Model-Driven Engineering Method to Support the Formalization of Machine Learning using SysML

Methods: This work introduces a method supporting the collaborative definition of machine learning tasks by leveraging model-based engineering in the formalization of the systems modeling language SysML. The method supports the identification and integration of various data sources, the required definition of semantic connections between data attributes, and the definition of data processing steps within the machine learning support. Results: By consolidating the knowledge of domain and machine learning experts, a powerful tool to describe machine learning tasks by formalizing knowledge using the systems modeling language SysML is introduced. The method is evaluated based on two use cases, i.e., a smart weather system that allows to predict weather forecasts based on sensor data, and a waste prevention case for 3D printer filament that cancels the printing if the intended result cannot be achieved (image processing). Further, a user study is conducted to gather insights of potential users regarding perceived workload and usability of the elaborated method. Conclusion: Integrating machine learning-specific properties in systems engineering techniques allows non-data scientists to understand formalized knowledge and define specific aspects of a machine learning problem, document knowledge on the data, and to further support data scientists to use the formalized knowledge as input for an implementation using (semi-) automatic code generation. In this respect, this work contributes by consolidating knowledge from various domains and therefore, fosters the integration of machine learning in industry by involving several stakeholders.Comment: 43 pages, 24 figure, 3 table

Model-Driven Engineering for Artificial Intelligence - A Systematic Literature Review

Objective: This study aims to investigate the existing body of knowledge in the field of Model-Driven Engineering MDE in support of AI (MDE4AI) to sharpen future research further and define the current state of the art. Method: We conducted a Systemic Literature Review (SLR), collecting papers from five major databases resulting in 703 candidate studies, eventually retaining 15 primary studies. Each primary study will be evaluated and discussed with respect to the adoption of (1) MDE principles and practices and (2) the phases of AI development support aligned with the stages of the CRISP-DM methodology. Results: The study's findings show that the pillar concepts of MDE (metamodel, concrete syntax and model transformation), are leveraged to define domain-specific languages (DSL) explicitly addressing AI concerns. Different MDE technologies are used, leveraging different language workbenches. The most prominent AI-related concerns are training and modeling of the AI algorithm, while minor emphasis is given to the time-consuming preparation of the data sets. Early project phases that support interdisciplinary communication of requirements, such as the CRISP-DM \textit{Business Understanding} phase, are rarely reflected. Conclusion: The study found that the use of MDE for AI is still in its early stages, and there is no single tool or method that is widely used. Additionally, current approaches tend to focus on specific stages of development rather than providing support for the entire development process. As a result, the study suggests several research directions to further improve the use of MDE for AI and to guide future research in this area

Combined alpha-methylacyl coenzyme A racemase/p53 analysis to identify dysplasia in inflammatory bowel disease

Identification of dysplasia in inflammatory bowel disease represents a major challenge for both clinicians and pathologists. Clear diagnosis of dysplasia in inflammatory bowel disease is sometimes not possible with biopsies remaining "indefinite for dysplasia." Recent studies have identified molecular alterations in colitis-associated cancers, including increased protein levels of alpha-methylacyl coenzyme A racemase, p53, p16 and bcl-2. In order to analyze the potential diagnostic use of these parameters in biopsies from inflammatory bowel disease, a tissue microarray was manufactured from colons of 54 patients with inflammatory bowel disease composed of 622 samples with normal mucosa, 78 samples with inflammatory activity, 6 samples with low-grade dysplasia, 12 samples with high-grade dysplasia, and 66 samples with carcinoma. In addition, 69 colonoscopic biopsies from 36 patients with inflammatory bowel disease (28 low-grade dysplasia, 8 high-grade dysplasia, and 33 indefinite for dysplasia) were included in this study. Immunohistochemistry for alpha-methylacyl coenzyme A racemase, p53, p16 and bcl-2 was performed on both tissue microarray and biopsies. p53 and alpha-methylacyl coenzyme A racemase showed the most discriminating results, being positive in most cancers (77.3% and 80.3%) and dysplasias (94.4% and 94.4%) but only rarely in nonneoplastic epithelium (1.6% and 9.4%; P > .001). Through combining the best discriminators, p53 and alpha-methylacyl coenzyme A racemase, a stronger distinction between neoplastic tissues was possible. Of all neoplastic lesions, 75.8% showed a coexpression of alpha-methylacyl coenzyme A racemase and p53, whereas this was found in only 4 of 700 nonneoplastic samples (0.6%). alpha-methylacyl coenzyme A racemase/p53 coexpression was also found in 10 of 33 indefinite for dysplasia biopsies (30.3 %), suggesting a possible neoplastic transformation in these cases. Progression to dysplasia or carcinoma was observed in 3 of 10 p53/alpha-methylacyl coenzyme A racemase-positive, indefinite-for-dysplasia cases, including 1 of 7 cases without and 2 of 3 cases with p53 mutation. It is concluded that combined alpha-methylacyl coenzyme A racemase/p53 analysis may represent a helpful tool to confirm dysplasia in inflammatory bowel disease