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8th International Conference on Electronic Information Technology and computer engineering, EITCE 2024

作者： Hu, Jurong Xing, Yanxiao Li, Yuxiao School of Information Science and Engineering Hohai University Jiangsu Nanjing China School of Computer and Software Hohai University Jiangsu Nanjing China

ISBN: (纸本)9798400710094

Aiming at the problem that the iForest algorithm is not sensitive enough to local anomalies and produces a large number of false alarms in the detection results on some low sea state datasets, this paper proposes the iForest algorithm based on an improved splitting strategy. The algorithm introduces the Gini impurity, incorporates the Gini impurity parameter into the splitting strategy, and is more targeted in the selection of splitting attributes and splitting points, thus better adapting to the problem of detecting weak targets under different sea states, and has an excellent performance of high accuracy, low time duration, and low false alarm rate compared with the traditional methods in anomaly detection. © 2024 Copyright held by the owner/author(s).

关键词： Optimization algorithms

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A Question-and-Answer Scheme of Clinical Practice Guidelines Based on SDA 24

A Question-and-Answer Scheme of Clinical Practice Guidelines...

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5th International Symposium on Artificial Intelligence for Medicine Science, ISAIMS 2024

作者： Zhang, Zhizheng Yang, Qian Du, Yan School of Computer Science and Engineering Southeast University Jiangsu Nanjing China Joint Graduate School Southeast University Jiangsu Nanjing China School of Software Engineering Southeast University Jiangsu Nanjing China

ISBN: (纸本)9798400717826

This paper proposes a medical diagnosis and treatment knowledge question and answer scheme based on clinical practice guidelines, aiming at the need of standardization of medical process and popularization of medical knowledge. The scheme realizes computer intelligent diagnosis and treatment assistance through diagnosis and treatment knowledge base construction, problem analysis, information retrieval and answer generation. The SDA*(State-DecisionAction) modeling method and question formal definition for hypertension diagnose and treatment are proposed, and the answers are generated based on the answer set program (ASP). For the transformation of natural language and ASP rules, large language model (LLM) technology is adopted to automatically identify question terms, generate ASP rules and process the generated answers. To verify the scheme’s ability to generate reliable answers, accuracy for problem classification and ASP rule generations are confirmed. The experimental results show that the scheme could provide the ability to solve medical problems based on the knowledge of clinical practice guidelines. © 2024 Copyright held by the owner/author(s).

关键词： Hyperthermia therapy

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Reflective clothing detection based on YOLOv5s fused with SE attention mechanism

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International Journal of Reasoning-based Intelligent Systems 2025年第1期17卷 9-16页

作者： Wang, Yuanyuan Gu, Dingyun Zhu, Yemeng Zhang, Zhihan Cao, Jiahui Zhang, Haiyan School of Computer and Software Engineering Huaiyin Institute of Technology Huaian223001 China Engineering Laboratory of Mobile Interconnection Technology for Internet of Things Jiangsu Province Huai’an Huaian City223001 China

This paper proposes a YOLOv5s deep learning algorithm incorporating the SE attention mechanism to address the issue of workers failing to wear reflective clothing on duty, which has resulted in casualties from time to time. The YOLOv5s model is used to train the reflective clothing dataset obtained by collecting images of construction sites, and the SE attention mechanism module is added to the network structure of YOLOv5 for improved performance. The reflective clothing detection algorithm is then deployed to the web for real-time detection. The experimental results show that the algorithm achieves mAP value of 96.70% with the SE attention module, resulting in an accuracy improvement of 1.09% compared to the model without the SE module. This enables practical applications to reduce the incidence of construction accidents at construction sites. Coрyright © 2025 Inderscience Enterрrises Ltd.

关键词： attention mechanism flask reflective clothing detection vue YOLOv5

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A Method for Extracting Small Water Areas Based on an Improved FPN Network 19

A Method for Extracting Small Water Areas Based on an Improv...

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19th International Conference on Ubiquitous Information Management and Communication, IMCOM 2025

作者： Yu, Yufeng Wang, Meixiang Zhao, Qun Wang, Yong Chen, Qingqing Department of Computer and Software Hohai University Jiangsu Nanjing211108 China School of Computer Engineering Nanjing Institute of Technology Jiangsu Nanjing211167 China Bank of Nanjing Co. Nanjing Jiangsu211800 China Aerosun Corporation Information Center Jiangsu Nanjing China

ISBN: (纸本)9798331507817

Small water areas such as river branches, lakes, and ponds are vital components of aquatic ecosystems closely linked to human activities. Extracting small water areas from remote sensing images holds significant importance in water resource planning, investigation, sustainable water resource management, and environmental protection. However, the current small water area extraction methods based on remote sensing often encounter the problems of small target, few features, difficult localization and easy loss of edge features. Therefore, this paper proposes a small water area extraction model based on IFPN_BEM network. The model leverages a Transformer Encoder module with deformable attention mechanisms to globally model deep features of the FPN network and optimizes small water area edges using the BEM module, which further improves the extraction accuracy of small water areas. Experimental results on the GID dataset demonstrate that this model provides more complete and accurate extraction of small water areas, achieving precision, F1 score, and IoU accuracy metrics of 81.3%, 84.4%, and 73.4% respectively. These metrics show improvements of 9.5%, 8.2%, and 8.5% over the original FPN network, outperforming UNet, DeepLabV3+, and the improved FPN + PointRend networks across all metrics. © 2025 IEEE.

关键词： Lakes

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Collaborative graph contrastive learning for recommendation

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Journal of Intelligent and Fuzzy Systems 2025年第1-2期48卷 33-46页

作者： Wei, Tao Yang, Changchun Zheng, Yanqi Zhang, Jingxue School of Computer Science and Artificial Intelligence Aliyun School of Big Data School of Software Changzhou University Jiangsu Changzhou China School of Microelectronics and Control Engineering Changzhou University Jiangsu Changzhou China

Recently, Graph Neural Networks (GNNs) using aggregating neighborhood collaborative information have shown effectiveness in recommendation. However, GNNs-based models suffer from over-smoothing and data sparsity problems. Due to its self-supervised nature, contrastive learning has gained considerable attention in the field of recommendation, aiming at alleviating highly sparse data. Graph contrastive learning models are widely used to learn the consistency of representations by constructing different graph augmentation views. Most current graph augmentation with random perturbation destroy the original graph structure information, which mislead embeddings learning. In this paper, an effective graph contrastive learning paradigm CollaGCL is proposed, which constructs graph augmentation by using singular value decomposition to preserve crucial structure information. CollaGCL enables perturbed views to effectively capture global collaborative information, mitigating the negative impact of graph structural perturbations. To optimize the contrastive learning task, the extracted meta-knowledge was propagate throughout the original graph to learn reliable embedding representations. The self-information learning between views enhances the semantic information of nodes, thus alleviating the problem of over-smoothing. Experimental results on three real-world datasets demonstrate the significant improvement of CollaGCL over state-of-the-art methods. © 2025 – IOS Press. All rights reserved.

关键词： Self-supervised learning

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Optimizing Secure Multi-User ISAC Systems With STAR-RIS: A Deep Reinforcement Learning Approach for 6G Networks

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IEEE Access 2025年 13卷 31472-31484页

作者： Kamal, Mian Muhammad Zain Ul Abideen, Syed Al-Khasawneh, M.A. Alabrah, Amerah Sohail Ahmed Larik, Raja Irfan Marwat, Muhammad Southeast University School of Electronic Science and Engineering Jiangning Jiangsu Nanjing211189 China Qingdao University College of Computer Science and Technology Qingdao266071 China Al-Ahliyya Amman University Hourani Center for Applied Scientific Research Amman19111 Jordan Skyline University College School of Computing University City Sharjah Sharjah United Arab Emirates King Saud University College of Computer and Information Science Department of Information Systems Riyadh11543 Saudi Arabia Ilma University Department of Computer Science Sindh Karachi75190 Pakistan University of Science and Technology Bannu Department of Software Engineering Bannu28100 Pakistan

The rapid evolution of wireless communication technologies and the increasing demand for multi-functional systems have led to the emergence of integrated sensing and communication (ISAC) as a key enabler for future 6G networks. Simultaneously transmitting and reflecting reconfigurable intelligent surfaces (STAR-RISs) have recently garnered significant attention for their ability to enhance signal coverage and improve system efficiency. This paper investigates a STAR-RIS-assisted ISAC system designed to secure communication for multiple legitimate users (LUs) while safeguarding against multiple eavesdroppers (Eves). By jointly optimizing the base station (BS) transmit beamforming, STAR-RIS transmission and reflection coefficients, and receive filters, the proposed framework aims to maximize the long-term average secrecy rate for all LUs. Constraints are imposed to ensure minimum echo signal-to-noise ratios (SNRs) for sensing and meet the achievable rate requirements of LUs. To address the inherent complexity of this non-convex problem, two deep reinforcement learning (DRL) algorithms are proposed. Numerical results demonstrate that the proposed system achieves significant improvements in secrecy rate compared to conventional RIS setups. This work provides a scalable and efficient approach for secure multi-user ISAC systems, making it highly relevant for future 6G networks, smart cities, and IoT applications. © 2025 The Authors.

关键词： Deep reinforcement learning

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A spectrum-enhanced attention model for semantic segmentation of remote sensing images

A spectrum-enhanced attention model for semantic segmentatio...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Li, Xin Xu, Feng Tao, Feifei Tong, Yao Lyu, Xin Zhong, Jianyi Kaup, André College of Computer Science and Software Engineering Hohai University Nanjing China Key Laboratory of Water Big Data Technology Ministry of Water Resources Hohai University Nanjing China School of Computer Engineering Jiangsu Ocean University Lianyungang China School of Artificial Intelligence and Information Technology Nanjing University of Chinese Medicine Nanjing China Multimedia Communications and Signal Processing Friedrich-Alexander University Erlangen-Nuremberg Erlangen Germany

ISBN: (纸本)9798350368741

Semantic segmentation of remote sensing images (RSIs) is essential for applications such as environmental monitoring, urban planning, and disaster management. Convolutional Neural Networks (CNNs) and their variants struggle to capture comprehensive spectral context for learning discriminative representations. In this paper, we propose a Spectrum-Enhanced Network (SPENet) that leverages the Frequency Transformer Block (FTB) to capture rich spectral context. FTB integrates Spectrum-Enhanced Attention (SEA) with Multi-Head Frequency Self-Attention (MH-FSA), incorporating more informative contextual cues. Specifically, SEA aggregates spectral statistics through covariance matrix normalization before applying channel-wise attention. By projecting feature maps onto the frequency domain, MH-FSA provides the network with a broader context, extending beyond the low-frequency focus of standard self-attention mechanisms. Extensive experiments on the ISPRS Potsdam and LoveDA datasets show that SPENet significantly outperforms state-of-the-art methods. Besides, the proposed SEA module notably rises average F1-score/overall accuracy/mean insert over union wiht more than 2.5/2.6%/2.3%, as demonstrated by ablation study. © 2025 IEEE.

关键词： frequency transformer remote sensing images Semantic segmentation spectrum enhancement

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FedVC: Virtual Clients for Federated Learning with Imbalanced Label Distribution 24

FedVC: Virtual Clients for Federated Learning with Imbalance...

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3rd International Conference on Algorithms, Data Mining, and Information Technology, ADMIT 2024

作者： Chen, Zhouguo Chen, Hongda Liu, Shuai Liu, Yongdong Schoolof Computer Science and Engineering Southeast University Jiangsu Nanjing China The 30th Research Institute of China Electronics Technology Group Corporation Sichuan Chengdu China School of Software Engineering Xian Jiaotong University Shaanxi Xian China China Industrial Control Systems Cyber Emergency Response Team Beijing China

ISBN: (纸本)9798400718120

Federated Learning (FL) has gained considerable attention for collaborative training in big data analysis, particularly in terms of privacy and communication constraints. Despite its promising advantages, FL faces the challenge of dealing with imbalanced label distribution on edge devices, which complicates the learning task. In literature, numerous studies have attempted to tackle this issue by introducing additional hyperparameters, which can complicate and prolong the process of model training and tuning. This complexity may potentially lead to overfitting and limited performance on new datasets. In this paper, we present a new Federated Learning framework called FedVC, which introduces virtual clients to minimize hyperparameter reliance. FedVC aims to bridge the gap between balanced and imbalanced label distribution. Compared with traditional federated learning frameworks, the proposed FedVC model designs a dynamically balanced dataset by utilizing virtual clients, which is proposed for the first time in this work. These virtual clients determine which part of the data should be used in one single training round and manage backpropagation timing for local real clients. The balanced dataset promotes greater stability in local models, thereby enhancing the overall stability of the training process. The experimental results show that FedVC outperforms classical FedAvg and the compared baseline methods for the data with imbalanced label distribution. Especially, FedVC exhibits more stable and accurate results than alternative approaches in highly heterogeneous settings. The source code is available at https://***/aikedaerC/FedVC. © 2024 Copyright held by the owner/author(s).

关键词： Federated learning

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A Method for Extracting Small Water Areas Based on an Improved FPN Network

A Method for Extracting Small Water Areas Based on an Improv...

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International Conference on Ubiquitous Information Management and Communication (IMCOM)

作者： Yufeng Yu Meixiang Wang Qun Zhao Yong Wang Qingqing Chen Department of Computer and Software Hohai University Nanjing Jiangsu China School of Computer Engineering Nanjing Institute of Technology Nanjing Jiangsu China Bank of Nanjing Co. Jiangsu Nanjing China Aerosun Corporation Information Center Nanjing Jiangsu China

ISBN: (数字)9798331507817

ISBN: (纸本)9798331507824

关键词： Measurement Deformable models Accuracy Attention mechanisms Image edge detection Feature extraction Transformers Data mining Water resources Remote sensing

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Corporate Fraud Detection in Rich-yet-Noisy Financial Graph

arXiv

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arXiv 2025年

作者： Wang, Shiqi Zhang, Zhibo Fang, Libing Nguyen, Cam-Tu Li, Wenzhon School of Artificial Intelligence Nanjing University Jiangsu Nanjing China School of Computer Science and Technology Nanjing University Jiangsu Nanjing China School of Management and Engineering Nanjing University Jiangsu Nanjing China State Key Laboratory for Novel Software Technology Nanjing University Jiangsu Nanjing China

Corporate fraud detection aims to automatically recognize companies that conduct wrongful activities such as fraudulent financial statements or illegal insider trading. Previous learning-based methods fail to effectively integrate rich interactions in the company network. To close this gap, we collect 18-year financial records in China to form three graph datasets with fraud labels. We analyze the characteristics of the financial graphs, highlighting two pronounced issues: (1) information overload: the dominance of (noisy) non-company nodes over company nodes hinders the message-passing process in Graph Convolution Networks (GCN);and (2) hidden fraud: there exists a large percentage of possible undetected violations in the collected data. The hidden fraud problem will introduce noisy labels in the training dataset and compromise fraud detection results. To handle such challenges, we propose a novel graph-based method, namely, Knowledge-enhanced GCN with Robust Two-stage Learning (KeGCNR), which leverages Knowledge Graph Embeddings to mitigate the information overload and effectively learns rich representations. The proposed model adopts a two-stage learning method to enhance robustness against hidden frauds. Extensive experimental results not only confirm the importance of interactions but also show the superiority of KeGCNR over a number of strong baselines in terms of fraud detection effectiveness and robustness. Copyright © 2025, The Authors. All rights reserved.

关键词： Graph embeddings

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