  One of the pillars of any machine learning model is its concepts. Using
software engineering, we can engineer these concepts and then develop and
expand them. In this article, we present a SELM framework for Software
Engineering of machine Learning Models. We then evaluate this framework through
a case study. Using the SELM framework, we can improve a machine learning
process efficiency and provide more accuracy in learning with less processing
hardware resources and a smaller training dataset. This issue highlights the
importance of an interdisciplinary approach to machine learning. Therefore, in
this article, we have provided interdisciplinary teams' proposals for machine
learning.
