Smart hospitals are important components of smart cities. An intelligent medical system for brain tumor segmentation is required to construct smart hospitals. To achieve intelligent brain tumor segmentation, morphological variety and serious category imbalance must be managed effectively. Conventional deep neural networks have difficulty in predicting high-accuracy segmentation images due to these issues. To solve these problems, we propose using multimodal brain tumor images combined with the UNET and LSTM models to construct a new network structure with a mixed loss function to solve sample imbalance and describe an intelligent segmentation process to identify brain tumors. To verify the practicability of this algorithm, we used the open source Brain Tumor Segmentation Challenge dataset to train and verify the proposed network. We obtained DSCs of 0.91, 0.82, and 0.80; sensitivities of 0.93, 0.85, and 0.82; and specificities of 0.99, 0.99, and 0.98 in three tumor regions, including the whole tumor ( WT ), tumor core ( TC ), and enhanced tumor ( ET ). We also compared the results of the proposed network with those of other brain tumor segmentation methods, and the results showed that the proposed algorithm could segment different tumor lesions more accurately, highlighting its potential application value in the clinical diagnosis of brain tumors.

Community detection in complex networks is of great importance in analyzing the interaction patterns and group behaviors. However, the traditional method of community division divide each node in the network into a specific community, while may ignore its internal connection. In this paper, a new strategy that selects a fuzzy function and fuzzy threshold (FF-FT) was presented to discover community structure. Edge dense degree coefficient was introduced to calculate fuzzy relation between nodes, and Fast–Warshall algorithm was used to reduce the complexity of FF-FT. Through the theoretical analysis and the comparison of eight current well-known community detection algorithms on seven real networks and artificial networks with different parameters, the results show that the FF-FT algorithm has a good community detection performance.

Summary To identify recyclable garbage via the garbage classification is an effective countermeasure for protecting the environment. An automatic classification system supported by image recognition technologies is able to significantly reduce huge human labors of recycling tasks. However, performing the real‐time and accurate garbage detection is not a trivial task. In this article, we present an edge‐cloud framework equipped with deep learning model for recyclable garbage detection. Specifically, we propose to use the deep convolutional neural network for garbage images classification, and thus design the collaborative mechanism between edge devices and the cloud server. As a result, we design and develop a novel recyclable garbage detection system, where scanning garbage images and thus detecting recyclable ones can be completed in real‐time. We validate the performances of the proposed recyclable garbage detection system on 1000 real‐life household garbage images. Experimental results show the overall accuracy of our system reaches nearly 90%, and the time for detection is less than 500 ms.

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