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11th International Joint Conference on Rough Sets, IJCRS 2025

作者： Fang, Yu Li, Tianrui Min, Fan School of Computing and Artificial Intelligence Southwest Jiaotong University Chengdu611756 China School of Computer Science and Software Engineering Southwest Petroleum University Chengdu610500 China

ISBN: (纸本)9783031927409

In tackling the issues of overfitting and limited generalization in machine learning models, especially with imbalanced and limited data, oversampling methods have been widely used to enhance model discrimination by generating synthetic minority samples. However, these methods often struggle to capture the expressive nuances of the minority class due to noisy interference. In response to this challenge, we propose a three-way oversampling (3WOS) method that partitions data into three regions via density clustering, then synthesizes samples in purified regions to avoid noise. The 3WOS operates in two stages: first, a three-way density clustering (3WDC) process uses density ratios to divide data and identify the FringeRegion (Fr) as a source of benchmark representative samples, aiding in similar data recognition and noise mitigation. Second, a data synthesis strategy creates a purified region from the representative samples, where the FringeRegion provides meaningful, discriminative data for synthesis through linear interpolation. This approach avoids data redundancy and overlap, ensuring a more robust minority representation. Extensive experiments validate 3WOS effectiveness, demonstrating significant improvements in model performance, particularly in enhancing minority class representation. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Cluster analysis

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DPFedSAM-Meas: Comparison Of Differential Privacy Federated Learning In Medical Image Classification

DPFedSAM-Meas: Comparison Of Differential Privacy Federated ...

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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Jiang, Shunrong Wang, Zhi He, Zixuan Li, Yonggang Li, Xiaofan Chi, Haotian Du, Xiaojiang China University of Mining and Technology School of Computer Science Xuzhou221116 China Jiangsu Jingchuang Electric CO. LTD China Shanxi University School of Automation and Software Engineering Taiyuan030006 China Stevens Institute of Technology Department of Electrical and Computer Engineering HobokenNJ United States

ISBN: (纸本)9798350351255

Machine learning is widely used in medical image classification tasks. However, medical images often exhibit uneven distribution and high sensitivity to noise. A feasible solution involves using federated learning (FL) with differential privacy (DP), a distributed training method that protects patient privacy. In this work, many studies have proposed improved solutions for localized differential-private federated learning (DP-FL) frameworks. However, these studies are isolated, which would be detrimental to privacy practitioners in designing and using the algorithms. To provide a comprehensive analysis of these algorithms, we propose DPFedSAM-Meas, as a framework for comprehensive utility analysis of DP-FL. In this framework, we employed the state-of-the-art federated learning framework FedSAM. Moreover, we categorize DP algorithms into Laplace DP and Gaussian DP by the underlying DP mechanisms, and into Gradient DP and Parameter DP by the DP position in FL train. DPFedSAM-Meas allows a comparative analysis of these four DP techniques, measuring their model utility, privacy leakage, and overhead when FL uses different network structures. Finally, we evaluate DPFedSAM-Meas on datasets of Pneumonia, Blood, and Path, aiming to investigate the performance of different DP techniques on mainstream deep learning algorithms, including Convolutional Neural Networks (CNN) and Vision Transformers (ViT). © 2024 IEEE.

关键词： Federated learning

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Distributed Service Placement in Ultra-Dense Edge Computing: A Game-Theoretical Approach 23

Distributed Service Placement in Ultra-Dense Edge Computing:...

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23rd IEEE International Conference on High Performance Computing and Communications, 7th IEEE International Conference on Data science and Systems, 19th IEEE International Conference on Smart City and 7th IEEE International Conference on Dependability in Sensor, Cloud and Big Data Systems and Applications, HPCC-DSS-SmartCity-DependSys 2021

作者： He, Junyi Zhang, Di Li, Xishuo Zhou, Yuezhi Zhang, Yaoxue School of Software Engineering Beijing Jiaotong University China School of Science and Engineering The Chinese University of Hong Kong Shenzhen China School of Computer Science and Engineering Beihang University China Tsinghua University Department of Computer Science and Technology China

ISBN: (纸本)9781665494571

Ultra-dense mobile edge computing (MEC) is expected as an effective approach to provide services with ultra-low latency and massive connectivity, which is extremely important for delay-sensitive applications. In this paper, we study service instance (SI) placement for delay-sensitive applications in ultra-dense MEC, which decides where to place applications and the required data (programming codes and datasets). In general, the SI placement problem is a combinatorial optimization and usually does not have low-complexity optimal solutions. Existing centralized policies are difficult to implement due to the high complexity, especially when edge servers are densely deployed in the ultra-dense network (UDN). To design an approach that can make SI placement decisions quickly and efficiently, this paper investigates a distributed strategy for multi-user multi-server edge systems in UDN. We first formulate the SI placement problem as a non-linear combinatorial optimization, which is proven to be NP-hard to compute a centralized optimal solution. Then, we propose an uncooperative game to design a low-complexity distributed algorithm. We further analyze the properties of the game and prove the existence of pure strategy Nash Equilibrium. A polynomial-complexity distributed algorithm is proposed to get the Nash Equilibrium. Theoretical analysis shows the algorithm's convergence and the bound of the price of anarchy (PoA), which serves as a performance guarantee in the worst-case scenarios compared to the global optimum. Numerical results validate the convergence and near-optimal performance of the proposed distributed algorithm. © 2021 IEEE.

关键词： Performance evaluation Games Reinforcement learning Nash equilibrium Servers Distributed algorithms Ultra-dense networks

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An Empirical Comparison of Machine and Deep Learning Algorithms for Predicting Maternal Health Risk 5th

An Empirical Comparison of Machine and Deep Learning Algorit...

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5th EAI International Conference on Cognitive Computing and Cyber Physical Systems, IC4S 2024

作者： N, Srinivasa Gupta Valarmathi, B. Kavitha, A. Gopini, Sai Bhuvan Seela, Hari Kumar Department of Manufacturing Engineering School of Mechanical Engineering Vellore Institute of Technology Tamil Nadu Vellore India Department of Software and Systems Engineering School of Computer Science Engineering and Information Systems Vellore Institute of Technology Tamil Nadu Vellore India Department of Mathematics School of Advanced of Sciences Vellore Institute of Technology Tamil Nadu Vellore India

ISBN: (纸本)9783031770777

Maternal health is among the greatest challenges in the world, especially in rural areas as there lack medical practitioners, they do not have easily accessible publics clinics and transport is difficult. Therefore, high rates of maternal as well as infant morbidity and mortalities are recorded. This research utilizes Artificial Intelligence (AI) with machine learning algorithms to forecast and address maternal health hazards right at their onset stage. The current research utilizes the concept of AI along with many Machine Learning (ML) methods like the Ensemble Learning Model (ELM), Random Forest (RF), K-Nearest Neighbour (KNN), Decision-Tree (DT), XG-Boost (XGB), Cat Boost (CB), and Gradient Boosting (GB), along with Synthetic Minority Over-sampling Technique (SMOTE) algorithm used for dealing with the problem class imbalance within the data set. SMOTE algorithm is utilized for the dataset balancing process. The handling system involves refining data preprocessing with the help of feature engineering and robust data cleaning which makes sure that anomalies do not erode the reliability of the predictive model. The existing methods [1] used RF (90%), DT (87%), XGB (85%), CB (86%), and GB (81%) algorithms and were compared with the accuracies of the proposed models like Logistic Regression (LR), Ensemble Learning Bagging (ELB), Ensemble Learning Stacking (ELS), Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU). The existing methods used only imbalance dataset. The accuracies of the proposed models with using SMOTE algorithm (balanced dataset) are LR (61.33%), KNN (81%), ELB (92.33%), ELS (90.66%) CNN (40.67%), RNN (59.67%), LSTM (54%), GRU (56%) respectively. Among these methods, ELB achieved 92.33% of accuracy with using SMOTE algorithm using imbalanced dataset. Whereas the accuracies of the proposed models without using SMOTE algorithm (imbalanced dataset) are LR (66.09%), KNN (68.47%)

关键词： Convolutional neural networks

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A Privacy Protection Task Offloading Algorithm in MEC

A Privacy Protection Task Offloading Algorithm in MEC

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International Conference on computer Supported Cooperative Work in Design

作者： Yun Deng Haihua Tang School of Computer Science and Engineering / School of Software Guangxi Normal University Guilin China

ISBN: (数字)9798350349184

ISBN: (纸本)9798350349191

Combining discrete difference evolution and convex optimization, a privacy protection task offloading algorithm is proposed for solving the new location privacy leakage problem at mobile terminals due to offloading tasks to multiple edge servers for computation. First, according to the characteristics of location privacy problem, the privacy cost of offloading decision is defined. Second, to minimize the total cost of task offloading, the objective problem is modeled as a joint optimization of offloading decision and transmission power allocation. Finally, to solve the problem efficiently, offloading decision variable is decoupled from transmission power variable; a discrete difference evolutionary algorithm is designed to accumulate the approximate optimal offloading decision, and the non-convex optimization problem is transformed into a convex optimization problem through variable substitution, to solve the optimal transmission power efficiently. Simulation results show the proposed algorithm has good convergence and real-time performance and can reduce the total cost of task offloading effectively.

关键词： Privacy Data privacy Costs Simulation Real-time systems Convex functions Servers

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Optimizing Solar-Energy Harvesting Communication Systems: An energy allocation scheme for maximizing data transmission 4

Optimizing Solar-Energy Harvesting Communication Systems: An...

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4th International Conference on Internet of Things and Smart City, IoTSC 2024

作者： Li, Shuai Yao, Zhonghao Chen, Shengbo Schoool of Software Henan University Henan Kaifeng China School of Computer Science & Technology Beijing Institute of Technology Beijing China School of Computer and Information Engineering Henan University Henan Kaifeng China Henan Engineering Research Center of Intelligent Technology and Application Henan University Henan Kaifeng China

ISBN: (纸本)9781510681767

The solar energy harvesting communication system is the basic support layer that is associated with the Internet of Things (IoT). However, the inherent stochasticity of solar energy brings about extensive research on energy allocation in the system. In this paper, we propose an energy allocation algorithm for maximizing the data transfer (MDT) of a solar energy harvesting communication system while jointly considering the practical constraints of battery capacity and link capacity restrictions. We can solve it through the convex optimization problem, but the solution is extremely complicated. This complexity is the result of the coupling of energy allocation problems caused by restricted battery capacity and link capacity. To simplify the solution process, we propose a decoupled energy allocation (DEA) algorithm. Simulation experiments are conducted through real energy harvesting data sets, and the results show that DEA outperforms the other two heuristic schemes. © 2024 SPIE.

关键词： Convex optimization

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Towards an API Marketplace for an e-Invoicing Ecosystem 1

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11th International Workshop on Enterprise Applications, Markets and Services in the Finance Industry, FinanceCom 2022

作者： Manchanda, Chinmay Hussain, Walayat Rabhi, Latif Rabhi, Fethi School of Computer Science and Engineering UNSW Sydney Australia Peter Faber Business School Australian Catholic University North Sydney Australia Ebusiness Software Services Sydney Australia

ISBN: (数字)9783031316715

ISBN: (纸本)9783031316708

Driven by a large number of very diverse and fast-evolving regulations, the adoption of e-invoicing is creating many challenges for solution providers, such as dealing with compliance requirements, cross-border issues, heterogeneity of standards and constant changes. Existing solutions do not represent a cost-effective and vendor-independent alternative to existing legacy systems, ERPs and databases. The proposed solution is based on leveraging cloud computing concepts, software-as-a-Service concept, API economy, and Business Process Modelling (BPM) concepts. It allows solution providers to choose SaaS components and customise their offerings according to customers needs. Given an ecosystem of APIs available via a marketplace, it becomes possible to rapidly compose and build new applications via BPM technologies. The paper describes an implementation of this concept realised using several e-invoicing APIs being composed using the WASP workflow system. Some preliminary results regarding the feasibility of the proposed approach in a simple buyer-seller scenario are discussed. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Ecosystems

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Improving Acoustic Echo Cancellation by Mixing Speech Local and Global Features with Transformer 48

Improving Acoustic Echo Cancellation by Mixing Speech Local ...

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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Liu, Yajie Xu, Xinmeng Tu, Weiping Yang, Yuhong Xiao, Li Wuhan University National Engineering Research Center for Multimedia Software School of Computer Science China Wuhan University Hubei Key Laboratory of Multimedia and Network Communication Engineering China

ISBN: (纸本)9781728163277

We propose MiT-Net, a novel mix-transformer neural network with a pyramid encoder operating in the time domain, for the task of acoustic echo cancellation. The MiT-Net formulates acoustic echo cancellation as a supervised speech separation problem, in which near-end speech is separated from a single microphone recording and sent to the far end, and consists of two key components. First, we apply a pyramid encoder, which adopts the coarse-to-fine structure, to extract the latent correlations between double-end signals and to fuse them in a multiscale manner. Second, we propose a mix-transformer, a combination of local and global attention in a parallel way, to leverage local and global speech information for separation. Experimental results show that the proposed method outperforms recent AEC methods in terms of objective evaluation metrics. In addition, exploring the correlation between speech local and global features by using the mix-transformer significantly improves the system performance and shows more robustness than the conventional transformer. © 2023 IEEE.

关键词： Signal encoding

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Microfluidic chip foreign body detection based on improved YOLOx

Microfluidic chip foreign body detection based on improved Y...

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2022 International Conference on Artificial Intelligence, Virtual Reality, and Visualization, AIVRV 2022

作者： Yan, Hao-Dong Liao, Li-Min Liu, Xiao-Dong School of Computer Science and Artificial Intelligence Zhengzhou University Zhengzhou450001 China Software Professional Department Guangdong Foshan Lianchuang Graduate School of Engineering Foshan528311 China

ISBN: (纸本)9781510662988

To solve the problem of identifying the presence of foreign objects in microfluidic chip images, an improved model is proposed for the feature of small foreign object targets. The attention mechanism is introduced to enhance the perceptiveness of the model in channel and space. The ResUnit module in the network is modified to enhance the feature information. Also choose diou as the loss function to improve the edge accuracy. The experimental results show that the improved YOLOx target detection algorithm has a significant improvement in foreign object detection in terms of accuracy, and the average precision (AP) reaches 99.12% on YOLOx, which is 0.7% higher than the original network. The results show that the improved algorithm based on YOLOx in this study can achieve foreign object detection in microfluidic chip images. © 2023 SPIE. All rights reserved.

关键词： Microfluidic chips

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Infrared Action Recognition Model Based on Improved ST-GCN

IAENG International Journal of Computer Science

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IAENG International Journal of computer science 2025年第6期52卷 1664-1671页

作者： Zhu, Xiaoliang Zhou, Ziwei School of Electronic Information University of Science and Technology Liaoning Anshan114051 China School of Computer Science and Software Engineering University of Science and Technology Liaoning Anshan114051 China

Infrared imaging technology is capable of capturing the thermal radiation emitted by the human body in conditions with insufficient visible light. Consequently, infrared behavior recognition leverages this capability to detect and analyze human movements in low-light or complex environments. However, infrared images are often affected by noise interference, which can obscure target features. To tackle these challenges, we propose an infrared human behavior recognition model. Within this model, human regions in infrared images are detected by YOLOv8 and passed on to AlphaPose to predict the locations of skeletal keypoints in the human body. Subsequently, the acquired skeletal sequences are employed to predict actions in ST-GCN. Simultaneously, we introduced the LKA attention mechanism and the PReLU activation function for structural optimization within the ST-GCN. These improvements enabled the ST-GCN to extract action features from skeletal keypoints more effectively, thereby enhancing the accuracy of infrared behavior *** extensive ablation studies, we have demonstrated that our proposed LPST-GCN model significantly enhances the performance of infrared action recognition and achieves excellent results on both the UNISV dataset (99.02%) and the NTU RGB+D dataset (95.86%). © (2025), IAENG International Journal of computer science

关键词： Thermography (imaging)

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