## 4 Results and Discussion  
Java code for clustering and the SecPri-BGMPOP technique were used in the trials, which were done under Windows utilising HADOOP jars in CloudSim. Python was used in the development of the suggested procedure. Python was used in the development of the suggested procedure. The Universiti Kebangsaan Malaysia faculty of education provided the autism datasets. The autism datasets used in this study were gathered from autistic children in various age groups. These include the 24-month dataset for autistic children, which contains 26 attributes and 209 instances; the 30-month dataset for autistic children, which contains 29 attributes and 209 instances; the 36-month dataset for autistic children, which includes 31 attributes and 234 instances; and the 48-month dataset for autistic children, which includes 33 attributes and 302 instances. There are three score alternatives for each dataset, which are autism diagnostic data: z = 0, v = 5, and x = 10. The cut-off numbers varied for each type of dataset at 71, 95, 100, and 105, respectively.  
Our research's experimental findings are contrasted with the current system. By tracking the amount of time and memory used during execution, the SecPri-BGMPOP technique is assessed. The whole SecPri-BGMPOP execution duration in Cloudsim is precisely calculated and measured in milliseconds (ms). The memory utilisation was effectively organised using the Java-based CloudSim programme. Kilobytes are used to measure the size of the SecPri-memory BGMPOP's footprint and the stack memory needed for newly generated objects (kb).  
The Boost Graph Convolutional Network Clustering approach's SecPri-BGMPOP threshold is \"1\" for time and memory efficiency tests. Table 1 lists the results for various cluster sizes. Then, in terms of time and memory requirements, the proposed system is compared with FCM [38] and WFCM [39] and the Boost Graph Convolutional Network Clustering employed in previous work.  
According to a graph drawn for time against cluster count (Figures 7 and 8), the process takes less time to complete the more clusters there are. Because many mappers can run simultaneously, the procedure can be completed more quickly plotted as shown in Figures 7 and 8. It is discovered that the least amount of memory is needed to complete a process when the number of clusters is low because higher cluster counts also result in higher memory allocation.  
Based on clusters 1, 2, and 4, the Boost Graph Convolutional Network Clustering method was evaluated in terms of time and memory for the SecPri-BGMPOP threshold values of 1, 2, and 4. The suggested system and the Boost Graph Convolutional Network Clustering used in earlier work are then compared with FCM and WFCM in terms of time and memory requirements. Because our suggested approach is now maintained in a cloud environment, analytics consumers can use it without worrying about their security or privacy. In order to demonstrate that the method based on Boost Graph Convolutional Network Clustering has produced better results than the existing FCM methodology, we compared it to the latter. Measuring performance includes looking at things like memory utilisation and execution time. The Boost Graph Convolutional Network Clustering recommended method produced improved outcomes, as illustrated in Figure 13.  
Figure 11: Clustering-based time measure for SecPri-BGMPOP threshold value “4”.  
Figure 12: Clustering-based memory measure for SecPri-BGMPOP threshold value “4”.  
Figure 13: Comparison of time measures with BGCNC and FCM for SecPri-BGMPOP threshold value “\(1,2,4^{\prime\prime}\).