# Cooperative Attention-Based Learning between Diverse Data Sources
## 5 Analysis and Discussion
### Evaluation Points and Results  
The evaluation of the proposed algorithm for multi-node networks focuses on learning nodes, quick convergence, and robust solutions for both cooperative and non-cooperative links with the key performance metrics. By cross-referencing the system state and selecting communication strategies based on random starting action links, the algorithm reduces computational costs. It learns from clustered social data on key sentiments and chooses macro actions, which enables efficient decision-making and policy implementation [5]. The algorithm makes decisions quickly to avoid action merging and implements a greedy policy to improve performance. Each scenario is indexed with an initial variable of 0 to a desired indexed \(\gamma_{dis}\). To ensure the smooth functioning of the algorithm, several assumptions are made, including the need for pre-existing data, the requirement for nodes to communicate effectively, and the selection of a cooperative strategy after message exchange. Additionally, a random selection of starting action links is assumed to reduce computational costs. The algorithm effectively generates macro actions by learning from various scenarios depicted by clustering nodes, as shown in Figure 6, in different stages of the spread of COVID-19. Figure 6 depicts the various stages of clustered nodes, including the initial affected, infected, and recovery stages. The figure demonstrates the spread of infection as the simulation progresses and the algorithm learns macro actions to control the spread. Monte Carlo tree search (MCTS), along with a cooperation factor, is employed to control the nodes, enabling effective management of the spread. Decision convergence is crucial for the algorithm's performance, as it replicates reward distribution and eliminates action merging cases for multiple nodes. Implementing a greedy policy improves performance while increasing the number of iterations. Policy accumulation plays a vital role in efficiently distributing spread decisions in the network. By utilizing decentralization for multi-node networks, the algorithm allows for asynchronous decision-making and unbiased policies. This approach improves the accuracy of the results and enables the optimal distribution of spread decisions in the network, providing valuable insights for researchers and policymakers in response to the COVID-19 pandemic. The collected data consisted of 185,755 tweets with an average of 65 words per tweet, giving a word cloud of 76,781 words [44]. The physical data were analyzed using the SEIR model to define the rate of infection and map the accumulated location infection rate. In order to assess the results and policies of the algorithm, key metrics were used as critical performance indicators to evaluate the effectiveness of decision-making policies in controlling disease transmission in a social network. These metrics include infection state, policy decision convergence, and policy accumulation with influence. The optimal cooperative spread metric can be used to identify the most influential nodes in the network and optimize the spread of decision-making policies. These metrics can be measured using accuracy, precision, F1 score, and confusion matrix to ensure the validity of the results and can be seen in Figure 7a,b. Table 1 shows the location and key metrics to predict infection state in the US states of New York and Florida. Figure 7a,b shows our evaluation assessing various performance metrics such as accuracy, precision, F1 score, and ROC AUC at different time points. In Figure 7a, our analysis showed that the decision convergence was generally high across all locations and time points, with an average convergence rate of 67%. We found that policy accumulation with influence played a significant role in determining the model's performance metrics. A higher influence score was associated with better accuracy, precision, F1 score, and ROC AUC, indicating the importance of cooperation and attention-based analysis for optimal decision making.