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Industry Upgrading and Public Opinion Prevention Using BERTopic 1

2nd International Conference on Artificial Intelligence Security and Privacy, AIS and P 2024

作者： Wang, Xinhua Yu, Xiaomei Jiang, Yunmeng Zheng, Xiangwei Li, Wei School of Information Science and Engineering Shandong Normal University Jinan China Shandong Provincial Key Laboratory for Distributed Computer Software Novel Technology Jinan250358 China Shandong Normal University Library Shandong Normal University Jinan China

ISBN: (数字)9789819611485

ISBN: (纸本)9789819611478

The development status of the hotel industry in a city is also an essential factor influencing local tourism revenue. In the tourism industry, besides dining and attractions, which are hotspots for public opinion, the hotel industry is also a key area. The development status of the hotel industry in a city is also an essential factor influencing local tourism revenue. In this context, it is crucial for hotels to address how to prevent such negative public opinion crises. In this paper, we use hotel review texts as an example to discuss how to use BERTopic and GRU networks to extract topics and analyze sentiment from guest reviews. This analysis helps understand guests’ focus and sentiment trends regarding hotels, enabling better grasp public opinion direction and user needs. Through topic extraction and sentiment analysis, we can extract crucial information from large scale hotel review texts. This enables monitor public opinion to help prevent large scale public opinion crises, allows for targeted improvements in the hotel industry, and supports decision-making for hotels and the tourism industry. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Hotels

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Sparse and Dense: Learning Confusion Representation Network for 3D Action Recognition

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IEEE Internet of Things Journal 2025年

作者： Hou, Xinran Geng, Pei Li, Tianchen Li, Yalin Li, Yan Lyu, Lei School of Information Science and Engineering Shandong Normal University Jinan250358 China School of Information Engineering Shandong Management University Jinan250100 China Shandong Provincial Key Laboratory for Novel Distributed Computer Software Technology Jinan250358 China

In the field of the Internet of Medical Things (IoMT), the demand for Human Action Recognition (HAR) is growing. Due to the limitations of portability and privacy of traditional sensors, many endeavors have made significant progress in 3D skeleton-based action recognition. However, existing methods ignore potential higher-order semantic information between joints and fail to perceive rapidly changing dynamic details, resulting in frequent confusion of actions with similar motion trajectories. To alleviate this issue, we propose a novel learning confusion representation network (LCR-Net). Specifically, a progressive feature enhancement module is first designed to utilize self-attention to gradually aggregate lower-order features to higher-order features, emphasizing the relative movement between body parts. Second, we design the enhanced spatiotemporal convolution to explore the potential spatio-temporal dependencies between joints by adding a mask matrix and an attention fusion mechanism. To further perceive the spatiotemporal relationships in subtle changes, we divide the interactive sparse-dense pathways at different spatio-temporal resolutions and enhance the complementary information between the two pathways through feature interaction. Finally, the frequency excitation learning module is proposed to efficiently learn the importance of different frequencies by cross-channel modeling, promoting the compactness of actions within classes and the separability of confusion actions. In addition, the lightweight LCR-Net+ is achieved through model compression optimization to meet the deployment requirements of IoT systems. Comprehensive experiments conducted on three public datasets (NTU-RGB+ D60&120, NW-UCLA) demonstrate the superior performance of our model. © 2025 IEEE. All rights reserved.

关键词： Joints (anatomy)

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TFSG: A Self-Supervised Guided Graph Learning Method for Traffic Flow Prediction

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IEEE Internet of Things Journal 2025年

作者： Zheng, Yingran Luo, Chao Shao, Rui Shandong Normal University School of Information Science and Engineering Jinan250014 China Shandong Provincial Key Laboratory for Novel Distributed Computer Software Technology Jinan250014 China China Mobile Shandong Co. Ltd center of network optimization Jinan250014 China

With the rapid development of the Internet of Things, a large amount of real-time traffic data has been collected. How to fully utilize these traffic data for rational planning of intelligent transportation systems is an important issue. Graph learning has proved to be potential tools for modeling complex spatiotemporal relationships in addressing traffic flow prediction problems. Existing methods usually relied on the graph structure constructed from predefined distance information between different regions. However, affected by factors such as road capacity, traffic rules etc., distance bias is non-trivial to affect traffic flow in reality. Besides, the distance based static transportation network structure is also difficult to describe the dynamic and uncertainty of road traffic in complex situations. To address the aforementioned issues, this article proposes an enhanced graph generation approach for traffic flow prediction, which is based on Temporal-Frequency Self-Supervised Guided Graph Learning (TFSG). The method progressively guides the feature latent representation to approach the target latent representation in both the time domain and frequency domain. Meanwhile, a multi-head self-attention mechanism is introduced to capture dependencies among different time series patches, constructing an enhanced graph structure for downstream prediction tasks. Experimental results first independently evaluated the qualities of the generated graphs. Subsequently, by using five public traffic datasets, the feasibility and effectiveness of the proposed method are further verified. © 2014 IEEE.

关键词： Self-supervised learning

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DNMDR: Dynamic Networks and Multi-view Drug Representations for Safe Medication Recommendation

arXiv

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arXiv 2025年

作者： Liu, Guanlin Yu, Xiaomei Liu, Zihao Li, Xue Fan, Xingxu Zheng, Xiangwei School of Information Science and Engineering Shandong Normal University Jinan250358 China Shandong Provincial Key Laboratory for Distributed Computer Software Novel Technology Jinan250358 China State Key Laboratory of High-end Server and Storage Technology Jinan250300 China

Medication Recommendation (MR) is a promising research topic which booms diverse applications in the healthcare and clinical domains. However, existing methods mainly rely on sequential modeling and static graphs for representation learning, which ignore the dynamic correlations in diverse medical events of a patient's temporal visits, leading to insufficient global structural exploration on nodes. Additionally, mitigating drug-drug interactions (DDIs) is another issue determining the utility of the MR systems. To address the challenges mentioned above, this paper proposes a novel MR method with the integration of dynamic networks and multi-view drug representations (DNMDR). Specifically, weighted snapshot sequences for dynamic heterogeneous networks are constructed based on discrete visits in temporal EHRs, and all the dynamic networks are jointly trained to gain both structural correlations in diverse medical events and temporal dependency in historical health conditions, for achieving comprehensive patient representations with both semantic features and structural relationships. Moreover, combining the drug co-occurrences and adverse drug-drug interactions (DDIs) in internal view of drug molecule structure and interactive view of drug pairs, the safe drug representations are available to obtain high-quality medication combination recommendation. Finally, extensive experiments on real world datasets are conducted for performance evaluation, and the experimental results demonstrate that the proposed DNMDR method outperforms the state-of-the-art baseline models with a large margin on various metrics such as PRAUC, Jaccard, DDI rates and so on. Copyright © 2025, The Authors. All rights reserved.

关键词： Electronic health record

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Cooperative Co-evolutionary Artificial Bee Colony Algorithm Based on Hierarchical Communication Model

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Chinese Journal of Electronics 2016年第3期25卷 570-576页

作者： HU Chunyu LIU Hong ZHANG Peng School of Information Science and Engineering Shandong Normal University Shandong Provincial Key Laboratory for Novel Distributed Computer Software Technology

Canonical Artificial bee colony(ABC) algorithm with a single species is insufficient to extend the diversity of solutions and may be trapped into the local optimal solution. This paper proposes a new co-evolutionary ABC algorithm(HABC) based on Hierarchical communication model(HCM). HCM combines advantages of global and local communication pattern. With adjustment strategies on species and groups, HCM can reduce the computational complexity dynamically. Performance tests show that the HABC algorithm exhibit good performance on accuracy, robustness and convergence speed. Compared with ABC and Integrated co-evolution algorithm(IABC),HABC performs better in solving complex multimodal functions.

关键词： Artificial bee colony(ABC) algorithm Multi-species co-evolution Hierarchical communication model(HCM) Dynamical adjustment

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Analysis on the Content Features and Their Correlation of Web Pages for Spam Detection

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China Communications 2015年第3期12卷 84-94页

作者： JI Hua ZHANG Huaxiang Department of computer science Shandong Normal University Shandong Provincial key laboratory for novel distributed computer software technology

In the global information era,people acquire more and more information from the Internet,but the quality of the search results is degraded strongly because of the presence of web *** spam is one of the serious problems for search engines,and many methods have been proposed for spam *** exploit the content features of non-spam in contrast to those of *** content features for non-spam pages always possess lots of statistical regularities; but those for spam pages possess very few statistical regularities,because spam pages are made randomly in order to increase the page *** this paper,we summarize the regularities distributions of content features for non-spam pages,and propose the calculating probability formulae of the entropy and independent n-grams ***,we put forward the calculation formulae of multi features *** them,the notable content features may be used as auxiliary information for spam detection.

关键词： web spam content features feature correlation spam detection

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An endoscopic images and diagnostic records based multimodal method for severity grading of gastric cancer

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Biomedical Signal Processing and Control 2025年 109卷

作者： Xingyu Zhang Xiangwei Zheng Minghan Dong Mingzhe Zhang School of Information Science and Engineering Shandong Normal University Jinan 250358 China Shandong Provincial Key Laboratory for Distributed Computer Software Novel Technology Jinan 250358 China

Background: Gastric cancer (GC) is a common malignant tumor of the digestive system, and accurate grading of its severity is crucial for developing effective health management and follow-up strategies. Currently, most studies adopt unimodal approaches for GC severity grading, such as endoscopic images or diagnostic texts. However, while multimodal classification methods have gained traction, they often emphasize inter-modal interactions while paying limited attention to intra-modal feature processing. However, the high content similarity between gastric cancer endoscopic images and diagnostic records written by physicians poses a significant challenge for effective intra-modal distinction. Methods: To address above challenges, we propose an endoscopic images and diagnostic records based multimodal method for severity grading of gastric cancer (MSGC). Specifically, MSGC employs an enhanced ResNeXt and BERT to simultaneously extract features from endoscopic images and diagnostic texts. Through contrastive learning, samples of the same category are mapped to similar positions in the feature space, enhancing intra-class similarity. A multi-head attention module is utilized to generate weighted representations of feature sequences, emphasizing and concatenating features with higher significance. Additionally, we design a novel loss function that combines contrastive loss between image and text features with cross-entropy loss derived from the prediction distribution, thereby promoting mutual learning between text and image modalities. Results: Experiments are conducted on a private dataset collected from a local hospital and MSGC achieved a Precision of 94.84%, Recall of 93.69%, Accuracy of 94.14%, and an F1-score of 94.26%. Conclusions: Experimental results demonstrate that MSGC significantly outperforms unimodal approaches in GC severity grading and surpasses other SOTA multimodal methods.

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Multi-Object Tracking based on Optimal Transport and Coordinate Attention Mechanism

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Signal Processing 2025年 236卷

作者： Shi, Wenjuan Zheng, Xiangwei Zhang, Lifeng Ji, Cun Zhang, Yuang Bian, Ji School of Information Science and Engineering Shandong Normal University Jinan250358 China Shandong Provincial Key Laboratory for Distributed Computer Software Novel Technology Jinan250358 China

Multi-Object Tracking (MOT) has currently attracted significant interest due to its wide applications in various fields, such as autonomous driving, intelligent surveillance, and behavior recognition. However, appearance similarity of different objects results in low accuracy of target matching and difficulties in data association. In this paper, we propose a Multi-Object Tracking based on Optimal Transport and Coordinate Attention Mechanism (MOT2A), which addresses above challenges by integrating the attention mechanism with optimal transport. These strategies effectively enhance the extraction of discriminative appearance features and improve target matching between different frames. Firstly, we construct a novel Coordinate attention module (CASA), which models the interdependence between the channel domain and the spatial domain of the feature map. Secondly, a Triplet loss with optimal transport (SK-Triplet) is designed to adjust the distance matrix for effective clustering of positive and negative samples during loss calculation. Finally, extensive experiments are conducted on MOT17 and MOT20. For MOT17: 79.4 MOTA, 78.9 IDF1, and 63.9 HOTA;For MOT20: 77.0 MOTA, 76.3 IDF1, and 62.3 HOTA are achieved, respectively. Compared to existing MOT methods, our method shows significant improvements in accuracy and stability. The code is available at: https://***/420-s/MOT2A. © 2025

关键词： Object detection

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Multi-objective Evolutionary Algorithm Based on Target Space Partitioning Method

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Journal of Donghua University(English Edition) 2011年第2期28卷 177-181页

作者：尚兆霞刘弘李焱 School of Information Science and Engineering Shandong Provincial Key Laboratory for Distributed Computer Software Novel Technology Shandong Normal University

Considering the defects of conventional optimization methods, a novel optimization algorithm is introduced in this paper. Target space partitioning method is used in this algorithm to solve multi-objective optimization problem, thus achieve the coherent solution which can meet the requirements of all target functions, and improve the population's overall evolution level. The algorithm which guarantees diversity preservation and fast convergence to the Pareto set is applied to structural optimization problems. The empirical analysis supports the algorithm and gives an example with program.

关键词： optimization algorithm multi-objective target space partitioning method

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Modeling and Path Generation Approaches for Crowd Simulation Based on Computational Intelligence

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电子学报(英文版) 2012年第4期21卷 636-641页

作者： LIU Hong SUN Yuling LI Yuanyuan School of Information Science and Engineering Shandong Normal University Jinan 250014 China Shandong Provincial Key Laboratory for Novel Distributed Computer Software Technology Jinan 250014 China

Modeling and simulating group behaviours have been an active research topic in the field of computer animation and game. This paper presents some novel approaches for supporting entity modeling and path generation in crowd simulation. It analyses related work about crowd simulation first. Then, an entity modeling approach based on CGA (Cellular genetic algorithm) and NURBS (Non uniform relational B splines) technologies is presented. Next, following the analysis to PSO (Particle swarm optimization) and ABC (Artificial bee colony) algorithms, a crowd path generative approach based on ABC-PSO is put forward. After that, a simulating example of crowd cohesion and performance comparison are exhibited for showing the efficiency of the algorithms. Finally, the current work is summarized and an outlook for the future work is given.

关键词：仿真建模生成方法人群计算智能路径遗传算法实体建模计算机动画 Crowd simulation, Entity modeling, Cellular genetic algorithm, PSO (Particle swarm optimization) algorithm, ABC algorithm Path generation.

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