Transactions on Engineering and Computing Sciences <p>Transactions on Engineering and Computing Sciences is peer-reviewed open access online journal that provides a medium of the rapid publication of original research papers, review articles, book reviews and short communications covering all areas of machine learning and artificial Intelligence. The journal publishes state-of-the-art research reports and critical evaluations of applications, techniques and algorithms in Engineering Management, Cloud Systems, Electrical Engineering, Industrial Networks and Intelligent Systems, Mechanical Civil and Chemiical Engineering, Internet of Things, Mathematical Modeling, Robotics Research, Engineering informatics, Computer Science, Computer Hardware/Software, Robotics and application, Embedded Systems, Data Base Management &amp; Information Retrievals, Geographical Information Systems/ Global Navigation Satellite Systems, Fuzzy Systems, Web and Internet computing, Machine learning, Artificial intelligence, Cognitive science, Software engineering, Database systems, Soft computing, Optimization and modelling and related application areas.</p> Services for Science and Education, United Kingdom en-US Transactions on Engineering and Computing Sciences 2054-7390 Scrutinizing UML Teaching and Learning Modeling Tools <p>The purpose of this paper was to identify, synthesize, and analyze the modeling tools, especially those that support UML modeling and interactive learning methods for Software Engineering and Information Systems Design courses. The goals were to guide both professors and students to choose the proper modeling tools to support software engineering courses objectives. The research identified many successful modeling tools that can help students use what is suitable for learning UML modeling and contribute to engaging students in modeling. Moreover, this research used the meta-ethnography method for synthesizing qualitative results in the area of software engineering, especially modeling. Group of modelling tools have been chosen for this paper based on their usages popularity and successfulness in producing high quality design models. &nbsp;The contribution of this paper is highlighting and defining the strengths, weakness, and limitations of each studied tool.<strong> &nbsp;</strong></p> Abdulqader Almasabe Stephanie Ludi Abdelrahman Elfaki Copyright (c) 2023 Abdulqader Almasabe, Stephanie Ludi, Abdelrahman Elfaki 2023-01-20 2023-01-20 11 1 1 21 10.14738/tecs.111.13820 Standardization of Criteria across Multiple Evaluators to Detect Objects <p>For a typical object detection task with machine learning technique, there has an absolute correct label on which a mechanical model is constructed. However, there are many tasks in which labels vary with evaluators because they have different criteria for discrimination. It happens when the detection criteria are vague and undefined among evaluators. This problem is avoided by constructing an individual model, but it is not recommended from a long-term perspective, because the model depends on a specific evaluator.&nbsp;&nbsp;</p> <p>This paper proposes a method to standardize the evaluator's criteria. The method is verified in the image detection task of asteroid powders in paint material as an example of ambiguity in the criteria.&nbsp;&nbsp;</p> <p>The performance and variance of detection with the proposed method are compared with conventional ones to evaluate whether it allows us to standardize the evaluators’ criteria. It turns out a clear reduction in the variance of evaluators’ detection results without significant degradation of the performance for all evaluators. It is confirmed this method standardizes the criteria across multiple evaluators. In addition, the paper discusses how to obtain manuals formally expressing the standardized criteria with text mining.&nbsp;&nbsp;</p> Naoya Wakabayashi Hiromitsu Shimakawa Copyright (c) 2023 Naoya Wakabayashi, Hiromitsu Shimakawa 2023-01-31 2023-01-31 11 1 22 36. 10.14738/tecs.111.13876