Advances in Multimedia
 Journal metrics
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Acceptance rate5%
Submission to final decision137 days
Acceptance to publication32 days
CiteScore0.400
Journal Citation Indicator0.220
Impact Factor1.4

Video Abnormal Action Recognition Based on Multimodal Heterogeneous Transfer Learning

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 Journal profile

Advances in Multimedia publishes research on the technologies associated with multimedia systems, including computer-media integration for digital information processing, storage, transmission, and representation.

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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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Research Article

Design of 3D Environment Combining Digital Image Processing Technology and Convolutional Neural Network

As virtual reality technology advances, 3D environment design and modeling have garnered increasing attention. Applications in networked virtual environments span urban planning, industrial design, and manufacturing, among other fields. However, existing 3D modeling methods exhibit high reconstruction error precision, limiting their practicality in many domains, particularly environmental design. To enhance 3D reconstruction accuracy, this study proposes a digital image processing technology that combines binocular camera calibration, stereo correction, and a convolutional neural network (CNN) algorithm for optimization and improvement. By employing the refined stereo-matching algorithm, a 3D reconstruction model was developed to augment 3D environment design and reconstruction accuracy while optimizing the 3D reconstruction effect. An experiment using the ShapeNet dataset demonstrated that the evaluation indices—Chamfer distance (CD), Earth mover’s distance (EMD), and intersection over union—of the model constructed in this study outperformed those of alternative methods. After incorporating the CNN module in the ablation experiment, CD and EMD increased by an average of 0.1 and 0.06, respectively. This validates that the proposed CNN module effectively enhances point cloud reconstruction accuracy. Upon adding the CNN module, the CD index and EMD index in the dataset increased by an average of 0.34 and 0.54, respectively. These results indicate that the proposed CNN module exhibits strong predictive capabilities for point cloud coordinates. Furthermore, the model demonstrates good generalization performance.

Research Article

Multilevel Feature Fusion-Based GCN for Rumor Detection with Topic Relevance Mining

This paper addresses the problem of detecting internet rumors in social media. Rumors do great harm to information society, making rumor detection necessary. However, existing methods for detecting rumors generally only learn pattern features or text content features from the whole propagation process, which fall short in capturing multilevel features with topic relevance of text content from social media data. In this paper, we propose a novel graph convolution network model, named multilevel feature fusion-based graph convolution network (MFF-GCN) which can employ multiple streams of GCNs to learn different level features of rumor data, respectively. We build a heterogeneous tweet graph for each single-level feature GCN to encode the topic relation among tweets based on the text contents. Experiments on real-world Twitter data demonstrate that our proposed approach achieves much better performance than the state-of-the-art methods with higher values of precision and recall as well as their corresponding F1 score. In addition, the diversity of our experimental results shows the generalization ability of our model.

Review Article

Algorithm Comparison and Evaluation of GAN Models Based on Image Transferring from Desert to Green Field

Some time-consuming and labor-intensive techniques, like manual drawing or interactive modeling with an image editing system, are often used to show how a desert area might look after being transformed into a green field (oasis) in an image way. In order to improve the rendering efficiency of image style transformation and increase the variety of renderings, we can build an algorithm for automatically generating style images based on machine learning. In this paper, after comparing seven generative adversarial network (GAN) models in the way of theory analysis, we propose a method for generating green fields using desert images as input data, and a comprehensive comparison is presented on how GANs are currently applied to solve the desert-to-oasis problem. Experimental results show that two GAN models, geometrically consistent GAN and cyclic consistent GAN, have the best transfer effect of a desert image to oasis one in the view of quantitative indicators, Fréchet inception distance, and learned perceptual image patch similarity.

Research Article

Multimodal Fake News Detection Incorporating External Knowledge and User Interaction Feature

With the development of online social media, the number of various news has exploded. While social media provides an information platform for news release and dissemination, it also makes fake news proliferate, which may cause potential social risks. How to detect fake news quickly and accurately is a difficult task. The multimodal fusion fake news detection model is the current research focus and development trend. However, in terms of content, most existing methods lack the mining of background knowledge hidden in the news content and ignore the connection between background knowledge and existing knowledge system. In terms of the propagation chain, the research tends to emphasize only the single chain from the previous communication node, ignoring the intricate communication chain and the mutual influence relationship among users. To address these problems, this paper proposes a multimodal fake news detection model, A-KWGCN, based on knowledge graph and weighted graph convolutional network (GCN). The model fully extracted the features of the content and the interaction between users of the news dissemination. On the one hand, the model mines relevant knowledge concepts from the news content and links them with the knowledge entities in the wiki knowledge graph, and integrates knowledge entities and entity context as auxiliary information. On the other hand, inspired by the “similarity effect” in social psychology, this paper constructs a user interaction network and defines the weighted GCN by calculating the feature similarity among users to analyze the mutual influence of users. Two public datasets, Twitter15 and Twitter16, are selected to evaluate the model, and the accuracy reaches 0.905 and 0.930, respectively. In the comparison experiments, A-KWGCN model has more significant advantages than the other six comparison models in four evaluation indexes. Also, ablation experiments are conducted to verify that knowledge module and weighted GCN module play the significant role in the detection of fake news.

Research Article

Smart Building Skin Design with Dynamic Climate Adaptability of Smart Cities Based on Artificial Intelligence

As the separation and carrier of indoor and outdoor energy and climate conditions, building skin plays an important role in indoor environment regulation and effective utilization of outdoor environmental resources. The traditional fixed skin of residential buildings in cold regions lacks the ability to respond to the external climate, so it is difficult to meet the dual requirements of building energy efficiency and indoor comfort. In the long river of architectural development, the most important thing of architectural design is how to meet the climate adaptability. Traditional architectural forms have long been unable to meet the current social development, climate conditions, and user needs. Based on the basic theory, this paper establishes a systematic understanding of inlay, studies the design method of complex skin with geometric algorithm as the operating tool, discusses the application of this method in architectural design in combination with practice, more systematically and comprehensively studies the building skin with dynamic climate adaptability, and makes a physical model of building skin with dynamic climate adaptability. The contrast experiments under different control modes were carried out using the climate chamber experimental system. This research focuses on taking geometric principles as the prototype, trying to break the common design idea of generating skin by overlapping cells, and providing a systematic skin design method with strong operability and modular structure, hoping to help expand creative thinking.

Research Article

[Retracted] Analysis of E-Commerce Marketing Strategy Based on Xgboost Algorithm

With the development and popularization of multimedia technology, people’s communication and information dissemination become more and more convenient. The use of new media technology in marketing accelerates the enterprise’s adaptation to market changes. With the help of new marketing technology, the quality of marketing can be improved and the effect of marketing can be maximized. This paper reviews the current literature on e-commerce marketing and then analyzes the feasibility of precision marketing in e-commerce market in the new media era. In order to screen potential consumers and improve the success rate of precision marketing, this paper establishes a prediction model for precision marketing of bank credit cards. A public dataset selected in this paper contains the basic characteristics of the customer, such as age, gender, monthly income, monthly consumption, and whether the customer has agreed to process the credit card after personalized recommendation. After data cleaning and preprocessing, XGboost algorithm is used to predict whether the customer will process the credit card. The calculation shows that the XGboost algorithm can still maintain a high accuracy when dealing with fewer characteristic variables. This study is helpful to better predict users’ consumption intention, further explore potential customers, and improve the success rate of precision marketing.

Advances in Multimedia
 Journal metrics
See full report
Acceptance rate5%
Submission to final decision137 days
Acceptance to publication32 days
CiteScore0.400
Journal Citation Indicator0.220
Impact Factor1.4
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