| Reference : SEMKIS : Software Engineering Methodology for Knowledge Management of Intelligent Systems |
| Reports : Internal report | |||
| Engineering, computing & technology : Computer science | |||
| http://hdl.handle.net/10993/39769 | |||
| SEMKIS : Software Engineering Methodology for Knowledge Management of Intelligent Systems | |
| English | |
Jahic, Benjamin  [University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Computer Science and Communications Research Unit (CSC) >] | |
| 2018 | |
| Laboratory for Advanced Software Systems | |
| 43 | |
| Belval | |
| Luxembourg | |
| [en] Software Engineering ; Deep Learning ; Methdology | |
| [en] Today, there is a high demand on intelligent systems (e.g chatbots, ob-
ject decetors, translators, etc). Engineers have to develop these systems in a lots of di erent domains (e.g. medicine, nance, car industry). More- over, these intelligent systems are trained on data collected from these do- mains using an iterative training process. Et each training iteration, the parameters of such system are updated intuitivly based on the engineer's experience. However, gathering and labelling these data is very costly and time consuming. Moreover, the systems are often complex. It is recom- mended to have a strong mathematical background. Thus, engineers often design these systems based on their own experience and collected informa- tion about the system. We present the road towards a novel methodology, called SEMKIS, for the design ang generation of intelligent systems and synthetic learning data. We use the model-driven engineering approach in our methodology to specify and design our systems. We generate speci - cations, designs and implementation of our intelligent systems. We used the mathematical set theory to de ne the concepts for the speci cation of intelligent systems and data synthetis within a formal conceptual frame- work. The concepts have been used in a small executable illustration that focuses on the recognition of handwritten digits on a picture. The results show that our concepts are usable and that we reduce the complexitiy of specifying and designing intelligent systems. | |
| Researchers ; Professionals ; Students | |
| http://hdl.handle.net/10993/39769 |
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