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Lately, traffic panel detection has been engrossed by academia and industry. This study proposes a new categorization method for traffic panels. The traffic panels are classified into three classes: symbol-based, text-based, and supplementary/additional traffic panels. Although few types of research have investigated text-based traffic panels, this type is considered in detail in this study. However, there are many challenges in this type of traffic panel, such as having different languages in different countries, their similarity with other text panels, and the lack of suitable quality datasets. The panels need to be detected first to obtain a reasonable accuracy in recognizing the text. Since there are few public text-based traffic panels datasets, this study gathered a novel dataset for the Persian textbased traffic panels all over the streets of Tehran-Iran. This dataset includes two collections of images. The first collection has 9294 images, and the latter has 3305 images. The latter dataset is more monotonous than the first one. Thus, the latter is utilized as the main dataset, and the first is used as an additional dataset. To this end, the algorithm uses the additional dataset for pre-training and the main datasets for training the network. The tiny YOLOv3 algorithm that is fast and has low complexity compared to the YOLOv3 is used for pre-training, training, and testing the data to examine the utility and advantages of the data. The K-fold cross-validation procedure is used to estimate the model's skill on the new data. It achieves 0.973 for Precision, 0.945 for Recall, and 0.955 for Fmeasure.

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Network analysis is an important term in different scientific areas and finding the structure of communities is a significant challenge in network analysis. A group of vertices with high intra-connection and sparse inter-connection is called community. In this paper, we propose a novel method for community detection in networks, which works better in time and precision compared to similar methods. The proposed method is able to detect communities of a wide variety of networks with different properties. This method is an agglomerative parallel algorithm. Also it can find multiple communities and exchange the nodes between detected communities simultaneously. It has utilized local modularity for constructing the communities. After all, genetic algorithm is used to optimize the parameters of the proposed method. The algorithm is evaluated by modularity metric and shows a noticeable good precision. Also it has used simulated annealing to maximize the modularity.

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