Imaging Examination and Quantitative Detection and Analysis of Gastrointestinal Diseases Based on Data Mining Technology

A Low-Cost Microfluidic-Based Detection Device for Rapid Identification and Quantification of Biomarkers-Based on a Smartphone

The sensitive and rapid detection of microsamples is crucial for early diagnosis of diseases. The short response times and low sample volume requirements of microfluidic chips have shown great potential in early diagnosis, but there are still shortcomings such as complex preparation processes and high costs. We developed a low-cost smartphone-based fluorescence detection device (Smartphone-BFDD) without precision equipment for rapid identification and quantification of biomarkers on glass capillary. The device combines microfluidic technology with RGB image analysis, effectively reducing the sample volume to 20 μL and detection time to only 30 min. For the sensitivity of the device, we constructed a standard sandwich immunoassay (antibody-antigen-antibody) in a glass capillary using the N-protein of SARS-CoV-2 as a biological model, realizing a low limit of detection (LOD, 40 ng mL^-1). This device provides potential applications for different biomarkers and offers wide use for rapid biochemical analysis in biomedical research.

Online Shopping Brand Sales Based on IoT Big Data Processing

As a pair of closely related technical fields, “Internet of Things” and “big data” are gradually applied to the analysis of online shopping, which is very beneficial to promote the healthy and orderly development of the online shopping industry. This article uses the big data processing technology of the Internet of Things to analyze the influencing factors of online shopping brand sales based on the data collected by online questionnaires. It draws certain rules from it, and provides relevant marketing strategies for online shopping brand sales to assist its market positioning. According to the survey, the five aspects of online evaluation, brand awareness, online shopping community, online public opinion influence of related brands, and other brand information acquisition channels all have an impact on online brand sales. Especially women aged 25–35 will pay attention to online information, and 195 people will not choose to buy from brands with negative news. And during the purchase process, 186 people will check the online reviews of related brand products. Among them, there are 183 men aged 25–35 who will pay attention to online reviews.

Research and Application of the Data Mining Technology in Economic Intelligence System

In the context of the rapid development of the modern economy, information is particularly important in the economic field, and information determines the decision-making of enterprises. Therefore, how to quickly dig out information that is beneficial to the enterprise has become a crucial issue. This topic applies data mining technology to economic intelligent systems and obtains the data object model of economic intelligent systems through the integration of information. This article analyzes the interrelationship between its objects on this basis and uses data mining-related methods to mine it. The establishment of economic intelligence systems not only involves the establishment of mathematical models of economic systems, but also includes research on the algorithms applied to them. How to apply an algorithm to quickly and accurately extract the required economic intelligence domain information from the potential information in the database, or to provide a method to find the best solution, involves the use of association rules and classification prediction methods. The application of data mining algorithms can be used to study the application of economic intelligence systems. This paper develops and designs an economic intelligence information database and realizes the economic intelligence system on this basis, and realizes the research results. Finally, this paper has tested the dataset, and the results show that the classification accuracy of this algorithm is 2.64% higher than that of the ID3 algorithm.