Evaluating the Conformity to Types of Unified Modeling Language Diagrams with Feature-Based Neural Networks

被引:1
作者
Nedelcu, Irina-Gabriela [1 ]
Ionita, Anca Daniela [1 ]
机构
[1] Natl Univ Sci & Technol Politehn Bucharest, Fac Automat Control & Comp, Automat & Ind Informat Dept, Bucharest 060042, Romania
来源
APPLIED SCIENCES-BASEL | 2024年 / 14卷 / 20期
关键词
UML; deep learning; feature-based datasets; education and training;
D O I
10.3390/app14209470
中图分类号
O6 [化学];
学科分类号
0703 ;
摘要
This article investigates the application of a deep learning model for evaluating the conformity of model images to types of UML diagrams to be used in self-training and educational settings. Our approach leans on a feature-based dataset that captures a broad range of modeling elements from class, state machine, and sequence diagrams, enhancing the ability to recognize a larger variety of categories selected for this research. The neural network trained with these features representing parts of the UML concrete syntax demonstrates 90% in classification accuracy on average, in respect to our previous research on UML diagrams classification without using a feature-based dataset. This study concludes that a feature-based approach, combined with advanced neural network architectures, can improve the classification of such images, especially in edge cases where diagrams contain similar graphical details but the whole does not represent a UML diagram. For the given research, we obtained a 0.87 F1 score.
引用
收藏
页数:22
相关论文
共 31 条
[1]  
Choi Edward, 2016, JMLR Workshop Conf Proc, V56, P301
[2]  
Chollet F., 2015, Keras
[3]  
Ding X, 2015, PROCEEDINGS OF THE TWENTY-FOURTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE (IJCAI), P2327
[4]  
Dosovitskiy A, 2020, INT C LEARN REPR
[5]  
Goodfellow I, 2016, ADAPT COMPUT MACH LE, P1
[6]  
Google Images Search. (n.d.), Google Images Search: A Python Library for Searching Images Using the Google Custom Search API. PyPI
[7]   Automatic Classification of UML Class Diagrams Using Deep Learning Technique: Convolutional Neural Network [J].
Gosala, Bethany ;
Chowdhuri, Sripriya Roy ;
Singh, Jyoti ;
Gupta, Manjari ;
Mishra, Alok .
APPLIED SCIENCES-BASEL, 2021, 11 (09)
[8]   Deep Residual Learning for Image Recognition [J].
He, Kaiming ;
Zhang, Xiangyu ;
Ren, Shaoqing ;
Sun, Jian .
2016 IEEE CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR), 2016, :770-778
[9]   SFA-Net: Semantic Feature Adjustment Network for Remote Sensing Image Segmentation [J].
Hwang, Gyutae ;
Jeong, Jiwoo ;
Lee, Sang Jun .
REMOTE SENSING, 2024, 16 (17)
[10]  
Ioffe S, 2015, PR MACH LEARN RES, V37, P448