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5th International Seminar on artificial intelligence, Networking and Information technology, AINIT 2024

作者： Xiang, Jieyu Yan, Xin School of Computer Science and Artificial Intelligence Wuhan University of Technology Wuhan China

ISBN: (纸本)9798350385557

This paper proposes a routing optimization approach that integrates genetic algorithms (GA) for multi-modal multi-objective optimization problems (MMOPs) with reinforcement learning (RL) to enhance network routing efficiency and throughput while reducing latency and packet loss rates. Our method employs genetic algorithms to generate multiple high-quality paths for each node pair, forming an action space for reinforcement learning. This accelerates the RL learning process and increases the flexibility of actions. Through the adaptability of RL, the routing strategy is further optimized to improve routing efficiency and guarantee network service quality. © 2024 IEEE.

关键词： Reinforcement learning

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Research on predicting legal judgments by incorporating key information 7

Research on predicting legal judgments by incorporating key ...

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7th International Conference on computer Information science and Application technology, CISAT 2024

作者： Xu, Fan Liu, Wei Guo, Kao Wang, Zihao Wuhan Institute of Technology School of Computer Science & Engineering School of Artificial Intelligence Wuhan China

ISBN: (纸本)9798350375107

The prediction of legal judgments is based on the description of case facts to predict the final charges. Through judgment prediction technology, the judicial system can handle a large number of cases more efficiently and provide more accurate judgment results. However, in reality, the types of charges are numerous and complex. To address the issues of severe data imbalance and easily confusable charges, this paper proposes a legal judgment model that incorporates key information. Firstly, it combines the ALBERT (A Lite BERT) pre-trained model and the bidirectional Gated Recurrent Unit (GRU) model to encode cases, efficiently capturing the contextual semantic information in case descriptions. Then, the concept of key information is introduced, using attention mechanisms and statistical methods to extract two distinguishable parts of key information from the cases, which are then concatenated with the case encoding for the final prediction. Additionally, category classification weights are employed to enhance the model's ability to predict low-frequency samples. The proposed method has achieved good results on real legal judgment datasets. © 2024 IEEE.

关键词： Prediction models

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Token Masked Pose Transformers Are Efficient Learners

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computers, Materials & Continua 2025年第5期83卷 2735-2750页

作者： Xinyi Song Haixiang Zhang Shaohua Li School of Computer Science and Technology Zhejiang Sci-Tech UniversityHangzhou310018China College of Artificial Intelligence Nankai UniversityTianjin300350China

In recent years,Transformer has achieved remarkable results in the field of computer vision,with its built-in attention layers effectively modeling global dependencies in images by transforming image features into token ***,Transformers often face high computational costs when processing large-scale image data,which limits their feasibility in real-time *** address this issue,we propose Token Masked Pose Transformers(TMPose),constructing an efficient Transformer network for pose *** network applies semantic-level masking to tokens and employs three different masking strategies to optimize model performance,aiming to reduce computational *** results show that TMPose reduces computational complexity by 61.1%on the COCO validation dataset,with negligible loss in ***,our performance on the MPII dataset is also *** research not only enhances the accuracy of pose estimation but also significantly reduces the demand for computational resources,providing new directions for further studies in this field.

关键词： Pattern recognition image processing neural network pose transformer

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Enhancing data Forwarding Efficiency in SIoT with Multidimensional Social Relations

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computers, Materials & Continua 2024年第1期78卷 1095-1113页

作者： Fang Xu Songhao Jiang Yi Ma Manzoor Ahmed Zenggang Xiong Yuanlin Lyu School of Computer and Information Science Hubei Engineering UniversityXiaogan432000China School of Computer Science and Information Engineering Hubei UniversityWuhan430062China Artificial Intelligence Industrial Technology Research Institute Hubei Engineering UniversityXiaogan432000China

Effective data communication is a crucial aspect of the Social Internet of Things(SIoT)and continues to be a significant research *** paper proposes a data forwarding algorithm based on Multidimensional Social Relations(MSRR)in SIoT to solve this *** proposed algorithm separates message forwarding into intra-and cross-community forwarding by analyzing interest traits and social connections among *** new metrics are defined:the intensity of node social relationships,node activity,and community *** the community,messages are sent by determining which node is most similar to the sender by weighing the strength of social connections and node *** a node performs cross-community forwarding,the message is forwarded to the most reasonable relay community by measuring the node activity and the connection between *** proposed algorithm was compared to three existing routing algorithms in simulation *** indicate that the proposed algorithmsubstantially improves message delivery efficiency while lessening network overhead and enhancing connectivity and coordination in the SIoT context.

关键词： SIoT data forwarding social attributes social relations community

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A Novel CAPTCHA Recognition System Based on Refined Visual Attention

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computers, Materials & Continua 2025年第4期83卷 115-136页

作者： Zaid Derea Beiji Zou Xiaoyan Kui Monir Abdullah Alaa Thobhani Amr Abdussalam School of Computer Science and Engineering Central South UniversityChangsha410083China College of Computer Science and Information Technology Wasit UniversityWasit52001Iraq Department of Computer Science and Artificial Intelligence College of Computing and Information TechnologyUniversity of BishaBisha67714Saudi Arabia Electronic Engineering and Information Science Department University of Science and Technology of ChinaHefei230026China

Improving website security to prevent malicious online activities is crucial,and CAPTCHA(Completely Automated Public Turing test to tell computers and Humans Apart)has emerged as a key strategy for distinguishing human users from automated ***-based CAPTCHAs,designed to be easily decipherable by humans yet challenging for machines,are a common form of this ***,advancements in deep learning have facilitated the creation of models adept at recognizing these text-based CAPTCHAs with surprising *** our comprehensive investigation into CAPTCHA recognition,we have tailored the renowned UpDown image captioning model specifically for this *** approach innovatively combines an encoder to extract both global and local features,significantly boosting the model’s capability to identify complex details within CAPTCHA *** the decoding phase,we have adopted a refined attention mechanism,integrating enhanced visual attention with dual layers of Long Short-Term Memory(LSTM)networks to elevate CAPTCHA recognition *** rigorous testing across four varied datasets,including those from Weibo,BoC,Gregwar,and Captcha 0.3,demonstrates the versatility and effectiveness of our *** results not only highlight the efficiency of our approach but also offer profound insights into its applicability across different CAPTCHA types,contributing to a deeper understanding of CAPTCHA recognition technology.

关键词： Text-based CAPTCHA recognition refined visual attention web security computer vision

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An adaptive dynamic community detection algorithm based on multi-objective evolutionary clustering

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International Journal of Intelligent Computing and Cybernetics 2024年第1期17卷 143-160页

作者： Wenxue Wang Qingxia Li Wenhong Wei School of ComputerScience and Technology Dongguan University of TechnologyDongguanChina School of Artificial Intelligence Dongguan City UniversityDongguanChina Department of Computer Science and Technology Dongguan University of TechnologyDongguanChina

Purpose:Community detection of dynamic networks provides more effective information than static network community detection in the real *** mainstream method for community detection in dynamic networks is evolutionary clustering,which uses temporal smoothness of community structures to connect snapshots of networks in adjacent time ***,the error accumulation issues limit the effectiveness of evolutionary *** the multi-objective evolutionary approach can solve the issue of fixed settings of the two objective function weight parameters in the evolutionary clustering framework,the traditional multi-objective evolutionary approach lacks ***/methodology/approach:This paper proposes a community detection algorithm that integrates evolutionary clustering and decomposition-based multi-objective optimization *** this approach,a benchmark correction procedure is added to the evolutionary clustering framework to prevent the division results from ***:Experimental results demonstrate the superior accuracy of this method compared to similar algorithms in both real and synthetic dynamic ***/value:To enhance the clustering results,adaptive variances and crossover probabilities are designed based on the relative change amounts of the subproblems decomposed by MOEA/D(A Multiobjective Optimization Evolutionary Algorithm based on Decomposition)to dynamically adjust the focus of different evolutionary stages.

关键词： Dynamic community network Evolutionary clustering Multi-objective optimization Adaptive

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An attention-based teacher-student model for multivariate short-term landslide displacement prediction incorporating weather forecast data

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Journal of Mountain science 2024年第8期21卷 2739-2753页

作者： CHEN Jun HU Wang ZHANG Yu QIU Hongzhi WANG Renchao Sichuan Artificial Intelligence Research Institute Yibin 644000China School of Computer Science and Engineering University of Electronic Science and Technology of ChinaChengdu 611731China School of Architecture and Civil Engineering Chengdu UniversityChengdu 610106China

Predicting the displacement of landslide is of utmost practical importance as the landslide can pose serious threats to both human life and ***,traditional methods have the limitation of random selection in sliding window selection and seldom incorporate weather forecast data for displacement prediction,while a single structural model cannot handle input sequences of different lengths at the same *** order to solve these limitations,in this study,a new approach is proposed that utilizes weather forecast data and incorporates the maximum information coefficient(MIC),long short-term memory network(LSTM),and attention mechanism to establish a teacher-student coupling model with parallel structure for short-term landslide displacement *** MIC,a suitable input sequence length is selected for the LSTM *** investigate the influence of rainfall on landslides during different seasons,a parallel teacher-student coupling model is developed that is able to learn sequential information from various time series of different *** teacher model learns sequence information from rainfall intensity time series while incorporating reliable short-term weather forecast data from platforms such as China Meteorological Administration(CMA)and Reliable Prognosis(https://***)to improve the model’s expression capability,and the student model learns sequence information from other time *** attention module is then designed to integrate different sequence information to derive a context vector,representing seasonal temporal attention ***,the predicted displacement is obtained through a linear *** proposed method demonstrates superior prediction accuracies,surpassing those of the support vector machine(SVM),LSTM,recurrent neural network(RNN),temporal convolutional network(TCN),and LSTM-Attention *** achieves a mean absolute error(MAE)of 0.072 mm,root mean square error(RMSE)of 0.096 mm,and pearson correlation coefficients(PC

关键词： Landslide prediction MIC LSTM Attention mechanism Teacher Student model Prediction stability and interpretability

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An optimized ensemble model based on meta-heuristic algorithms for effective detection and classification of breast tumors

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Neural Computing and Applications 2025年第6期37卷 4881-4894页

作者： Saber, Abeer Elbedwehy, Samar Awad, Wael A. Hassan, Esraa Information Technology Department Faculty of Computers and Artificial Intelligence Damietta University Damietta34517 Egypt Department of Data Science Faculty of Artificial Intelligence Kafrelsheikh University Kafrelsheikh33511 Egypt Department of Computer Science Faculty of Computers and Artificial Intelligence Damietta University Damietta34517 Egypt Faculty of Artificial Intelligence Kafrelsheikh University Kafrelsheikh33511 Egypt

One of the most common cancers among women worldwide is breast cancer (BC), and early diagnosis can save lives. Early detection of BC increases the likelihood of a successful outcome by enabling treatment to start sooner. Even in areas without access to a specialist physician, machine learning (ML) aids in early BC detection. The medical imaging community is becoming more interested in using ML, and deep learning (DL) to increase the accuracy of cancer screening. Many disease-related data are sparse. However, for DL models to perform well, a large amount of data is required. Because of this, the DL models that are currently in use on medical images are not as effective as they could be. Convolutional neural network (CNN) models have recently gained popularity in the medical industry, and they perform admirably in terms of high performance and robustness at image classification. The proposed method classifies data using ensemble pre-trained models such as the dense convolutional network (DenseNet)-121 and EfficientNet-B5 feature extractor networks, as well as the support vector machine for classification. Using a modified meta-heuristic optimizer, the selected pre-trained CNN hyperparameters were optimized to improve the performance. The experimental results for the presented model on the INbreast dataset show that the EfficientNet-B5 model is effective for BC classification, with overall accuracy, sensitivity, specificity, precision, and area under the ROC curve (AUC) values of 99.9%, 99.9%, 99.8%, 99.1%, 1.0, respectively. © The Author(s) 2024.

关键词： Adversarial machine learning

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Point-Voxel Based Geometry-Adaptive Network for 3D Point Cloud Analysis

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Journal of computer science & technology 2024年第5期39卷 1167-1179页

作者： Tian-Meng Zhao Hui Zeng Bao-Qing Zhang Hong-Min Liu Bin Fan Beijing Engineering Research Center of Industrial Spectrum Imaging School of Automation and Electrial Engineering University of Science and Technology BeijingBeijing 100083China Shunde Innovation School University of Science and Technology BeijingFoshan 528399China Beijing Institute of Electronic System Engineering Beijing 100854China School of Intelligence Science and Technology University of Science and Technology BeijingBeijing 100083China Institute of Artificial Intelligence University of Science and Technology BeijingBeijing 100083China

Point cloud analysis is challenging because of the unordered and irregular data structure of point *** describe geometric information in point clouds,existing methods mainly use convolution,graph,and attention operations to construct sophisticated local aggregation *** operators work well in extracting local information but bring unfavorable inference latency due to high computation *** solve the above problem,this paper presents a novel point-voxel based geometry-adaptive network(PVGANet),which combines multiple representations of point and voxel to describe the point cloud from different granularities and can obtain features of different scales *** extract fine-grained geometric features,we design the position-adaptive pooling operator,which uses point pairs’relative position and feature similarity to weight and aggregate the point features at local areas of point *** extract coarse-grained local features,we design a depth-wise convolution operator,which conducts the depth-wise convolution on voxel *** an easy addition,fine-grained geometric and coarse-grained local features can be fused,and we can use the geometry-adaptive fused features to complete the efficient shape analysis of point clouds,such as shape classification and part *** experiments on ModelNet40,ScanObjectNN,and ShapeNet Part benchmarks demonstrate that our PVGANet achieves competitive performance compared with the related methods.

关键词： point cloud geometric feature multi-representation part segmentation shape classification

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SG-NeRF:Sparse-Input Generalized Neural Radiance Fields for Novel View Synthesis

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Journal of computer science & technology 2024年第4期39卷 785-797页

作者： Kuo Xu Jie Li Zhen-Qiang Li Yang-Jie Cao School of Cyber Science and Engineering Zhengzhou UniversityZhengzhou 450002China Intelligent Big Data System(iBDSys)Lab Shanghai Jiao Tong UniversityShanghai 200240China School of Computer and Artificial Intelligence Zhengzhou UniversityZhengzhou 450001China

Traditional neural radiance fields for rendering novel views require intensive input images and pre-scene optimization,which limits their practical *** propose a generalization method to infer scenes from input images and perform high-quality rendering without pre-scene optimization named SG-NeRF(Sparse-Input Generalized Neural Radiance Fields).Firstly,we construct an improved multi-view stereo structure based on the convolutional attention and multi-level fusion mechanism to obtain the geometric features and appearance features of the scene from the sparse input images,and then these features are aggregated by multi-head attention as the input of the neural radiance *** strategy of utilizing neural radiance fields to decode scene features instead of mapping positions and orientations enables our method to perform cross-scene training as well as inference,thus enabling neural radiance fields to generalize for novel view synthesis on unseen *** tested the generalization ability on DTU dataset,and our PSNR(peak signal-to-noise ratio)improved by 3.14 compared with the baseline method under the same input *** addition,if the scene has dense input views available,the average PSNR can be improved by 1.04 through further refinement training in a short time,and a higher quality rendering effect can be obtained.

关键词： Neural Radiance Fields(NeRF) multi-view stereo(MVS) new view synthesis(NVS)

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