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Enhancing link prediction in graph data augmentation through graphon mixup

Neural Computing and Applications 2025年第8期37卷 6267-6282页

作者： Sultana, Tangina Hossain, Md. Delowar Morshed, Md. Golam Lee, Young-Koo Department of Electronics and Communication Engineering Hajee Mohammad Danesh Science and Technology University Dinajpur Bangladesh Department of Computer Science and Engineering Kyung Hee University Yongin Korea Republic of Department of Computer Science and Engineering Hajee Mohammad Danesh Science and Technology University Dinajpur Bangladesh

Link prediction in complex networks is a fundamental problem with applications in diverse domains, from social networks to biological systems. Traditional approaches often struggle to capture intricate relationships in graphs, leading to suboptimal predictions. To address this, we introduce a novel method called graphon mixup (GM), which leverages the power of graphons to enhance link prediction. The augmentation strategy involves generating a synthetic graph by combining the original graph with a graphon-based synthetic graph. This process, expressed as a weighted combination of adjacency matrices, strategically blends real and synthetic information, enriching the training dataset. GM formulates link prediction as a joint optimization problem, aligning the characteristics of the synthetic graph with the true underlying structure. The objective is to minimize cross-entropy loss between predicted and true edge probabilities. A detailed computational complexity analysis evaluates the time and space requirements, aiding in understanding the efficiency and scalability of GM across different datasets and network sizes. Empirical validation on benchmark datasets demonstrates GM’s effectiveness in consistently improving average precision across diverse network types. The proposed method enhances the generalization capabilities of link prediction models, providing a more robust framework capable of accurate predictions even in the presence of noise or unseen patterns. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Prediction models

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Stairdepth: a novel staircase detection through depth maps generated by depth anything V2

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International Journal of information technology (Singapore) 2025年第4期17卷 2075-2086页

作者： Shekar, Avire Laxmi Chandra Chandrika, Mukkolla Bhuvana Naidu, Vakkalagadda Hemanth Muppalaneni, Naresh Babu Department of Computer Science and Engineering Indian Institute of Information Technology Design and Manufacturing Kurnool Kurnool India

Detection of a staircase is an important task in the fields of both assistive technology and autonomous navigation with an aim to enable substantial improvement in safety and accessibility for both those with limited vision and those with mobility problems. Classic methods often perform poorly under challenging conditions, such as low lighting, irregular architectural arrangements, or occluded views. Whereas the integration of physical depth sensors has increased detection accuracy, this integration introduces increased complexity, cost and greater maintenance issues. Our work proposes a novel architecture capable of directly estimating depth maps from color images using a pre-trained model without relying on physical sensors for depth estimation. It detects the staircase efficiently, including the classification of stair lines into concave or convex, with the classification of surfaces as a tread or riser, hence localizing staircases accurately. Our proposed methodology demonstrated exceptional results, with 83.2% Precision, 90.8% Recall, and 72.2% Intersection over Union on stair line classification and 97.1% Pixel Accuracy and 82.9% Mean Pixel Accuracy on stair surfaces by outperforming the efficacy of current approaches pertaining to stair line and surface classification. © Bharati Vidyapeeth's Institute of computer Applications and Management 2025.

关键词： Depth image Segmentation Stair surfaces Staircase detection StairDepth Stairline

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ResdenseNet: a lightweight dense ResNet enhanced with depthwise separable convolutions and its applications for early plant disease classification

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Neural Computing and Applications 2025年第8期37卷 6305-6326页

作者： Nagpal, Jyoti Goel, Lavika Department of Computer Science & Engineering The NorthCap University Gurugram122017 India Department of Computer Science and Engineering Malaviya National Institute of Technology Rajasthan Jaipur302017 India

In recent years, artificial intelligence has undergone robust development, leading to the emergence of numerous autonomous AI applications. However, a crucial challenge lies in optimizing computational efficiency and reducing training time while maintaining high accuracy with limited hardware resources. This paper introduces ResdenseNet, a model built upon the MobileNet, DenseNet, and ResNet architectures. ResdenseNet combines dense blocks and residual blocks from the DenseNet and ResNet architectures. In these dense blocks, the standard convolutional units are replaced by depthwise separable convolutional units, a significant part of the MobileNet architecture. The experimental outcomes are contrasted with established models and their iterations, including ResNet-50, ResNet-101, MobileNet-V1, MobileNet-V2, DenseNet-121, and DenseNet-169. The proposed model is tested on benchmark and proposed datasets, showcasing its efficiency in reducing computations and accelerating the training process. Emphasizing hyperparameter importance, ResdenseNet, optimized with a growth rate of 64, 6 layers, and ReLU activation, achieves an accuracy of (98.73%) and a F1-score of (98.20%) on the wheat and barley dataset. The results indicate that ResdenseNet significantly decreases the number of parameters to 0.72M and efficiently shortens training time to 5983.54 s. Particularly noteworthy is ResdenseNet’s superiority over other models in terms of having the fewest parameters, the shortest training time, and the highest accuracy, especially when dealing with wheat, barley, and maize datasets. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Plant diseases

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3D Road Network Modeling and Road Structure Recognition in Internet of Vehicles

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computer Modeling in engineering & sciences 2024年第2期138卷 1365-1384页

作者： Dun Cao Jia Ru Jian Qin Amr Tolba Jin Wang Min Zhu College of Computer and Communication Engineering Changsha University of Science and TechnologyChangsha410114China Department of Computer Science Community CollegeKing Saud UniversityRiyadh11437Saudi Arabia College of Information Science and Technology Zhejiang Shuren UniversityHangzhou310015China

Internet of Vehicles (IoV) is a new system that enables individual vehicles to connect with nearby vehicles,people, transportation infrastructure, and networks, thereby realizing amore intelligent and efficient transportationsystem. The movement of vehicles and the three-dimensional (3D) nature of the road network cause the topologicalstructure of IoV to have the high space and time *** modeling and structure recognition for 3Droads can benefit the description of topological changes for IoV. This paper proposes a 3Dgeneral roadmodel basedon discrete points of roads obtained from GIS. First, the constraints imposed by 3D roads on moving vehicles areanalyzed. Then the effects of road curvature radius (Ra), longitudinal slope (Slo), and length (Len) on speed andacceleration are studied. Finally, a general 3D road network model based on road section features is *** paper also presents intersection and road section recognition methods based on the structural features ofthe 3D road network model and the road features. Real GIS data from a specific region of Beijing is adopted tocreate the simulation scenario, and the simulation results validate the general 3D road network model and therecognitionmethod. Therefore, thiswork makes contributions to the field of intelligent transportation by providinga comprehensive approach tomodeling the 3Droad network and its topological changes in achieving efficient trafficflowand improved road safety.

关键词： Internet of vehicles road networks 3D road model structure recognition GIS

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Campaign: A Personalized Offloading, Semantic Communication, Latency-aware Resource Slicing and SFC Orchestration for SDN and NFV Empowered 6G Serverless Computing Network

IAENG International Journal of Computer Science

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IAENG International Journal of computer science 2024年第10期51卷 1480-1515页

作者： Chowdhury, Mahfuzulhoq The Computer Science and Engineering Department Chittagong University of Engineering and Technology Chittagong4349 Bangladesh

Serverless computing has shifted cloud server management responsibilities away from end users and towards service providers. Serverless computing offers greater scalability, flexibility, ease of deployment, and cost-effective resource utilization. Previous serverless computing research excluded semantic communication, software-defined networking (SDN) and network function virtualization (NFV)-enabled service function chaining (SFC) orchestration, and minimum latency-aware hybrid worker selection based on user and service provider-retained resource usage, federated learning (FL), and blockchain (BC) operations. To overcome these issues, this article proposes cognitive intelligence, personalized offloading, FL and BC-based semantic and nonsemantic serverless applications with user satisfaction, multiple benefits, minimum hybrid latency cost-aware resource slicing, physical, digital, and NFV worker selection, and an SFC orchestration policy for SDN and NFV-enabled 6G networks that leverage user and service provider retained resource usage. Simulation results indicate that the proposed scheme improves E2E delay by over 40%, user financial gain by 4%, user satisfaction by 8%, and throughput by 6% when compared to existing schemes. © (2024), (International Association of Engineers). All rights reserved.

关键词： Network function virtualization

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Multistage Parameter Optimization for Rule Generation for Multistage Manufacturing Processes

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IEEE Transactions on Industrial Informatics 2024年第3期20卷 3857-3867页

作者： Wahyuni, Ida Chang, Chin-Chun Yang, Hua-Sheng Wang, Wei-Jen Liang, Deron National Central University Department of Computer Science and Information Engineering Taoyuan32001 Taiwan National Taiwan Ocean University Department of Computer Science and Engineering Keelung20224 Taiwan

Defects in multistage manufacturing processes (MMPs) decrease profitability and product quality. Therefore, MMP parameter optimization within a range is essential to prevent defects, achieve dynamic accuracy, and accommodate manufacturing tolerances. However, existing studies only focused on optimization in a single manufacturing stage of MMP, such as the weaving stage in fabric manufacturing. Furthermore, existing methods optimize for a single value rather than a range. Thus, we propose a novel approach called multistage parameter optimization for rule generation (MPORG) to prevent the occurrence of defects in MMPs. In the proposed approach, key parameters are identified and optimized to a range for each defect type. Subsequently, the optimized parameters for each defect type are merged. Our approach is novel because it optimizes parameters to a range rather than a single value, allowing engineers to select a value in this range according to their experience. It also provides results that are specific to a product type. Our approach outperformed the classification and regression tree (CART) algorithm and multiresponse CART method in experiments on an empirical fabric manufacturing dataset that we gathered. The experimental results demonstrated that the MPORG approach can prevent the occurrence of single-type or multiple-type defects by approximately 89%. © 2005-2012 IEEE.

关键词： Defects

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An Improved Multi-Objective Particle Swarm Optimization Routing on MANET

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computer Systems science & engineering 2023年第2期44卷 1187-1200页

作者： G.Rajeshkumar M.Vinoth Kumar K.Sailaja Kumar Surbhi Bhatia Arwa Mashat Pankaj Dadheech Department of Computer Science and Engineering Erode Sengunthar Engineering CollegePerunduraiErode638057Tamil NaduIndia Department of Information Science and Engineering Dayananda Sagar Academy of Technology and ManagementBangalore560082India Department of Master of Computer Applications M.S.Ramaiah Institute of TechnologyBangalore560054India Department of Information Systems College of Computer Science and Information TechnologyKing Faisal UniversityRiyadh11533Saudi Arabia Faculty of Computing and Information Technology King Abdulaziz UniversityRabigh21911Saudi Arabia Department of Computer Science and Engineering Swami Keshvanand Institute of TechnologyManagement&GramothanJaipur302017RajasthanIndia

A Mobile Ad hoc Network(MANET)is a group of low-power con-sumption of wireless mobile nodes that conﬁgure a wireless network without the assistance of any existing infrastructure/centralized *** primary aim of MANETs is to extendﬂexibility into the self-directed,mobile,and wireless domain,in which a cluster of autonomous nodes forms a MANET routing *** Intrusion Detection System(IDS)is a tool that examines a network for mal-icious behavior/policy violations.A network monitoring system is often used to report/gather any suspicious attacks/*** IDS is a software program or hardware system that monitors network/security trafﬁc for malicious attacks,sending out alerts whenever it detects malicious *** impact of Dynamic Source Routing(DSR)in MANETs challenging blackhole attack is investigated in this research *** Cluster Trust Adaptive Acknowledgement(CTAA)method is used to identify unauthorised and malfunctioning nodes in a MANET *** system is active and provides successful delivery of a data packet,which implements Kalman Filters(KF)to anticipate node ***,KF is used to eliminate synchronisation errors that arise during the sending and receiving *** order to provide an energy-efﬁcient solution and to minimize network trafﬁc,route optimization in MANET by using Multi-Objective Particle Swarm Optimization(MOPSO)technique to determine the optimal num-ber of clustered MANET along with energy dissipation in *** to the researchﬁndings,the proposed CTAA-MPSO achieves a Packet Delivery Ratio(PDR)of 3.3%.In MANET,the PDR of CTAA-MPSO improves CTAA-PSO by 3.5%at 30%malware.

关键词： MANET intrusion detection system cluster kalmanﬁlter dynamic source routing multi-objective particle swarm optimization packet delivery ratio

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UNet Based onMulti-Object Segmentation and Convolution Neural Network for Object Recognition

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computers, Materials & Continua 2024年第7期80卷 1563-1580页

作者： Nouf Abdullah Almujally Bisma Riaz Chughtai Naif Al Mudawi Abdulwahab Alazeb Asaad Algarni Hamdan A.Alzahrani Jeongmin Park Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Science Air UniversityIslamabad44000Pakistan Department of Computer Science College of Computer Science and Information SystemNajran UniversityNajran55461Saudi Arabia Department of Computer Sciences Faculty of Computing and Information TechnologyNorthern Border UniversityRafha91911Saudi Arabia Information Technology Department College of Computing and InformaticsSaudi Electronic UniversityRiyadh13316Saudi Arabia Department of Computer Engineering Korea Polytechnic UniversitySiheung-siGyeonggi-do237Republic of Korea

The recent advancements in vision technology have had a significant impact on our ability to identify multiple objects and understand complex *** technologies,such as augmented reality-driven scene integration,robotic navigation,autonomous driving,and guided tour systems,heavily rely on this type of scene *** paper presents a novel segmentation approach based on the UNet network model,aimed at recognizing multiple objects within an *** methodology begins with the acquisition and preprocessing of the image,followed by segmentation using the fine-tuned UNet ***,we use an annotation tool to accurately label the segmented *** labeling,significant features are extracted from these segmented objects,encompassing KAZE(Accelerated Segmentation and Extraction)features,energy-based edge detection,frequency-based,and blob *** the classification stage,a convolution neural network(CNN)is *** comprehensive methodology demonstrates a robust framework for achieving accurate and efficient recognition of multiple objects in *** experimental results,which include complex object datasets like MSRC-v2 and PASCAL-VOC12,have been *** analyzing the experimental results,it was found that the PASCAL-VOC12 dataset achieved an accuracy rate of 95%,while the MSRC-v2 dataset achieved an accuracy of 89%.The evaluation performed on these diverse datasets highlights a notably impressive level of performance.

关键词： UNet segmentation blob fourier transform convolution neural network

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SuspAct: novel suspicious activity prediction based on deep learning in the real-time environment

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Neural Computing and Applications 2024年第34期36卷 21307-21320页

作者： Kansal, Sachin Jain, Akshat Kumar Biswas, Moyukh Bansal, Shaurya Mahindru, Namay Kansal, Priya Computer Science Engineering Department Thapar Institute of Engineering and Technology Punjab Patiala India

In today’s evolving landscape of video surveillance, our study introduces SuspAct, an innovative ensemble model designed to detect suspicious activities in real time swiftly. Leveraging advanced Long-term Recurrent Convolutional Networks (LRCN), SuspAct represents a significant advancement in intelligent surveillance technology. By combining insights from various LRCN models through the Majority Voting ensemble technique, SuspAct enhances its overall robustness, outperforming traditional surveillance methods. Through rigorous experimentation on large-scale datasets, we demonstrate SuspAct’s superiority in proactive crime prevention, showcasing its potential to revolutionize security protocols and contribute substantially to public safety. Our work addresses the challenges posed by the escalating volume of video data and lays a strong foundation for future advancements in intelligent video surveillance technology. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Deep learning

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Quality Assessment of Selective Laser Melting Components Using Quantitative Ultrasound Measurements and Image Texture Analysis

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IEEE Access 2024年 12卷 71797-71807页

作者： Lin, Chun-Hui Lin, Cheng-Jian Wang, Shyh-Hau National Cheng Kung University Department of Computer Science and Information Engineering Tainan 701 Taiwan National Chin-Yi University of Technology Department of Computer Science and Information Engineering Taichung 411 Taiwan

Successful production of metallic selective laser melting components requires a quality assurance process that can effectively and nondestructively assess internal defects. Ultrasound testing has emerged as a valuable modality for defect identification as well as the characterization of microstructural and material properties. In this study, a comprehensive quality assessment approach was developed, leveraging quantitative ultrasound parameters, images, and image texture analysis. The evaluation focused on Inconel 718 selective laser melting components manufactured using diverse combinations of process parameters. The investigation involved examining the correlation between ultrasound parameters, image texture features, and the overall quality of the fabricated components. To validate the findings obtained through ultrasound testing, micro-computed tomography was employed. The experimental results provided evidence that the ultrasound parameters and image texture features successfully detected nonhomogeneous microstructures present within the selective laser melting components. Notably, the speed of sound and attenuation exhibited a positive correlation of 0.64 and 0.74, respectively, with porosity, indicating their potential as indicators of porosity. Furthermore, image texture features such as entropy, contrast, and homogeneity demonstrated high correlations (R2> 0.7 ) with the quality of the components. Consequently, these identified features hold promising potential for aiding in the selection of optimal process parameters and the prediction of porosity in future research. © 2013 IEEE.

关键词： image texture analysis Quality assessment selective laser melting ultrasound testing

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