## 4 Experiments and Results
The supervised learning approaches such as SVM and RF are conventional for analyzing the phase samples of signals deeply. In the developed testbed, this proposed article assumes more complex uncertain conditions for channel optimization. Under this case, the existing SMC is not qualified to obtain better precision and prediction rate (link quality) compared with the proposed model. It attains 85% of precision rate and 82% of link quality prediction rate. This is lower than the proposed DMCAP and CRLR (91% of precision and 87% of prediction). The reason behind the better performance of CRLR is related to effective training procedures of reinforcement network over channel parameters. At the same time, OETN attains minimal growth in terms of system precision and link quality prediction. Since the OETN procedures and  
Figure 7: System precision rate. following baseline node evaluation procedures to optimize the channel utilization and energy utilization, the result is not significant under uncertain underground conditions. As provided in Figures 8 and 9, the testbed was adapted to evaluate the performance of all techniques against changing uncertainty rate (Equation (26)). Link quality predictions can be observed against channel distortions and malicious events. Comparably, the proposed DMCAP shows 8% of better precision rate than other techniques. In this experiment, the proposed techniques observe the major channel distortion issues using multi-channel assessment principles and channel quality tuple analysis procedures. Additionally, the proposed DMCAP ensures multi-level ML and DL evaluation schemes for predicting noise, SNIR, entropy, and other significant distortions [48; 49]. The absence of deeply trained evaluation procedures affects the existing techniques against uncertain WUGSN channels. Figures 8 and 9 show the measurements of link quality prediction rate against environmental distortions and malicious event distortions. Predicting the link quality to transmit the data without loss is a primary goal of this proposed system. The goal can be achieved when the sources of data loss are detected properly. The liveliness of channels is affected due to either environmental disturbances or malicious events. Finding and treating the issues ensure the better link prediction rate. Considering the practical issues of link quality management, the proposed DMCAP system initiates dual case channel quality monitoring practices. In this manner, DMCAP implements both environmental property assessment models and malicious event assessment models. The experiments show the benefit of resilience proposed DMCAP system as provided in Figures 8 and 9  
Compared with other techniques, the performance of DMCAP is commendable (96% of prediction rate against maximum uncertainty rate). On the other hand, CRLR produces optimal prediction and security benefits against OETN and SMC. As CRLR has channel-aware RL engines in each node, the impact shows on better results (92%). At the same time, the existing techniques such as SMC and OETN are not efficient against malicious events under uncertain wireless networks. SMC and OETN are the good procedure for analyzing the channel metrics under limited channel assumptions with minimal rate of dynamic network conditions. However, the development of multi-channel assessment and reactive route selection are primary goals for next generation networks. On the scope, the proposed DMCAP is performing uniquely compared with other techniques. DMCAP maintains the link quality prediction rate between 99.8% and 96.6% against raising uncertainty rate. The uncertainty rate of each link denotes the overall signal interruptions produced as noise, interference, link failures, node failures, and intruder activities on the channel. Likewise, DMCAP holds 99.7% to 97% of link quality prediction rate against malicious events (Figure 9).