Abstract A small intestinal stromal tumor is a type of gastrointestinal tumor with hidden clinical manifestations. The present diagnostic approaches mostly depend on the examination and diagnosis of patients' abdominal Computed Tomography (CT), which heavily depends on the expertise of physicians and exhibits inefficiency. Although tumor detection methods based on neural networks can overcome the shortcomings of artificial diagnosis, the detection of small intestinal stromal tumors exhibits characteristics such as blurred boundaries and low discrimination, resulting in difficulties in data annotation and insufficient training data. Therefore, this paper introduces the knowledge distillation technologies, applies computational intelligence methods, and then proposes a self-correction mechanism based on deformable convolution and a regional weight Loss function based on pathGAN to enhance the distillation effect. In the procession of knowledge distillation, a threshold constraint is set on the loss of the teacher network: when the threshold is exceeded, the offset matrix between the output and the label is calculated to perform deformable convolution on the teacher network feature map to correct its errors; When the threshold is below it, the pathGAN network is used to calculate the distillation loss, so as to achieve regional key learning in the student network. This method prevents the error of the teacher network from being transmitted to the student network for further iteration, while assigning different weights to each region of the image to guide the student network to strengthen its learning of the foreground region. The experimental results prove that, compared to other knowledge distillation methods, the proposed method better balances the computational speed and detection accuracy of the model.
Small intestinal stromal tumor (SIST) is a common gastrointestinal tumor. Currently, SIST diagnosis relies on clinical radiologists reviewing CT images from medical imaging sensors. However, this method is inefficient and greatly affected by subjective factors. The automatic detection method for stromal tumors based on computer vision technology can better solve these problems. However, in CT images, SIST have different shapes and sizes, blurred edge texture, and little difference from surrounding normal tissues, which to a large extent challenges the use of computer vision technology for the automatic detection of stromal tumors. Furthermore, there are the following issues in the research on the detection and recognition of SIST. After analyzing mainstream target detection models on SIST data, it was discovered that there is an imbalance in the features at different levels during the feature fusion stage of the network model. Therefore, this paper proposes an algorithm, based on the attention balance feature pyramid (ABFP), for detecting SIST with unbalanced feature fusion in the target detection model. By combining weighted multi-level feature maps from the backbone network, the algorithm creates a balanced semantic feature map. Spatial attention and channel attention modules are then introduced to enhance this map. In the feature fusion stage, the algorithm scales the enhanced balanced semantic feature map to the size of each level feature map and enhances the original feature information with the original feature map, effectively addressing the imbalance between deep and shallow features. Consequently, the SIST detection model's detection performance is significantly improved, and the method is highly versatile. Experimental results show that the ABFP method can enhance traditional target detection methods, and is compatible with various models and feature fusion strategies.
Due to the lack of medical materials in some emergency public events, for example, the outbreak of COVID-19, it is urgent to establish a medical emergency material warehouse. Taking Xi’an, China, as an example, this study aims to select suitable sites of Xi’an medical emergency material warehouse. In this study, the problem of site selection models as a multiobjective optimization problem. The coverage function and comprehensive efficiency function are designed as two conflicting objectives. Then, a multiobjective evolutionary algorithm based on multiple memetic direction is proposed to optimize the two objectives concurrently. The crossover and mutation operators are designed for evolutionary multiobjective site selection. The proposed crossover operator is able to balance the global and local search abilities, and the proposed mutation operator fuses the distribution information of hospital location, service population, and the overall coverage. Experiments on real dataset verify the superiority of the proposed evolutionary multiobjective site selection method.
Analysis of the temporal distribution characteristics of waves can evaluate the temporal distribution status of regional wave energy resources. In this paper, wave dataset from an offshore wave buoy was used, annual and seasonal variation characteristics of significant wave height and zero-crossing wave period were analyzed. Joint frequency distribution between wave height and wave period was established by using Copula function to analyze their correlations.
With the country’s policy support and the rapid development of Internet technology, the domestic consumption level has been escalating and the consumption structure has changed. The traditional retail industry cannot integrate all the relevant data due to data security and privacy protection concerns so that it is unable to adjust sales strategies in an accurate and timely manner. New retail has sounded the clarion call for the retail revolution. The supply chain demand forecasting is an important problem for the supply chain management. In this research, we propose a new retail supply chain commodity demand forecasting framework based on vertical federal learning, which solves the problems of data security and privacy faced by new retail theoretically and empirically. In experiments, we use datasets from different platforms (such as social platforms, e-commerce platforms, and retailers) in the same region for federated learning. The experiment results demonstrate the superiority of the proposed algorithm.
Student loans had to a large extent alleviated their economic burden of poverty students in universities. When the universities gave students economic assistance, they should at the same time, pay attention to the cultivation of their ability of various aspects, and improve their social competitiveness.
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