## 6 Discussion
for commercial and research investigations to develop technology support systems such as multimedia guides by handheld or wearable devices, art authentication, and recommendation systems according to customer's preferences. AI, sometimes called machine intelligence, has been seeping into our lives, and we use it to accomplish fundamental tasks [54]. In 2017, [PERSON] and [PERSON] attempted to group ML applications in museums into art authentication, commercial recommendations, guiding, three-dimensional virtual reality, data analysis, ticketing, and museum layout [55]. Consequently, it is important to improve the problem of ELP by utilizing new technologies such as automating layout [42], ML [56; 57; 58; 59], big data analysis [60], etc. Museum crews dynamically fill in the objects database for any museum, and the number of collected objects could be thousands or even more. Selecting four trains just from the objects database of 96 items in this study may generate 79,727,040 = P (96,4) layout instances at random. This is a hard problem for planners to manually optimize such numerous permutations manually by calculating the total score of each layout instance with six variables. If there are thousands of objects collected, the optimization of ELP is much more challenging and arduous than this study. It is essential to optimize ELP plans in advance before the exhibition, and it needs appropriate technologies to leverage planners to complete the endeavor. The heuristic algorithms of ML have solved the layout problems of facility, floor, and furniture; a GA is a fundamental algorithm and can be a great help for planners to overcome the layout problem effectively. Research into ML application in ELP is a very interesting and feasible issue to work on. It is urgent and important to establish a customized recommendation scheme using heuristic algorithms to support ELP. Railway museums belong to the transport and industry museum category, and the majority of real display objects are commonly immense and heavy. In general, the proposed three aspects, 12 dimensions, 30 physical principles, 24 suggestions, and 5 main procedures, which are suitable for multiple types of museums not limited to transport, or industry museums, e.g., geology museum, military museums, and art museums etc. Some conditions of principles can be adjusted, e.g., the appropriate visual distance should be modified by the size of 3D exhibits for best visual distance that is twice the diagonal of an object [26]. Besides, due to the cultural heritage and the desire for historical buildings to be an exhibition space, museum curators should notice and select the shortest width of the gallery walls: which, to our understanding, is [(2\(\times\) object height \(\times\) 1.6) + 1] m [26]. These principles, suggestions, and procedures can also be used to develop layout patterns for multiple types of museums. Appropriate layout planning can reduce costs by at least 30% [48]. Of course, the impact factor of ELP is not only costs but also includes the precious cultural heritage, value of collections, efficiency, visiting time, reception/visitor facilities, visitors' behavior and satisfaction, etc. ELP, a design of a physical environment in which the interpretive environment is provided, is becoming an increasingly critical issue as our society faces the development of cultural tourism and economy. ELP is a tactical decision and a complex, high-cost, time-consuming, and laborious manual process. Establishing a customized recommendation scheme in ELP by ML can help planners and museum crews collect and accumulate the prior knowledge for sustainable utilization and improve ELP. ML application in ELP is a hard task and needs the cooperation of domain experts to implement it. There is currently not enough space to provide visitors an abundant, knowledgeable, and educational setting within supplementary displays.