# Evaluation of Deep Learning-Based Automatic Floor Plan Analysis Technology: An AHP-Based Assessment
## 4 Results
### Relative Weight of Floor Plan Analysis Technology  
In this study, the consistency rate of participants below the reference value of 0.1 was used to calculate relative weights. For each module, the relative importance of the criteria and factors were established (see Table 6). From the weight analysis of the criteria elements, distinctiveness (0.3549) scored the highest relative importance, followed by substitutivity (0.2461), completeness (0.2216), and supply and demand (0.1774).  
Under the completeness criterion, completeness of technology (0.3577) is the most important factor, followed by commercialization potential (0.3410), and technical development phase (0.3013). Overall, the factors of completeness exhibited comparatively similar levels of relative importance. With respect to the distinctiveness criteria, the weights of the three factors exhibited relatively high differences. Efficiency had the highest importance, not only under the distinctiveness criteria (0.6001) but also among all factors (0.2130). Originality and novelty had relative index weights of 0.2222 and 0.1777, respectively. For the substitutivity criteria, the possibility of existence of similar technologies (0.6659) had approximately twice the relative importance of the possibility of new technologies emerging (0.3341). Finally, evaluators stated that the demand from customers (0.8334) was five times more important than supply from providers (0.1666).  
Using the value of index weight of criteria and relative index weight among factors, the final importance was derived. Among all factors, efficiency from distinctiveness criteria had the highest importance (0.2130); the existence of the possibility of similar technologies (0.1639) from the substitutivity criteria and demand from customers (0.1478) from supply  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Criteria** & **Importance (Rank)** & **Factors** & **Relative Importance (Rank)** & **Final Importance (Rank)** \\ \hline \multirow{3}{*}{Completeness} & \multirow{3}{*}{0.2216 (3)} & Technical development phase & 0.3013 (3) & 0.0668 (8) \\  & & Completeness of technology & 0.3577 (1) & 0.0793 (5) \\  & & Commercialization potential & 0.3410 (2) & 0.0756 (7) \\ \hline \multirow{3}{*}{Distinctiveness} & \multirow{3}{*}{0.3549 (1)} & Novelty & 0.1777 (2) & 0.0631 (9) \\  & & Originality & 0.2222 (2) & 0.0789 (6) \\  & & Efficiency & 0.6001 (1) & 0.2130 (1) \\ \hline \multirow{3}{*}{Substitutivity} & \multirow{3}{*}{0.2461 (2)} & Possibility of existence of similar technologies of new technologies to emerge & 0.6659 (1) & 0.1639 (2) \\  & & Possibility of new technologies to emerge & 0.3341 (2) & 0.0822 (4) \\ \hline \multirow{2}{*}{Supply and demand} & \multirow{2}{*}{0.1774 (4)} & Demand from customers & 0.8334 (1) & 0.1478 (3) \\  & & Supply from providers & 0.1666 (2) & 0.0296 (10) \\ \hline \hline \end{tabular}
\end{table}
Table 6: Weight calculation of the assessment index.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Category** & **Frequency** & **Rate** \\ \hline \multirow{3}{*}{Affiliation} & Government and public agency & 10 & 16.39\% \\  & University and research institute & 37 & 60.66\% \\  & Private company & 14 & 22.95\% \\ \hline \multirow{3}{*}{Career period} & Less than 5 years & 19 & 31.15\% \\  & 5–10 years & 20 & 32.79\% \\  & 10–20 years & 12 & 19.67\% \\  & More than 20 years & 10 & 16.39\% \\ \hline \multirow{3}{*}{Education} & Bachelor’s degree & 10 & 16.39\% \\  & Master’s degree & 27 & 44.26\% \\ \cline{1-1}  & Doctoral degree & 24 & 39.34\% \\ \hline \hline \end{tabular}
\end{table}
Table 5: Demographic characteristics of the sample.  
and demand criteria had similar weights and ranked second and third, respectively. The supply from providers (0.0269) from the supply and demand criteria had the lowest weight. Figure 5 shows the relative weights of the factors under each criterion in a radar diagram.