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Peer Collaboration in DBLP Using Graph Convolutional Network

SN computer science 2025年第1期6卷 1-7页

作者： Kumar, Chintoo Dar, Showkat Ahmad Batchu, Charishma Singamaneni, Kundan Sai Panguluri, Venkata Lakshmi Alekhya Department of Artificial Intelligence and Data Science GITAM Karnataka Bengaluru 562163 India Department of Computer Science and Engineering GITAM Karnataka Bengaluru 562163 India

The increasing popularity of Graph-based neural network architectures plays a pivotal role in providing promising results in applications, viz., Friendship networks, Co-authorship networks, Product recommendations, etc. Many machine learning tasks can be addressed when dealing with graph representation, i.e., graph classification, node classification, link prediction, graph embedding, etc. The traditional rule-based methods have many drawbacks in comparison to graph representation learning, such as it requires manual feature engineering and struggles with unseen cases (generalizability), etc., in the case of rule-based methods. At the same time, graph representation learning overcomes those issues profoundly. The advent of graph-based deep neural network architectures inspired us to complete a graph classification problem to explore clusters of researchers who worked together in academic publications. We built the model by using a graph convolutional network to solve a multi-classification problem. We conducted a comparative study to check the efficacy of graph representation learning by GCNs on the DBLP dataset. A comparative study shows that the built model outperforms the baseline models. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： DBLP Graph convolutional network Graph mining Social networks

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Towards Adaptive Masked Structural Learning for Graph-Level Clustering

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IEEE Transactions on Network science and Engineering 2025年第3期12卷 2021-2032页

作者： Yang, Jinbin Cai, Jinyu Zhang, Yunhe Huang, Sujia Wang, Shiping Fuzhou University College of Computer and Data Science Fuzhou350116 China Bank of Quanzhou Company Ltd. Quanzhou362046 China National University of Singapore Institute of Data Science 117602 Singapore Shenzhen Research Institute of Big Data Shenzhen518172 China Chinese University of Hong Kong Guangdong Provincial Key Laboratory of Big Data Computing Shenzhen518172 China

Graph data presents a vast landscape for real-world applications. Current graph-level clustering approaches predominantly utilize graph neural networks to capture the intricate structural information for graph data. However, a significant challenge arises in effectively integrate structural and feature information under the prevalent noise in the real-world scenario. The advent of masking strategies has marked significant strides in boosting model robustness, accommodating incomplete data, and enhancing generalization capabilities. Yet, research attention on leveraging mask strategy for facilitating graph-level clustering is still limited. In this paper, we introduce a novel graph-level clustering method, towards adaptive masked structural learning for graph-level clustering. The method performs adaptive masking through reconstruction loss, and jointly adaptive mask representation learning and clustering in an end-to-end unsupervised framework. The mutual information between maximized the entire graph and substructure representations is also utilized to learn to generate cluster-oriented graph-level representations. Extensive experiments on eight real graph-level benchmark datasets demonstrate the effectiveness and superiority of the proposed method. © 2013 IEEE.

关键词： Graph neural networks

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Lightweight LUW-DETR for efficient underwater benthic organism detection

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Visual computer 2025年 1-16页

作者： Li, Ning Ding, Bocheng Yang, Gaochao Ni, Shiyun Wang, Mingliang School of Computer Science and Artificial Intelligence Aliyun School of Big Data School of Software Changzhou University Changzhou213164 China

With the advancement of marine science research, the study and monitoring of benthic organisms have gained prominence. These organisms, inhabiting the seafloor, play crucial roles in marine ecosystems. However, traditional methods for benthic organism surveys rely heavily on manual operations, which are labor-intensive and prone to errors. Moreover, the complexity of underwater environments poses significant challenges for automated detection algorithms. Current models face issues such as large parameter sizes, high computational demands, and difficulties in detecting small targets and overlapping objects. To address these challenges, we propose LUW-DETR, a lightweight algorithm based on the transformer architecture. LUW-DETR incorporates a high–low-frequency attention mechanism and dynamic group convolution module to significantly improve feature extraction and computational efficiency. Experimental results demonstrate that LUW-DETR achieves competitive performance with an mAP of 83.1% on the URPC 2020 dataset and 63.7% on the Pascal VOC 2007 dataset, while reducing parameters by 28.3% and computational cost by 29.6% compared to baseline models. These results highlight LUW-DETR’s balance between accuracy and efficiency, making it suitable for resource-constrained underwater environments. Our source code is available at:https://***/DingBC/LUWDETR. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.

关键词： Marine biology

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Generating (Semi-)active Schedules for Dynamic Multi-mode Project Scheduling Using Genetic Programming Hyper-heuristics 25th

Generating (Semi-)active Schedules for Dynamic Multi-mode P...

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25th European Conference on Evolutionary Computation in Combinatorial Optimization, EvoCOP 2025 Held as Part of EvoStar 2025

作者： Tian, Yuan Mei, Yi Zhang, Mengjie Centre for Data Science and Artificial Intelligence and School of Engineering and Computer Science Victoria University of Wellington Wellington6012 New Zealand

ISBN: (纸本)9783031868481

Dynamic multi-mode resource-constrained project scheduling problem (DMRCPSP) is crucial for effectively managing complex projects where activities have multiple options of resource demand and durations are uncertain. Efficient solutions to this problem are vital for industrial applications, where optimal scheduling can significantly impact project costs and timelines. Genetic programming hyper-heuristic (GPHH) has been applied to evolve effective scheduling heuristics to make real-time decisions. However, current decision-making procedures for solving DMRCPSP primarily generate non-delay schedules, which are often suboptimal. This paper proposes two new decision-making procedures to generate (semi-)active schedules. The core idea is to expand the decision set and allow activities that cannot immediately start to reserve resources for future execution. We further developed GPHH to evolve scheduling heuristics based on these procedures and a comparison was conducted with existing decision-making procedures. The results demonstrated that the proposed GP approaches managed to evolve rules that generate more effective (semi-)active schedules than the best-known non-delayed schedules in real-time. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Genetic programming

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Cumulative Step Size Adaptation for Adaptive SEMO in Integer Space 13th

Cumulative Step Size Adaptation for Adaptive SEMO in Integ...

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13th International Conference on Evolutionary Multi-Criterion Optimization, EMO 2025

作者： Rudolph, Günter Wagner, Markus Department of Computer Science TU Dortmund University Dortmund Germany Department of Data Science and AI Monash University Clayton Australia

ISBN: (纸本)9789819635054

Parameter control involves dynamically adjusting the parameter values of the evolutionary algorithm throughout the optimization process, including parameters like mutation rate and operator selection. Self-adaptation can improve the performance and robustness of the algorithm, however, parameter control mechanisms themselves need to be designed and configured carefully. In this article, we review the cumulative step size adaptation method originally proposed for single-objective optimization over continuous variables and recast it for deployment in multiobjective optimization over unbounded integer space. We contribute a systematic investigation of its hyperparameters which shows that while (1) the very best configurations remain problem-specific, (2) the performance of the algorithm is largely independent of the self-adaptation scheme’s parameterization and initial configuration. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Integer linear programming

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DELTa: Dynamic Energy-and-Latency-aware Task scheduling for Fog-Cloud Paradigm

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IEEE Access 2025年 13卷 74617-74633页

作者： Mahapatra, Abhijeet Pradhan, Rosy Majhi, Santosh K. Mishra, Kaushik Department of Computer Science & Engineering Odisha Burla768018 India Sikkim Manipal University Sikkim Manipal Institute of Technology Department of Artificial Intelligence and Data Science Sikkim India Department of Electrical Engineering Odisha Burla768018 India Department of Computer Science and Information Technology Chhattisgarh Bilaspur495009 India Manipal Academy of Higher Education Manipal Institute of Technology Bengaluru Department of Computer Science and Engineering Manipal India

The rapid proliferation of IoT devices like smartphones, smartwatches, etc. has significantly elevated the quantity of data requiring execution. It poses challenges for centralized Cloud computing servers, such as latency overhead and increased consumption of energy. To address this, Fog computing has been incorporated in this research as a complementary paradigm to the Cloud-based computing model. It thereby reduces the huge distance between end-user IoT gadgets and the Cloud computing servers. In order to improve the latency and waiting time this research employs a Multi-Level Queue (MLQ) for classifying tasks based on their priority. For parallel processing, a Self-adaptive Fuzzy C-means++ (SaFCm++) approach has been applied to cluster the Fog computing nodes depending on the heterogeneity of the computational nodes. For efficient scheduling, a Dynamic Energy-and-Latency-aware Task scheduling (DELTa) approach has been proposed to optimize latency, makespan, and utilization of resources. Extensive simulations are carried out for different test cases considering task and machine heterogeneity, followed by a statistical analysis. The overall improvements of the proposed method over other baselines are 9.75% for makespan, 64.59% for service latency, 13.90% for resource utilization, and 12.75% for energy consumption. The proposed energy-efficient scheduling method effectively manages diverse tasks and resources, enhancing task processing efficiency while minimizing energy consumption and maximizing resource utilization. Furthermore, the value of the Friedman statistics FF for the proposed model is estimated at 5.44, which shows superiority over the crucial value for four methodologies and four evaluation matrices. © 2013 IEEE.

关键词： Fog computing

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Adaptive Treatment Planning via Multi-class Segmentation of GI Tract Tumours 6th

Adaptive Treatment Planning via Multi-class Segmentation of ...

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6th International conference on communication and computational technologies, ICCCT 2024

作者： Jain, Samyak Pal, Aarush Andrew, J. Maheshwari, Saatvik Department of Computer Science and Engineering and Department of Data Science and Computer Applications Manipal Institute of Technology Manipal Academy of Higher Education Karnataka Manipal576104 India

ISBN: (纸本)9789819774258

This study addresses gastrointestinal cancer radiation therapy challenges by implementing advanced deep learning techniques. We focus on automating manual segmentation tasks during treatment planning to enhance efficiency and patient tolerance. Our methodology uses a diverse 3D MRI scan dataset to employ image analysis, Convolutional Neural Networks (CNNs), and Image Segmentation. The primary architectural choices involve the U-Net and InceptionResNetv2 models, contributing to developing a predictive segmentation model. This substantial result underscores the effectiveness of our approach in achieving accurate segmentations in the clinical setting. The overarching objective of our project is to automate the segmentation of the stomach and intestines in MRI scans, thereby streamlining radiation therapy planning for improved efficiency and patient care. In our evaluations, the implemented model demonstrates a commendable dice coefficient of 0.7, as indicated by metrics such as dice loss. Anticipated outcomes encompass reduced treatment times, enhanced patient comfort, and optimised tumour radiation doses while minimising healthy tissue exposure. The successful implementation of this advanced model holds the potential to revolutionise current radiation therapy practices, significantly elevating cancer treatment outcomes. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Convolutional neural networks

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A Single Source Generalization Model via Spatial Amplitude Perturbation and Sensitivity Guidance for Colored Medical Image Segmentation 27th

A Single Source Generalization Model via Spatial Amplitude ...

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Yang, Zeyuan Yu, Chunyan College of Computer and Data Science/College of Software Fuzhou University Fuzhou350108 China

ISBN: (纸本)9783031782008

For medical images, domain shift is a very common phenomenon. To address this issue, researchers have proposed unsupervised domain adaptation and multi-source domain generalization. However, these methods are sometimes impractical for clinical applications since they need multi-domain data. To this end, single-source domain generalization has been further proposed. However, most single-source domain generalization methods are designed for grayscale medical images, making them unsuitable for color images such as fundus images. In this paper, we first propose a novel and effective Fourier transform-based data augmentation method for single-source domain color medical images, named spatial amplitude perturbation module (SAPM). The SAPM uses different Gaussian distributions to perturb different regions of the amplitude map obtained by FFT decomposition, thereby avoiding the need for information from other domains and ensuring the diversity of the augmented images. Then, we use feature sensitivity to guide the network to learn domain-invariant features, which can suppress feature channels sensitive to domain shift and emphasize feature channels insensitive to domain shift. We evaluate our method on a multi-domain fundus segmentation benchmark, and the results demonstrate the effectiveness of our proposed method. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Image segmentation

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A Digital Twin and Consensus Empowered Cooperative Control Framework for Platoon-Based Autonomous Driving

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清华大学学报自然科学版（英文版） 2025年第3期30卷 1096-1111页

作者： Jiayu Cao Supeng Leng Kai Xiong Xiaosha Chen School of Information and Communication Engineering University of Electronic Science and Technology of ChinaChengdu 611731China Shenzhen Institute for Advanced Study University of Electronic Science and Technology of ChinaShenzhen 518110China College of Computer Science and Technology and also with College of Data Science Taiyuan University of TechnologyTaiyuan 030024China

Platoon-based autonomous driving is indispensable for traffic automation,but it confronts substantial constraints in rugged terrains with unreliable links and scarce communication *** paper proposes a novel hierarchical Digital Twin(DT)and consensus empowered cooperative control framework for safe driving in harsh ***,leveraging intra-platoon information exchange,one platoon-level DT is constructed on the leader and multiple vehicle-level DTs are distributed among platoon *** leader first makes critical platoon-driving decisions based on the platoon-level ***,considering the impact of unreliable links on the platoon-level DT accuracy and the consequent risk of unsafe decision-making,a distributed consensus scheme is proposed to negotiate critical decisions *** successful negotiation,vehicles proceed to execute critical decisions,relying on their vehicle-level ***,a Space-Air-Ground-Integrated-Network(SAGIN)enabled information exchange is utilized to update the platoon-level DT for subsequent safe decision-making in scenarios with unreliable links,no roadside units,and obstructed ***,based on this framework,an adaptive platooning scheme is designed to minimize total delay and ensure driving *** results indicate that our proposed scheme improves driving safety by 21.1％and reduces total delay by 24.2％in harsh areas compared with existing approaches.

关键词： platoon autonomous driving distributed consensus digital twin space-air-ground integrated network

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Modelling a Multi-Perceptron Q Network Layer for Analyzing Patient Condition in Healthcare Sector

Modelling a Multi-Perceptron Q Network Layer for Analyzing P...

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2025 International Conference on Multi-Agent Systems for Collaborative Intelligence, ICMSCI 2025

作者： Karthi, M. Manirajan, S.P. Prince, A. Santhosh, R. Department of Artificial Intelligence and Data Science India Karpagam Academy of Higher Education Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798331509828

Chronic diseases like chronic respiratory diseases, diabetes, heart disease (HD) and cancer are the important causes of mortality globally. The diagnosis of Heart-related diseases with a variety of symptoms or characteristics is incredibly complex. Internet of Things (loT) embedded solution has improved with the increase in smart wearable devices. However, people who undergo heart attacks unexpectedly have poor survival rates. As a result, an Internet of Things based Multi-Perceptron Q-Network (MQN)-based monitoring program is suggested for cardiac patients to assist in identifying heart disease and administering medication appropriately. The technology based on MQN is used to securely encrypt this sensor data before sending it to the cloud. The encrypted and decrypted data is eventually done and MQN classifier is used to complete the classification process. The two categories of data that make up the classified outcomes are i) regular and ii) irregular. It indicates the individual's cardiac status, and if the results are peculiar, the doctor is notified via text so that the patient can receive treatment. When compared to current algorithms, the MQN for HD diagnosis exhibits better enhancement in the estimated investigational results. Furthermore, when contrasted against the current MQN, the introduced Multi-layered Perceptron (MLP) system facilitates safe data transmission yields the maximum security level of 96%, and is accomplished in the shortest amount of time for both decryption and encryption. © 2025 IEEE.

关键词： Lung cancer

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