A semantic feature enhanced YOLOv5-based network for polyp detection from colonoscopy images.

Colorectal cancer (CRC) is a common digestive system tumor with high morbidity and mortality worldwide. At present, the use of computer-assisted colonoscopy technology to detect polyps is relatively mature, but it still faces some challenges, such as missed or false detection of polyps. Therefore, how to improve the detection rate of polyps more accurately is the key to colonoscopy. To solve this problem, this paper proposes an improved YOLOv5-based cancer polyp detection method for colorectal cancer. The method is designed with a new structure called P-C3 incorporated into the backbone and neck network of the model to enhance the expression of features. In addition, a contextual feature augmentation module was introduced to the bottom of the backbone network to increase the receptive field for multi-scale feature information and to focus on polyp features by coordinate attention mechanism. The experimental results show that compared with some traditional target detection algorithms, the model proposed in this paper has significant advantages for the detection accuracy of polyp, especially in the recall rate, which largely solves the problem of missed detection of polyps. This study will contribute to improve the polyp/adenoma detection rate of endoscopists in the process of colonoscopy, and also has important significance for the development of clinical work.

Colorectal image analysis for polyp diagnosis.

Colorectal polyp is an important early manifestation of colorectal cancer, which is significant for the prevention of colorectal cancer. Despite timely detection and manual intervention of colorectal polyps can reduce their chances of becoming cancerous, most existing methods ignore the uncertainties and location problems of polyps, causing a degradation in detection performance. To address these problems, in this paper, we propose a novel colorectal image analysis method for polyp diagnosis via PAM-Net. Specifically, a parallel attention module is designed to enhance the analysis of colorectal polyp images for improving the certainties of polyps. In addition, our method introduces the GWD loss to enhance the accuracy of polyp diagnosis from the perspective of polyp location. Extensive experimental results demonstrate the effectiveness of the proposed method compared with the SOTA baselines. This study enhances the performance of polyp detection accuracy and contributes to polyp detection in clinical medicine.

Research on epidemic spread model based on cold chain input.

In recent years, the new type of coronary pneumonia (COVID-19) has become a highly contagious disease worldwide, posing a serious threat to the public health. This paper is based on the SEIR model of the new coronavirus pneumonia, considering the impact of cold chain input and re-positive on the spread of the virus in the COVID-19. In the process of model design, the food cold chain and re-positive are used as parameters, and its stability is analyzed and simulated. The experimental results show that taking into account the cold chain input and re-positive can effectively simulate the spread of the epidemic. The research results have important research value and practical significance for the prevention and control of the COVID-19 and the prediction of important time nodes.

An Empirical Mode Decomposition Fuzzy Forecast Model for COVID-19.

At present, the Corona Virus Disease 2019 (COVID-19) is ravaging the world, bringing great impact on people's life safety and health as well as the healthy development of economy and society, so the research on the prediction of the development trend of the epidemic is crucial. In this paper, we focus on the prevention and control of epidemic using the relevant technologies in the field of artificial intelligence and signal analysis. With the unknown principle of epidemic transmission, we first smooth out the complex and variable epidemic data through the empirical mode decomposition model to obtain the change trends of epidemic data at different time scales. On this basis, the change trends under different time scales are trained using an extreme learning machine to obtain the corresponding prediction values, and finally the epidemic prediction results are obtained by fitting through Adaptive Network-based Fuzzy Inference System. The experimental results show that the algorithm has good learning ability, especially in the prediction of time-series sequences can guarantee the accuracy rate while having low time complexity. Therefore, this paper not only plays a theoretical support for epidemic prevention and control, but also plays an important role in the construction of public emergency health system in the long run.

Research on multi-effect evaporation salt prediction based on feature extraction.

In the multi-effect evaporation salt making process, the smooth operation of the salt making process is crucial. As the salt production process continues, many unstable factors will cause the salt production process not to proceed smoothly. These factors can be discovered in advance by predicting the salt production data, thus, it is of great significance to predict the multi-effect evaporation salt production data. In the process of multi-effect evaporation and salt production, the multiple salt-making devices make the influence between the parameters closer, and the influence of a single parameter on itself is sometimes ductile. Therefore, the data of multi-effect evaporation and salt production have the characteristics of high dimensions, high complexity and temporal information. If the historical salt production data is used for data prediction directly, the prediction model will take a long time and the prediction effect is not good. Thus, how to predict the multi-effect evaporation salt production data is the main research problem of this paper. In view of the above problems, according to the characteristics of multi-effect evaporation salt production data, this paper analyzes and improves the self encoder for feature extraction of multi effect-evaporation salt production data, so as to solve the problem of high dimensions and high complexity of salt production data. On this basis, combined with the time-series information contained in the salt production data, a multi-effect evaporation salt production data prediction model is proposed based on long-term and short-term memory cycle neural network to solve the prediction problem of time-series salt production data. Experiments show that the prediction model can predict and prevent the problems in salt production line in advance. It has a certain theoretical research value and application value in the intelligent production process and production line optimization of salt chemical industry.

An Early Intestinal Cancer Prediction Algorithm Based on Deep Belief Network.

The incidence of colorectal cancer (colorectal cancer, CRC) in China has increased in recent years, and its mortality rate has become one of the highest among all cancers. CRC also increasingly affects people's health and quality of life, and the workloads of medical doctors have further increased due to the lack of sufficient medical resources in China. The goal of this study was to construct an automated expert system using a deep learning technique to predict the probability of early stage CRC based on the patient's case report and the patient's attributes. Compared with previous prediction methods, which are either based on sophisticated examinations or have high computational complexity, this method is shown to provide valuable information such as suggesting potentially important early signs to assist in early diagnosis, early treatment and prevention of CRC, hence helping medical doctors reduce the workloads of endoscopies and other treatments.

Industry Upgrading: Recommendations of New Products Based on World Trade Network.

GDP is a classic indicator of the extent of national economic development. Research based on the World Trade Network has found that a country's GDP depends largely on the products it exports. In order to increase the competitiveness of a country and further increase its GDP, a crucial issue is finding the right direction to upgrade the industry so that the country can enhance its competitiveness. The proximity indicator measures the similarity between products and can be used to predict the probability that a country will develop a new industry. On the other hand, the Fitness-Complexity algorithm can help to find the important products and developing countries. In this paper, we find that the maximum of the proximity between a certain product and a country's existing products is highly correlated with the probability that the country exports this new product in the next year. In addition, we find that the more products that are related to a certain product, the higher probability of the emergence of the new product. Finally, we combine the proximity indicator and the Fitness-Complexity algorithm and then attempt to provide a recommendation list of new products that can help developing countries to upgrade their industry. A few examples are given in the end.