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IAENG International Journal of Computer Science 2025年第3期52卷 673-683页

作者： Li, Yiran Zhang, Chunna School of Applied Technology University of Science and Technology Liaoning Liaoning Anshan114051 China School of Computer Science and Software Engineering University of Science and Technology Liaoning Liaoning Anshan114051 China

To address the problem of inaccurate prediction of slab quality in continuous casting, an algorithm based on particle swarm optimisation and differential evolution is proposed. The algorithm combines BP neural network prediction method. Firstly, the factors affecting the quality of the slab are analysed and a sufficient number of sample sets are extracted for training;Secondly, the particle swarm optimisation algorithm is optimised, and then the BP neural network is optimised and the chaos mechanism is introduced to increase the continuity of motion of the particles. The population is partitioned hierarchically, and different types of particles are processed separately to improve the global search capability of the algorithm. The differential evolution algorithm is also integrated to optimise cross-selection and increase particle diversity. Finally, a prediction model is built to predict and analyse the sample data. In the experiment, three algorithms are tested for different benchmark functions, namely the comparison of convergence, stability and searchability. The optimisation time comparison is completed, the target curve fit test is performed and the slab quality test results are checked. The experimental results show that the PSO-EIDE algorithm has strong optimisation ability, fast convergence speed and obvious improvement in stability and accuracy. © (2025), (International Association of Engineers). All rights reserved.

关键词： Prediction models

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Federated Learning for IoV Adaptive Vehicle Clusters: A Dynamic Gradient Compression Strategy

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IEEE Internet of Things Journal 2025年第11期12卷 17526-17541页

作者： Xing, Ling Zhao, Pengcheng Gao, Jianping Wu, Honghai Ma, Huahong Zhang, Xiaohui Henan University of Science and Technology College of Information Engineering Luoyang471003 China Henan University of Science and Technology School of Software Luoyang471003 China Henan University of Science and Technology College of Vehicle and Traffic Engineering Luoyang471003 China

Federated learning (FL) has been extensively utilized in distributed learning scenarios for the Internet of Vehicles (IoV). However, two key challenges exist: 1) gradients are frequently transmitted between vehicles during FL training, which reduces the communication timeliness between the traffic participants and 2) the fixed gradient compression method cannot sufficiently adapt to the dynamic IoV network topology. Therefore, we design an adaptive vehicle clustering method constructed according to multiple attributes, such as computational resources, communication distance, and latency. Accordingly, we propose a dynamic gradient compression strategy that filters similar local training models between vehicles and uses the Wasserstein distance to compute a sparsity threshold. This threshold acts as a dynamic compression factor that compresses gradient model parameters, reducing redundant parameter transmission. Furthermore, we conduct experiments using two datasets to evaluate the proposed strategy’s effectiveness. The compression ratio improved by 132- and 178-fold compared to the baselines, and the aggregated accuracy increased by an average of 10.13%. Additionally, experiments incorporating communication noise revealed that the aggregation model of the signal noise ratio is -29 dB. © 2014 IEEE.

关键词： Training Vehicle dynamics Adaptation models Computational modeling Accuracy Federated learning Convergence Quantization (signal) Network topology Internet of Vehicles

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Data Security and Privacy Protection Scheme Based on EC-ElGamal in Federal Learning

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SN Computer Science 2025年第2期6卷 1-10页

作者： Liu, Bin Blancaflor, Eric B. School of Information Technology Mapua University Manila Philippines School of Software Engineering Xiamen University of Technology Xiamen China

Federated learning is a new distributed learning framework that can train multi-party user data without knowing the original user data, which protects user privacy to a certain extent. However, the application of federated learning in the Internet of Things still faces some problems, such as the high cost of client-server communication, frequent communication failures, and lack of high-quality data to guarantee the training process. To solve the problems of communication costs affected by different encryption schemes, dynamic user joining and exiting, and low user data quality affecting accuracy, this paper proposes a data security and privacy protection method based on EC-ElGamal in federated learning. EC-ElGamal is an additive homomorphic encryption scheme based on ElGamal elliptic curves, which has a shorter key length and less traffic than the ElGamal scheme. Firstly, the scheme uses lightweight EC-ElGamal and improved multi-key EC-ElGamal cryptosystem in the Internet of Things environment to realize cooperative training of multi-key users under federated learning and reduce communication costs. Secondly, FSOA(Federated sum optimization algorithm) is proposed to obtain high-quality data for training, which can avoid the problem of low data quality affecting accuracy. At the same time, a dynamic user joining and exiting algorithm is proposed, which can realize the dynamic user update without affecting the training accuracy. The experiment results shows that by selecting high-quality training data and a short key length encryption scheme for model training, The total encryption and decryption time of this scheme is lower than the other three encryption and decryption time of Paillier, ElGamal and LWE, the communication cost of the scheme is reduced, and the training accuracy is ensured under the condition of dynamic updating of users. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Data security Dynamic update EC-ElGamal Federated learning Internet of things Privacy protection

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COVID-19 Prediction Model Empowered with Fused Computational Intelligence Technique

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IEIE Transactions on Smart Processing and Computing 2025年第1期14卷 109-117页

作者： Khan, Muhammad Adnan Naseer, Iftikhar Ali, Muhammad Nadeem Kim, Byung-Seo Department of Software Faculty of Artificial Intelligence & Software Gachon University Seongnam13120 Korea Republic of Faculty of Computer Science & Information Technology The Superior University Lahore54600 Pakistan Department of Software and Communications Engineering Hongik University Sejong Campus Korea Republic of

The novel Coronavirus (COVID-19) spread rapidly around the world and caused overwhelming effects on the health and economy of the world. It first appeared in Wuhan city of China and was declared a pandemic by the World Health Organization (WHO). Many researchers, as well as experts in clinical and artificial intelligence experts, are working together to control the rapid spread of COVID-19 with early detection. This study focused on intelligent prediction for coronavirus using computational intelligence approaches (IPC-FCIA) like convolutional neural networks, support vector machines, and fuzzy logic techniques. The proposed IPC-FCIA model is based on two sections namely the training section and the validation section. Features fusion and decision-level fusion are used in this study to enhance the performance of the recommended IPC-FCIA model. The proposed model predicts the early detection of COVID-19 in two types COVID-negative and COVID-positive. The benchmark results of the model show an accuracy of 97.66 % on decision-level fusion in the detection of COVID-19. The proposed model can be helpful for medical experts as well as COVID-19-affected patients. © 2025 Institute of Electronics Engineers of Korea. All rights reserved.

关键词： COVID-19

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CausalConf: Datasize-Aware Configuration Auto-Tuning for Recurring Big Data Processing Jobs via Adaptive Causal Structure Learning

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IEEE Transactions on Parallel and Distributed Systems 2025年第7期36卷 1354-1371页

作者： Dou, Hui He, Mingjie Zhang, Lei Zhang, Yiwen Zheng, Zibin Anhui University School of Computer Science and Technology Hefei China Sun Yat-sen University School of Software Engineering Guangzhou China

To ensure high-performance processing capabilities across diverse application scenarios, Big Data frameworks such as Spark and Flink usually provide a number of performance-related parameters to configure. Considering the computation scale and the characteristic of repeated executions of typical recurring Big Data processing jobs, how to automatically tune parameters for performance optimization has emerged as a hot research topic in both academic and industry. With the advantages in interpretability and generalization ability, causal inference-based methods recently prove their advancement over conventional search-based and machine learning-based methods. However, the complexity of Big Data frameworks, the time-varying input dataset size of a recurring job and the limitation of a single causal structure learning algorithm together prevent these methods from practical application. Therefore, in this paper, we design and implement CausalConf, a datasize-aware configuration auto-tuning approach for recurring Big Data processing jobs via adaptive causal structure learning. Specifically, the offline training phase is responsible for training multiple datasize-aware causal structure models with different causal structure learning algorithms, while the online tuning phase is responsible for recommending the next promising configuration in an iterative manner via the Multi-Armed Bandit-based optimal intervention set selection as well as the novel datasize-aware causal Bayesian optimization. To evaluate the performance of CausalConf, a series of experiments are conducted on our local Spark cluster with 9 different previously unknown target applications from HiBench. Experimental results show that the performance speed ratio achieved by CausalConf compared to the four recent and representative baselines can respectively reach 1.45×, 1.31×, 1.26× and 1.54× on average and up to 2.53×, 1.55×, 1.57×, 2.18×. Besides, the average total online tuning cost of CausalConf is reduced b

关键词： Spatio-temporal data

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A survey on cross-user federated recommendation

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Science China(Information Sciences) 2025年第4期68卷 7-32页

作者： Enyue YANG Yudi XIONG Wei YUAN Weike PAN Qiang YANG Zhong MING College of Computer Science and Software Engineering Shenzhen University School of Electrical Engineering and Computer Science The University of Queensland WeBank AI Lab WeBank Department of Computer Science and Engineering Hong Kong University of Science and Technology College of Big Data and Internet Shenzhen Technology University Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ)

Recommender systems are effective in mitigating information overload, yet the centralized storage of user data raises significant privacy concerns. Cross-user federated recommendation(CUFR) provides a promising distributed paradigm to address these concerns by enabling privacy-preserving recommendations directly on user devices. In this survey, we review and categorize current progress in CUFR, focusing on four key aspects: privacy, security, accuracy, and efficiency. Firstly,we conduct an in-depth privacy analysis, discuss various cases of privacy leakage, and then review recent methods for privacy protection. Secondly, we analyze security concerns and review recent methods for untargeted and targeted *** untargeted attack methods, we categorize them into data poisoning attack methods and parameter poisoning attack methods. For targeted attack methods, we categorize them into user-based methods and item-based methods. Thirdly,we provide an overview of the federated variants of some representative methods, and then review the recent methods for improving accuracy from two categories: data heterogeneity and high-order information. Fourthly, we review recent methods for improving training efficiency from two categories: client sampling and model compression. Finally, we conclude this survey and explore some potential future research topics in CUFR.

关键词： cross-user federated recommendation federated recommendation federated learning recommender systems user privacy

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NJmat 2.0:User Instructions of Data-Driven Machine Learning Interface for Materials Science

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Computers, Materials & Continua 2025年第4期83卷 1-11页

作者： Lei Zhang Hangyuan Deng Department of Internet Engineering School of Software EngineeringNanjing University of Information Science&TechnologyNanjing210044China Department of Materials Physics School of Chemistry and Materials ScienceNanjing University of Information Science&TechnologyNanjing210044China

NJmat is a user-friendly,data-driven machine learning interface designed for materials design and *** platform integrates advanced computational techniques,including natural language processing(NLP),large language models(LLM),machine learning potentials(MLP),and graph neural networks(GNN),to facili-tate materials *** platform has been applied in diverse materials research areas,including perovskite surface design,catalyst discovery,battery materials screening,structural alloy design,and molecular *** automating feature selection,predictive modeling,and result interpretation,NJmat accelerates the development of high-performance materials across energy storage,conversion,and structural ***,NJmat serves as an educational tool,allowing students and researchers to apply machine learning techniques in materials science with minimal coding *** automated feature extraction,genetic algorithms,and interpretable machine learning models,NJmat simplifies the workflow for materials informatics,bridging the gap between AI and experimental materials *** latest version(available at https://***/articles/software/NJmatML/24607893(accessed on 01 January 2025))enhances its functionality by incorporating NJmatNLP,a module leveraging language models like MatBERT and those based on Word2Vec to support materials prediction *** utilizing clustering and cosine similarity analysis with UMAP visualization,NJmat enables intuitive exploration of materials *** NJmat primarily focuses on structure-property relationships and the discovery of novel chemistries,it can also assist in optimizing processing conditions when relevant parameters are included in the training *** providing an accessible,integrated environment for machine learning-driven materials discovery,NJmat aligns with the objectives of the Materials Genome Initiative and promotes broader adoption of AI techniques in materials science.

关键词： Data-driven machine learning natural language processing machine learning potential large language model

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New analytical solutions for free vibration of embedded magneto-electro-elastic cylindrical shells with step-wise thickness variations

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Applied Mathematics and Mechanics(English Edition) 2025年第3期46卷 447-466页

作者： Jufang JIA Huilin YIN Qinyu YU Jiabin SUN Xinsheng XU Zhenhuan ZHOU School of Mechanical Engineering and Automation Dalian Polytechnic UniversityDalian 116034Liaoning ProvinceChina State Key Laboratory of Structural Analysis Optimization and CAE Software for Industrial EquipmentSchool of Mechanics and Aerospace EngineeringDalian University of TechnologyDalian 116024Liaoning ProvinceChina State Key Laboratory of Structural Analysis Optimization and CAE Software for Industrial EquipmentSchool of Chemical EngineeringOcean and Life SciencesDalian University of TechnologyPanjin 124221Liaoning ProvinceChina

In recent years,magneto-electro-elastic(MEE)cylindrical shells with step-wise thicknesses have shown significant potential in the field of vibration energy *** aid the design of such energy harvesting devices,an accurate free vibration analysis of embedded MEE cylindrical shells with step-wise thicknesses is performed within the framework of symplectic *** using the Legendre transformation,a new known vector is defined to transform the higher-order partial differential governing equations into a set of lower-order ordinary differential ***,the original vibration analysis is regarded as an eigen problem in the symplectic space,and analytical solutions can be represented by the symplectic *** numerical examples,the new analytical solutions are compared with the existing results,and good agreement is ***,the effects of critical design parameters on free vibration characteristics are thoroughly *** numerical results can serve as benchmarks for the development of other approximate or numerical methods.

关键词： analytical solution free vibration magneto-electro-elastic(MEE)material cylindrical shell with variable thickness symplectic method

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Ethical Aspects of ChatGPT in software engineering Research

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年第2期6卷 254-267页

作者： Akbar, Muhammad Azeem Khan, Arif Ali Liang, Peng Lappeenranta-Lahti University of Technology Software Engineering Department Lappeenranta15210 Finland University of Oulu M3S Empirical Software Engineering Research Unit Oulu90014 Finland Wuhan University School of Computer Science Wuhan430072 China

ChatGPT can improve software engineering (SE) research practices by offering efficient, accessible information analysis, and synthesis based on natural language interactions. However, ChatGPT could bring ethical challenges, encompassing plagiarism, privacy, data security, and the risk of generating biased or potentially detrimental data. This research aims to fill the given gap by elaborating on the key elements: motivators, demotivators, and ethical principles of using ChatGPT in SE research. To achieve this objective, we conducted a literature survey, identified the mentioned elements, and presented their relationships by developing a taxonomy. Furthermore, the identified literature-based elements (motivators, demotivators, and ethical principles) were empirically evaluated by conducting a comprehensive questionnaire-based survey involving SE researchers. In addition, we employed an interpretive structure modeling approach to analyze the relationships between the ethical principles of using ChatGPT in SE research and develop a level-based decision model. We further conducted a cross-impact matrix multiplication applied to classification analysis to create a cluster-based decision model. These models aim to help SE researchers devise effective strategies for ethically integrating ChatGPT into SE research by following the identified principles by adopting the motivators and addressing the demotivators. The findings of this study will establish a benchmark for incorporating ChatGPT services in SE research with an emphasis on ethical considerations. © 2020 IEEE.

关键词： software engineering

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Masked hybrid attention with Laplacian query fusion and tripartite sequence matching for medical image segmentation

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Neural Computing and Applications 2025年第8期37卷 5891-5911页

作者： Ekong, Favour Yu, Yongbin Patamia, Rutherford Agbeshi Sarpong, Kwabena Ukwuoma, Chiagoziem C. Wang, Xiangxiang Ukot, Akpanika Robert Cai, Jingye School of Information and Software Engineering University of Electronic Science and Technology of China Sichuan Chengdu611731 China College of Nuclear Technology and Automation Engineering Chengdu University of Technology Sichuan Chengdu610059 China Sichuan Engineering Technology Research Center for Industrial Internet Intelligent Monitoring and Application Chengdu University of Technology Sichuan Chengdu610059 China

Medical image segmentation is pivotal in computer-aided diagnosis systems, demanding high precision and contextual understanding. Vision Transformer-based approaches have gained much attention recently due to their excellent performance and ability to capture long-range dependencies in medical images. However, research shows they suffer from inadequate multi-scale feature integration, poor object localization, and inconsistent mask predictions, leading to sub-optimal segmentation performance. This paper addresses these challenges by redefining semantic medical image segmentation through learnable object queries within an enhanced transformer framework with a masked hybrid attention querying mechanism, optimizing multi-scale feature fusion, object localization, and instance-specific segmentation. First, this study presents a novel transformer-based masked hybrid attention mechanism using Laplacian query fusion on learnable query features and incorporating a novel tripartite sequence matching technique as part of the enhanced decoder block to improve the consistency of mask predictions and optimize decoder queries. The designed hybrid multi-head self- and cross-attention mechanisms aim to selectively integrate multi-scale features, ensuring optimal feature combinations for precise segmentation. Secondly, multiple class tokens are incorporated to improve object localization and capture class-specific characteristics within the transformer framework, leveraging the transformer decoders’ ability to learn distinct instance representations. Experimental results and extensive ablation studies demonstrate the effectiveness of the proposed approach on three publicly available datasets, obtaining better segmentation results compared to various state-of-the-art approaches using various evaluation metrics. Specifically, the proposed model achieves a Dice Score of 95.25%, 92.75%, and 85.25% on LUNA, ISIC, and DRIVE datasets, respectively. © The Author(s), under exclusive licence

关键词： Laplace transforms

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