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Traditional network data transmission (DT) has certain limitations in terms of processing, storage, and communication capabilities. Moreover, DT is easily affected by the network environment, which can reduce the real-time performance of DT. In severe cases, network transmission failures, DT interruptions, and other issues may even occur. As a new network, wireless sensor networks (WSN) have been widely used in military, industrial, agricultural, medical and other fields. In this paper, WSN was regarded as an important research method, and in-depth research was carried out around the real-time and energy consumption (for the convenience of the following text, the abbreviation for energy consumption is EC) of WSN DT. This paper focused on the issues of real-time performance, EC, and real-time transmission. It balanced EC and improved real-time performance through the low energy adaptive clustering hierarchy (LEACH) protocol and the power-efficient gathering in sensor information systems (PEGASIS) protocol. The experimental results showed that the real-time rates of LEACH under single reaction nodes were 45.5% and 48.6%, respectively. The lowest and highest real-time rates of PEGASIS were 86.2% and 89.5%, respectively. The real-time rate of PEGASIS was much higher than that of LEACH, proving that the proposed PEGASIS had high real-time performance and reduced DT delay.

In the backdrop of evolving technological landscapes, this paper delves into the utilization of computer technology, specifically Artificial Neural Networks (ANN), in enhancing piano improvisation instruction. Recognizing the growing demand for innovative teaching methods, our study aims to evaluate the practical effectiveness of ANN-based teaching evaluation in real-world piano improvisation settings. Through rigorous testing, we found that ANN demonstrates remarkable precision and consistency in assessing piano improvisation skills. In comparison to the traditional decision tree algorithm, ANN excels in managing complex nonlinear relationships, providing more accurate and reliable scoring results. This integration of technology not only elevates students’ performance but also fosters their musical creativity and perception. Our findings suggest potential improvements in refining instructional methodologies and expanding the use of computer technology in piano teaching. This underscores the importance of personalized teaching, blended learning models, and technical proficiency training for educators. Overall, our research methodologies and findings significantly contribute to advancing the modernization and technological progress of music education, equipping piano instructors with cutting-edge teaching tools and strategies.

To provide an effective way for product modeling design, a product modeling design method based on the fusion of texture and shape features and computer technology is proposed. Based on the product modeling design drawing. By constructing the image texture gray-level co-occurrence matrix and extracting the image texture primitives, the energy, inertia, entropy, and evenness statistics of the texture are obtained, which serve to describe the image texture characteristics. The OHTA color model is employed to segment the shape and background of the product design drawing, while the Fourier descriptor is utilized to obtain the shape features. Based on the texture and shape features required for product modeling design, the image required for product modeling design is retrieved from the image database by calculating the similarity between the texture and shape features and the image feature vector in the image database. Using the retrieved image as input, the framework of the product modeling design virtual environment is first established, and subsequently, the product modeling design is implemented within this virtual environment using Rhino 3D software. The experiment shows that the texture and shape features extracted by this method are more accurate, and can effectively retrieve the image needed for product modeling design from the image database according to the texture and shape features. Based on this image, the product modeling design is realized, and the application effect is relatively remarkable.

The professional painting industry has experienced a dramatic breakthrough with the rapid expansion of computer science and technology. In the current digital era, digital painting art is extending the more significant creative space to add new content. Digital painting is the modern trend of mainstream painting presented to the public as a new generation of visual art. Creativity may show up, and new techniques of creating art can arise infinitely with the assistance of computer intelligence technology. This article explains how computer image processing is used in the production of art. The report offers a painting technique based on Image Rendering (IR), which does not rely on human expertise in the past, and a color image is turned into a photo with a painting effect automated. Image-based rendering is a novel way in which computer graphics and picture processing are drawn and combined with the requirement to build geometric models, get information from the input image simply by interpolating views, deforming images, and reconstructing the desired action. This article proposes the indirect use of picture processing technology and computer technology to produce oil painting. It will investigate the application of contemporary digital picture technology in order not only to maintain traditional tastes, and to keep pace with the pace of the times, to create traditional optimization.

A prediction model of financial fraud of listed companies based on machine learning method is proposed to predict financial fraud of listed companies. Using the data set of Chinese listed companies from 2000 to 2020 as observation samples, Benford’s Law, LOF local anomaly method and SMOTE oversample were adopted, grey samples were excluded, and characteristic variables were selected from five aspects: fraud motivation, solvency, profitability, cash flow and operating capacity. The financial fraud identification model Xscore is established based on the XGBoost method. The Xscore model can improve the accuracy of model prediction, and is superior to the Fscore model and Cscore model in accuracy, recall rate, AUC index, KS value, PSI stability, etc. It is more suitable for predicting the financial fraud of listed companies in China. The results of this study are helpful in promoting the research and application of artificial intelligence and machine learning in accounting, and provide references for promoting the disclosure of high-quality financial information by listed companies and maintaining the order of the capital market.

This paper presents a comparative study of the linear and nonlinear relationship between energy consumption and economic growth in China and explores the dynamic dependence between the two. The study has some reference value for the formulation of China’s energy policy. Specifically, the study of the nonlinear relationship between energy consumption and economic growth helps to understand the relationship between the two better. A study of the nonlinear relationship between energy consumption and economic growth will help to grasp the interrelationship between the two in a more rational manner, providing the basic premise for the formulation of energy development strategies that will enable China to achieve healthy economic development and sustainable social development. The study of the nonlinear relationship between energy consumption and economic growth helps to grasp the interrelationship between the two more rationally and provides the basic premise for the formulation of energy development strategies, thus enabling China to achieve healthy economic growth and sustainable social development.

The paper describes the growth of image processing technology in the last 25 years. The current market place is described and future developments based on rapidly growing computer technology are predicted…