The ant colony optimization algorithm is used to find the optimal path based on the behavior of an ant while searching a food. It will communicate with other ants using the pheromone to find the best solution. In this paper, we introduce recursive ant colony optimization (RACO) in the wireless mesh network. This technique is used to subdivide a large network into smaller networks and based on the network, the shortest path is found in each subproblem, and finally it is combined to generate an optimal path for the network. In each subproblem, the iteration is performed recursively to obtain the shortest path in that subproblem. In our paper, we use recursive ant colony to reduce redundancy in connection and so the data will transfer in less time effectively. RACO is used to find best solutions more accurate than the other ant colony systems. Isolation of a subproblem is reduced in RACO.
The rapid growth of industry and the economy has contributed to a tremendous increase in traffic in all urban areas. People face the problem of traffic congestion frequently in their day-to-day life. To alleviate congestion and provide traffic guidance and control, several types of research have been carried out in the past to develop suitable computational models for short- and long-term traffic. This study developed an effective multi-dimensional dataset-based model in cyber–physical systems for more accurate traffic-volume prediction. The integration of quantum convolutional neural network and Bayesian optimization (QCNN_BaOpt) constituted the proposed model in this study. Furthermore, optimal tuning of hyperparameters was carried out using Bayesian optimization. The constructed model was evaluated using the US accident dataset records available in Kaggle, which comprise 1.5 million records. The dataset consists of 47 attributes describing spatial and temporal behavior, accidents, and weather characteristics. The efficiency of the proposed model was evaluated by calculating various metrics. The performance of the proposed model was assessed as having an accuracy of 99.3%. Furthermore, the proposed model was compared against the existing state-of-the-art models to demonstrate its superiority.
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