## 4 Analysis, Results, and Discussion
### Stage 1--Data Analysis Using Infra Nodus  
The research idea stated in the abstract and the research problem statement were used as the main seeds to generate the connections, facts, and ideas before being processed via two scientific search engines, namely Google Scholar and PubMed. This software works on a systematic similarity matrix and compares frequencies to reveal research gaps, as shown in Figure 1.  
The resulting cloud with different entries shows multiple clusters every time. When one is chosen, we can reveal all the connected text by selecting the Questions, Ideas, or Facts buttons, to be reclassified as output data files, i.e., CSVs. This technique also helps in specifying the algorithms of the adaptation actions, specifically related to the coastal heritage sites, as shown in Figure 2.  
It is advised that main node of the coastal heritage site connected with hazards and facing material deterioration, according to its type, be adapted strictly as it is a non-newable resource. All of those connections appear in one cluster, followed by the second nearest cluster, i.e., the climate change impacts highlighting the main defense procedures and solutions, and show that the major impact of climate change on coastal heritage sites is sea-level rise. The third cluster highlights the main methods to achieve the research objective of devising a resilience approach to adapting to the impacts of climate change on coastal heritage. The fourth cluster is related to machine learning, while the final cluster addresses the range of resilience and its related indicators.  
Figure 1: The Infra Nodus user interface after the first entry of the research ideas; the colored cluster groups classify the most connected components, which have to be dealt with together, and the node size refers to the order of importance in relation to the inserted idea.  
Another processing operation was performed using the evolution of topics that were implemented, by classifying the number of occurrences per text segmentation to show the connections between the clusters, splitting them into a maximum of ten parts, as shown in Figures 6-8.  
Figure 4: Second trial of reprocessing the information and files.  
Figure 5: Third trial of multiple entry, whereby solutions finally produced better results.  
Figure 6: First trial of processing of topic evolution in relation to clusters.  
Narrative-influence propagation is usually performed after cumulative processing to regenerate ideas from the main topics and to show consistency and variability. This consistent presentation of the main clusters appears in the propagation dynamics processed via cyclical variability based on detrended fluctuation analysis of influence, as shown in Figures 9-11.  
Figure 8: Third trial of processing of topic evolution in relation to clusters.  
Figure 7: Second trial of processing of topic evolution in relation to clusters.  
Figure 9: First trial of cyclical variability-alpha exponent 0.45 in the narrative-influence propagation.  
From the previous results and their related analysis, the resilience adaptation plan was conceptually formulated with respect to solutions, impacts, and priorities. The data were extracted as the final output of this step from the Infra Nodus software as a CSV file, as shown in Table 3, to be used as an input for machine learning data mining using WEKA software.