Hybrid data transmission scheme based on source node centrality and community reconstruction in opportunistic social networks

Deng, Yepeng; Gou, Fangfang; Wu, Jia

doi:10.1007/s12083-021-01205-3

Cited by 32 publications

(10 citation statements)

References 39 publications

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“…An illness may cause the extinction of rare plants. The purpose in this work is improving the survival rate of rare plants [ 20 ]. We adopt Pterospermum heterophyllum, which is a kind of rare plant in the plant ecosystem to explain the network communication system.…”

Section: System Model Designmentioning

confidence: 99%

Disease Control and Prevention in Rare Plants Based on the Dominant Population Selection Method in Opportunistic Social Networks

Gou

Tian

2022

Computational Intelligence and Neuroscience

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The spread of seeds of rare and dangerous plants affects the regeneration, pattern, genetic structure, invasion, and settlement of plant populations. However, seed transmission is a relatively weak research link. The spread of plant seeds is not controlled by the communicator. Rather, this event results from the interaction between the host and the external environment determined by the mother. The way plants transmit and accept seeds is similar to how user nodes accept data transmission requests in social networks. Plants select the characteristics including seed size, maturity time, and gene matching, which are consistent with the size, delay, and keywords of the data received by the user. In this study, we selected rare and endangered Pterospermum heterophyllum as the research object and applied them to a social network. All plants were considered nodes and all seeds as transmitted data. This method avoids the influence of errors in actual sampling and statistical laws. By using historical information to record the reception of seeds, the Infection and Immunity Algorithm (IAIA) in opportunistic social networks was established. This method selects healthy plants through plant social populations and reduces the number of diseased plants. The experimental results show that the IAIA algorithm has a good effect in distinguishing dominant seedlings from seedlings with disease genes and realizes the selection of dominant plants in social networks.

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Section: System Model Designmentioning

confidence: 99%

Disease Control and Prevention in Rare Plants Based on the Dominant Population Selection Method in Opportunistic Social Networks

Gou

Tian

2022

Computational Intelligence and Neuroscience

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“…The community, between the media and social [31][32][33][34], satisfies the connection between a person and a group. From the BBS when the Internet started to the current social networks (Facebook, WeChat Moments, etc.)…”

Section: Data Decision and Transmission Model Designmentioning

confidence: 99%

Effective Data Optimization and Evaluation Based on Social Communication with AI-Assisted in Opportunistic Social Networks

Gou

Long

et al. 2022

Wireless Communications and Mobile Computing

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Billions of people around the world send and receive data over online networks daily. Sufficient and redundant data are transmitted over social platforms with AI-assisted in 5G networks. In opportunistic social networks, the main challenge faced by traditional methods is that numerous user nodes participate in data transmission, causing a lot of message copy redundancy and node cache consumption. As a result, the transmission delay of the algorithm is high, the node energy consumption is too large, and even information is lost. To solve these problems, this study establishes an artificial intelligence-based optimization multiple evaluation method. The main purpose of this method is to avoid information loss caused by data loss when reducing data noise, reasonably select communication nodes in opportunistic social network scenarios, optimize data transmission performance, and avoid network congestion. Moreover, our method can effectively identify and exclude potential malicious nodes, reducing the situation that packets are intercepted and discarded. The experiment confirms that the optimized transmission evaluation scheme can effectively reduce routing overheads and energy consumption of a user node, improve the delivery ratio of node data transmission, and ensure the reliability and security of data transmission.

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“…Meanwhile, osteosarcoma has a wide range of local tissue development and shape [39,40]. Tese properties cause indistinct tumor boundaries and complex form structures, making it difcult to maintain edge features [41][42][43]. As a result, it is worth looking into how to segment osteosarcoma efectively and properly.…”

Section: Introductionmentioning

confidence: 99%

Auxiliary Segmentation Method of Osteosarcoma MRI Image Based on Transformer and U-Net

Liu

Zhu

et al. 2022

Computational Intelligence and Neuroscience

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One of the most prevalent malignant bone tumors is osteosarcoma. The diagnosis and treatment cycle are long and the prognosis is poor. It takes a lot of time to manually identify osteosarcoma from osteosarcoma magnetic resonance imaging (MRI). Medical image processing technology has greatly alleviated the problems faced by medical diagnoses. However, MRI images of osteosarcoma are characterized by high noise and blurred edges. The complex features increase the difficulty of lesion area identification. Therefore, this study proposes an osteosarcoma MRI image segmentation method (OSTransnet) based on Transformer and U-net. This technique primarily addresses the issues of fuzzy tumor edge segmentation and overfitting brought on by data noise. First, we optimize the dataset by changing the precise spatial distribution of noise and the data-increment image rotation process. The tumor is then segmented based on the model of U-Net and Transformer with edge improvement. It compensates for the limitations of U-semantic Net by using channel-based transformers. Finally, we also add an edge enhancement module (BAB) and a combined loss function to improve the performance of edge segmentation. The method’s accuracy and stability are demonstrated by the detection and training results based on more than 4,000 MRI images of osteosarcoma, which also demonstrate how well the method works as an adjunct to clinical diagnosis and treatment.

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Hybrid data transmission scheme based on source node centrality and community reconstruction in opportunistic social networks

Cited by 32 publications

References 39 publications

Disease Control and Prevention in Rare Plants Based on the Dominant Population Selection Method in Opportunistic Social Networks

Disease Control and Prevention in Rare Plants Based on the Dominant Population Selection Method in Opportunistic Social Networks

Effective Data Optimization and Evaluation Based on Social Communication with AI-Assisted in Opportunistic Social Networks

Auxiliary Segmentation Method of Osteosarcoma MRI Image Based on Transformer and U-Net

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