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Advances in Multimedia publishes research on the technologies associated with multimedia systems, including computer-media integration for digital information processing, storage, transmission, and representation.
Advances in Multimedia maintains an Editorial Board of practicing researchers from around the world, to ensure manuscripts are handled by editors who are experts in the field of study.
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Artificial Intelligence Interactive Design System Based on Digital Multimedia Technology
The current extraction speed of artificial intelligence interactive elements is low, with the low effect, resulting in the poor effect of artificial intelligence interaction. Therefore, a new artificial intelligence interaction method has been developed for digital multimedia technology, performing analysis based on the current background of artificial intelligence, providing a good environment foundation for the interactive place, so that it can integrate artificial intelligence technology after interaction. Aiming at the current problems of digital multimedia technology in the use of artificial intelligence interaction design, innovative exploration of artificial intelligence technology will be carried out based on multimedia technology and innovative thinking. Based on the in-depth analysis of digital multimedia technology, the relationship between artificial intelligence technology and digital multimedia technology is analyzed, and an artificial intelligence interactive design system based on digital multimedia technology is proposed. Finally, the digital multimedia technology is verified on the artificial intelligence interactive design through case analysis.
Research on Forecasting Methods of Agricultural Products Consumption Behavior Based on Unsupervised Learning
Constructing a perfect urban fresh agricultural product supply system is the basic guarantee for the stability of urban life, and it is also the basic condition for supporting urbanization. This paper combines unsupervised learning algorithms to predict and analyze agricultural product consumption behaviors, determines whether to generate new neurons through the cumulative error value of the winning neurons, and gives a network model that can dynamically self-grow. Moreover, this paper constructs an agricultural product consumption behavior prediction model based on unsupervised learning and uses data to verify the performance of the algorithm in this paper. After confirming the performance test of the algorithm, it verifies the prediction effect of this method on the consumption behavior of agricultural products. Through statistical analysis of data, it can be known that the prediction method of agricultural consumption behavior based on unsupervised learning has a certain effect.
Research on the Impact of Interest Rate and Virtual Finance Reform on GDP Growth Based on Error Correction Model
In the context of the virtual economy, monitoring and controlling the operation of the virtual economy to prevent excessive asset bubbles due to inherent volatility and minimize the harm to the macroeconomy is one of the important tasks of economic development. This study applies the error correction model to the analysis of GDP growth factors, improves the algorithm to adapt it to the needs of GDP growth analysis, and constructs an analysis model of the impact of interest rate and virtual financial reforms on GDP growth. Moreover, this study combines data analysis to verify the performance of the model in this study. Through experimental analysis, we can see that the error correction model proposed in this study can play an important role in the analysis of GDP growth factors. At the same time, this study verifies that virtual financial reform and interest rate reform can have a certain impact on GDP growth and have a certain degree of relevance.
Research on the Application of CAD Auxiliary Intelligent Technology in Sports
This paper mainly combines CAD auxiliary intelligent technology and image processing technology, by combining the experience and creativity of sports coaches and sports researchers, and using modern technology, it can design and efficiently study sports quickly, conveniently, and efficiently and uses advanced high-tech technology to replace the traditional relatively backward artificial design method of sports. In the field of sports, in-depth research and scientific research and feasibility analysis can be conducted, so as to realize the design of CAD auxiliary intelligent technology system for sports competitive movements. Finally, the experimental results show the CAD auxiliary intelligence technology in sports to verify the feasibility and have practical value.
Extended Function Analysis of Urban Planning and Design Based on Automatic Extraction Algorithm of Closed Area Boundary
With the continuous development of social economy, the expansion of cities often leads to the disorderly utilization of land resources and even waste. In view of these limitations and requirements, this paper introduces the automatic extraction algorithm of closed area boundary, combs the requirements of urban boundary extraction involved in urban planning and design, and uses the technology of geospatial analysis to carry out spatial analysis practice from three angles, so as to realize the expansion of functional analysis of urban planning and design and improve the efficiency and rationality of urban planning. The simulation results show that the automatic extraction algorithm of closed area boundary is effective and can support the functional analysis of urban planning and design expansion.
Multiscale Deep Network with Centerness-Aware Loss for Salient Object Detection
Deep encoder-decoder networks have been adopted for saliency detection and achieved state-of-the-art performance. However, most existing saliency models usually fail to detect very small salient objects. In this paper, we propose a multitask architecture, M2Net, and a novel centerness-aware loss for salient object detection. The proposed M2Net aims to solve saliency prediction and centerness prediction simultaneously. Specifically, the network architecture is composed of a bottom-up encoder module, top-down decoder module, and centerness prediction module. In addition, different from binary cross entropy, the proposed centerness-aware loss can guide the proposed M2Net to uniformly highlight the entire salient regions with well-defined object boundaries. Experimental results on five benchmark saliency datasets demonstrate that M2Net outperforms state-of-the-art methods on different evaluation metrics.