efficiency of the system, ensuring that the outcomes not only meet but also compare favorably with the current state-of-the-art solutions to this pervasive problem.
The framework leverages advanced tools such as YOLO, PyTorch, OpenCV, and Detecto, including the Faster R-CNN architecture. This amalgamation of frameworks contributes to the robustness and efficiency of the system, ensuring that the outcomes not only meet but also compare favorably with the current state-of-the-art solutions to this pervasive problem.
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