IF 3.2 4区计算机科学

Journal of Mechanisms and Robotics-Transactions of the Asme Pub Date : 2025-11-01 Epub Date: 2025-08-04 DOI: 10.1115/1.4069154

Huan Liu, Qiaode Jeffrey Ge, Mark P Langer

{"title":"KINEMATIC CONVEX COMBINATIONS OF MULTIPLE POSES OF A BOUNDED PLANAR OBJECT BASED ON AN AVERAGE-DISTANCE MINIMIZING MOTION SWEEP.","authors":"Huan Liu, Qiaode Jeffrey Ge, Mark P Langer","doi":"10.1115/1.4069154","DOIUrl":"10.1115/1.4069154","url":null,"abstract":"Convex combination of points is a fundamental operation in computational geometry. By considering rigid-body displacements as points in the image spaces of planar quaternions, quaternions and dual quaternions, respectively, the notion of convexity in Euclidean three-space has been extended to kinematic convexity in <math><mi>S</mi> <mi>E</mi> <mo>(</mo> <mn>2</mn> <mo>)</mo> <mo>,</mo> <mi>S</mi> <mi>O</mi> <mo>(</mo> <mn>3</mn> <mo>)</mo></math> , and <math><mi>S</mi> <mi>E</mi> <mo>(</mo> <mn>3</mn> <mo>)</mo></math> in the context of computational kinematic geometry. This paper deals with computational kinematic geometry of bounded planar objects rather than that of infinitely large moving spaces. In this paper, we present a new formulation for kinematic convexity based on an average-distance minimizing motion sweep of a bounded planar object. The resulting 1-DOF motion sweep between two planar poses is represented as a convex combination in the configuration space defined by <math><mo>(</mo> <mi>x</mi> <mo>,</mo> <mi>y</mi> <mo>,</mo> <mi>z</mi> <mo>)</mo></math> where <math><mo>(</mo> <mi>x</mi> <mo>,</mo> <mi>y</mi> <mo>)</mo></math> is associated with the location of the centroid of the planar object and <math><mi>z</mi> <mo>=</mo> <mtext>sin</mtext> <mspace></mspace> <mi>θ</mi></math> with <math><mi>θ</mi></math> being the angle of rotation. For three poses, a 2-DOF motion sweep is developed that not only minimizes the combined average squared distances but also attains a convex-combination representation so that existing algorithms for convex hull of points can be readily applied to the construction and analysis of kinematic convex hulls. This results in a new type of convex hull for planar kinematics such that its boundaries are defined by the average-distance minimizing sweeps of the bounded planar object.","PeriodicalId":49155,"journal":{"name":"Journal of Mechanisms and Robotics-Transactions of the Asme","volume":"17 11","pages":""},"PeriodicalIF":3.2,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12349904/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144856880","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

S-YOLO: An enhanced small object detection method based on adaptive gating strategy and dynamic multi-scale focus module. S-YOLO：一种基于自适应门控策略和动态多尺度聚焦模块的增强小目标检测方法。

IF 6.3 1区计算机科学

Neural Networks Pub Date : 2025-11-01 Epub Date: 2025-07-01 DOI: 10.1016/j.neunet.2025.107782

Zengnan Wang, Feng Yan, Liejun Wang, Yabo Yin, Jiahuan Lin

{"title":"S-YOLO: An enhanced small object detection method based on adaptive gating strategy and dynamic multi-scale focus module.","authors":"Zengnan Wang, Feng Yan, Liejun Wang, Yabo Yin, Jiahuan Lin","doi":"10.1016/j.neunet.2025.107782","DOIUrl":"10.1016/j.neunet.2025.107782","url":null,"abstract":"Detecting small objects in drone aerial imagery presents significant challenges, particularly when algorithms must operate in real-time under computational constraints. To address this issue, we propose S-YOLO, an efficient and streamlined small object detection framework based on YOLOv10. The S-YOLO architecture emphasizes three key innovations: (1) Enhanced Small Object Detection Layers: These layers augment semantic richness to improve detection of diminutive targets. (2) C2fGCU Module: Incorporating Gated Convolutional Units (GCU), this module adaptively modulates activation strength through deep feature analysis, enabling the model to concentrate on salient information while effectively mitigating background interference. (3) Dynamic Multi-Scale Fusion (DMSF) Module: By integrating SE-Norm with multi-scale feature extraction, this component dynamically recalibrates feature weights to optimize cross-scale information integration and focus. S-YOLO surpasses YOLOv10-n, achieving mAP50:95 improvements of 5.3%, 4.4%, and 1.4% on the VisDrone2019, AI-TOD, and DOTA1.0 datasets, respectively. Notably, S-YOLO maintains fewer parameters than YOLOv10-n while processing 285 images per second, establishing it as a highly efficient solution for real-time small object detection in aerial imagery.","PeriodicalId":49763,"journal":{"name":"Neural Networks","volume":"191 ","pages":"107782"},"PeriodicalIF":6.3,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144668866","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Curriculum negative mining for temporal networks. 时间网络的课程负向挖掘。

IF 6.3 1区计算机科学

Neural Networks Pub Date : 2025-11-01 Epub Date: 2025-07-11 DOI: 10.1016/j.neunet.2025.107858

Ziyue Chen, Tongya Zheng, Mingli Song

{"title":"Curriculum negative mining for temporal networks.","authors":"Ziyue Chen, Tongya Zheng, Mingli Song","doi":"10.1016/j.neunet.2025.107858","DOIUrl":"10.1016/j.neunet.2025.107858","url":null,"abstract":"Temporal networks are effective in capturing the evolving interactions of networks over time, such as social networks and e-commerce networks. In recent years, researchers have primarily concentrated on developing specific model architectures for Temporal Graph Neural Networks (TGNNs) in order to improve the representation quality of temporal nodes and edges. However, limited attention has been given to the quality of negative samples during the training of TGNNs. When compared with static networks, temporal networks present two specific challenges for negative sampling: positive sparsity and positive shift. Positive sparsity refers to the presence of a single positive sample amidst numerous negative samples at each timestamp, while positive shift relates to the variations in positive samples across different timestamps. To robustly address these challenges in training TGNNs, we introduce Curriculum Negative Mining (CurNM), a model-aware curriculum learning framework that adaptively adjusts the difficulty of negative samples. Within this framework, we first establish a dynamically updated negative pool that balances random, historical, and hard negatives to address the challenges posed by positive sparsity. Secondly, we implement a temporal-aware negative selection module that focuses on learning from the disentangled factors of recently active edges, thus accurately capturing shifting preferences. Finally, the selected negatives are combined with annealing random negatives to support stable training. Extensive experiments on 12 datasets and 3 TGNNs demonstrate that our method outperforms baseline methods by a significant margin. Additionally, thorough ablation studies and parameter sensitivity experiments verify the usefulness and robustness of our approach. Our code is available at https://github.com/zziyue83/CurNM.","PeriodicalId":49763,"journal":{"name":"Neural Networks","volume":"191 ","pages":"107858"},"PeriodicalIF":6.3,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144660884","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Pixel adaptive deep-unfolding neural network with state space model for image deraining. 基于状态空间模型的像素自适应深度展开神经网络图像训练。

IF 6.3 1区计算机科学

Neural Networks Pub Date : 2025-11-01 Epub Date: 2025-07-14 DOI: 10.1016/j.neunet.2025.107845

Yao Xiao, Youshen Xia

{"title":"Pixel adaptive deep-unfolding neural network with state space model for image deraining.","authors":"Yao Xiao, Youshen Xia","doi":"10.1016/j.neunet.2025.107845","DOIUrl":"10.1016/j.neunet.2025.107845","url":null,"abstract":"Rain streaks affects the visual quality and interfere with high-level vision tasks on rainy days. Removing raindrops from captured rainy images becomes important in computer vision applications. Recently, deep-unfolding neural networks (DUNs) are shown their effectiveness on image deraining. Yet, there are two issues that need to be further addressed : 1) Deep unfolding networks typically use convolutional neural networks (CNNs), which lack the ability to perceive global structures, thereby limiting the applicability of the network model; 2) Their gradient descent modules usually rely on a scalar step size, which limits the adaptability of the method to different input images. To address the two issues, we proposes a new image de-raining method based on a pixel adaptive deep unfolding network with state space models. The proposed network mainly consists of both the adaptive pixel-wise gradient descent (APGD) module and the stage fusion proximal mapping (SFPM) module. APGD module overcomes scalar step size inflexibility by adaptively adjusting the gradient step size for each pixel based on the previous stage features. SFPM module adopts a dual-branch architecture combining CNNs with state space models (SSMs) to enhance the perception of both local and global structures. Compared to Transformer-based models, SSM enables efficient long-range dependency modeling with linear complexity. In addition, we introduce a stage feature fusion with the Fourier transform mechanism to reduce information loss during the unfolding process, ensuring key features are effectively propagated. Extensive experiments on multiple public datasets demonstrate that our method consistently outperforms state-of-the-art deraining methods in terms of quantitative metrics and visual quality. The source code is available at https://github.com/cassiopeia-yxx/PADUM.","PeriodicalId":49763,"journal":{"name":"Neural Networks","volume":"191 ","pages":"107845"},"PeriodicalIF":6.3,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144668864","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Real-time data-efficient portrait stylization via geometric alignment. 通过几何对齐实现实时数据高效的肖像样式化。

IF 6.3 1区计算机科学

Neural Networks Pub Date : 2025-11-01 Epub Date: 2025-06-25 DOI: 10.1016/j.neunet.2025.107774

Xinrui Wang, Zhuoru Li, Xuanyu Yin, Xiao Zhou, Yusuke Iwasawa, Yutaka Matsuo, Jiaxian Guo

{"title":"Real-time data-efficient portrait stylization via geometric alignment.","authors":"Xinrui Wang, Zhuoru Li, Xuanyu Yin, Xiao Zhou, Yusuke Iwasawa, Yutaka Matsuo, Jiaxian Guo","doi":"10.1016/j.neunet.2025.107774","DOIUrl":"10.1016/j.neunet.2025.107774","url":null,"abstract":"Portrait Stylization aims to imbue portrait photos with vivid artistic effects drawn from style examples. Despite the availability of enormous training datasets and large network weights, existing methods struggle to maintain geometric consistency and achieve satisfactory stylization effects due to the disparity in facial feature distributions between facial photographs and stylized images, limiting the application on rare styles and mobile devices. To alleviate this, we propose to establish meaningful geometric correlations between portraits and style samples to simplify the stylization by aligning corresponding facial characteristics. Specifically, we integrate differentiable Thin-Plate-Spline (TPS) modules into an end-to-end Generative Adversarial Network (GAN) framework to improve the training efficiency and promote the consistency of facial identities. By leveraging inherent structural information of faces, e.g., facial landmarks, TPS module can establish geometric alignments between the two domains, at global and local scales, both in pixel and feature spaces, thereby overcoming the aforementioned challenges. Quantitative and qualitative comparisons on a range of portrait stylization tasks demonstrate that our models not only outperforms existing models in terms of fidelity and stylistic consistency, but also achieves remarkable improvements in 2× training data efficiency and 100× less computational complexity, allowing our lightweight model to achieve real-time inference (30 FPS) at 512*512 resolution on mobile devices.","PeriodicalId":49763,"journal":{"name":"Neural Networks","volume":"191 ","pages":"107774"},"PeriodicalIF":6.3,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144668865","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Practicing in quiz, assessing in quiz: A quiz-based neural network approach for knowledge tracing. 在测验中练习，在测验中评估：一种基于测验的知识追踪神经网络方法。

IF 6.3 1区计算机科学

Neural Networks Pub Date : 2025-11-01 Epub Date: 2025-07-12 DOI: 10.1016/j.neunet.2025.107797

Shuanghong Shen, Qi Liu, Zhenya Huang, Linbo Zhu, Junyu Lu, Kai Zhang

{"title":"Practicing in quiz, assessing in quiz: A quiz-based neural network approach for knowledge tracing.","authors":"Shuanghong Shen, Qi Liu, Zhenya Huang, Linbo Zhu, Junyu Lu, Kai Zhang","doi":"10.1016/j.neunet.2025.107797","DOIUrl":"10.1016/j.neunet.2025.107797","url":null,"abstract":"Online learning has demonstrated superiority in connecting high-quality educational resources to a global audience. To ensure an excellent learning experience with sustainable and opportune learning instructions, online learning systems must comprehend learners' evolving knowledge states based on their learning interactions, known as the Knowledge Tracing (KT) task. Generally, learners practice through various quizzes, each comprising several exercises that cover similar knowledge concepts. Therefore, their learning interactions are continuous within each quiz but discrete across different quizzes. However, existing methods overlook the quiz structure and assume all learning interactions are uniformly distributed. We argue that learners' knowledge states should also be assessed in quiz since they practiced in quiz. To achieve this goal, we present a novel Quiz-based Knowledge Tracing (QKT) model, which effectively integrates the quiz structure of learning interactions. This is achieved by designing two distinct modules by neural networks: one for intra-quiz modeling and another for inter-quiz fusion. Extensive experimental results on public real-world datasets demonstrate that QKT achieves new state-of-the-art performance. The findings of this study suggest that incorporating the quiz structure of learning interactions can efficiently comprehend learners' knowledge states with fewer quizzes, and provides valuable insights into designing effective quizzes with fewer exercises.","PeriodicalId":49763,"journal":{"name":"Neural Networks","volume":"191 ","pages":"107797"},"PeriodicalIF":6.3,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144660885","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

PolarFusion: A multi-modal fusion algorithm for 3D object detection based on polar coordinates. PolarFusion：一种基于极坐标的三维目标检测多模态融合算法。

IF 6.3 1区计算机科学

Neural Networks Pub Date : 2025-10-01 Epub Date: 2025-06-18 DOI: 10.1016/j.neunet.2025.107704

Peicheng Shi, Runshuai Ge, Xinlong Dong, Chadia Chakir, Taonian Liang, Aixi Yang

{"title":"PolarFusion: A multi-modal fusion algorithm for 3D object detection based on polar coordinates.","authors":"Peicheng Shi, Runshuai Ge, Xinlong Dong, Chadia Chakir, Taonian Liang, Aixi Yang","doi":"10.1016/j.neunet.2025.107704","DOIUrl":"10.1016/j.neunet.2025.107704","url":null,"abstract":"Existing 3D object detection algorithms that fuse multi-modal sensor information typically operate in Cartesian coordinates, which can lead to asymmetrical feature information and uneven attention across multiple views. To address this, we propose PolarFusion, the first multi-modal fusion BEV object detection algorithm based on polar coordinates. We designed three specialized modules for this approach: the Polar Region Candidates Generation Module, the Polar Region Query Generation Module, and the Polar Region Information Fusion Module. In the Polar Region Candidates Generation Module, we use a region proposal-based segmentation method to remove irrelevant areas from images, enhancing PolarFusion's information processing efficiency. These segmented image regions are then integrated into the point cloud segmentation task, addressing feature misalignment during fusion. The Polar Region Query Generation Module leverages prior information to generate high-quality target queries, reducing the time spent learning from initialization. For the Polar Region Information Fusion Module, PolarFusion employs a simple yet efficient self-attention to merge internal information from images and point clouds. This captures long-range dependencies in image texture information while preserving the precise positional data from point clouds, enabling more accurate BEV object detection. We conducted extensive experiments on challenging BEV object detection datasets. Both qualitative and quantitative results demonstrate that PolarFusion achieves an NDS of 76.1% and mAP of 74.5% on the nuScenes test set, significantly outperforming Cartesian-based methods. This advancement enhances the environmental perception capabilities of autonomous vehicles and contributes to the development of future intelligent transportation systems. The code will be released at https://github.com/RunshuaiGe/PolarFusion.git.","PeriodicalId":49763,"journal":{"name":"Neural Networks","volume":"190 ","pages":"107704"},"PeriodicalIF":6.3,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144499002","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Cloud-Edge-End Collaborative Dependent Computing Schedule Strategy for Immersive Media 沉浸式媒体的云-边缘协同依赖计算调度策略

IF 2.5 4区计算机科学

Transactions on Emerging Telecommunications Technologies Pub Date : 2025-09-27 DOI: 10.1002/ett.70247

Xiaoxi Wang, Shujie Yang, Hong Tang, Xueying Li, Wei Wang, Hui Xiao, Yuxing Liu, Jia Chen, Enbo Wang, Shaoyun Wu, Mingyu Zhao

{"title":"Cloud-Edge-End Collaborative Dependent Computing Schedule Strategy for Immersive Media","authors":"Xiaoxi Wang, Shujie Yang, Hong Tang, Xueying Li, Wei Wang, Hui Xiao, Yuxing Liu, Jia Chen, Enbo Wang, Shaoyun Wu, Mingyu Zhao","doi":"10.1002/ett.70247","DOIUrl":"https://doi.org/10.1002/ett.70247","url":null,"abstract":"<div>\u0000 \u0000 Immersive media applications often create an immersive experience for users through head-mounted displays. However, the computing power and storage capacity of terminal devices are limited, and the local computing architecture cannot meet the high resolution and low latency requirements of panoramic video frames. As a new computing paradigm, cloud, edge and end collaborative computing architecture selectively schedules computing tasks to cloud servers and edge servers with higher computing power, which can effectively improve computing efficiency. However, for dependent computational tasks, the scheduling of each task needs to consider its previous tasks, network state, and computational resources of different servers. Therefore, how to make computational offloading decisions and resource allocation for dependent tasks is a key issue for collaborative computing architectures. This paper investigates and analyzes the immersive media scenarios and the basic computation offloading strategies, and construct a dependent task model graph and optimization problem model. Based on threshold strategy, greedy strategy of heuristic algorithm and deep reinforcement learning model, a scheduling strategy under collaborative computing architecture is designed to maximize the reward related to delay and cost. Finally, the basic performance of the computational task scheduling strategy based on deep reinforcement learning and greedy policy is verified through simulation experiments. The experimental results show that the algorithm reduces the latency by more than 1.8 ms and increases the timely completion rate by more than <math></math> relative to several basic scheduling schemes, which can effectively improve the service quality and user experience.\u0000 </div>","PeriodicalId":23282,"journal":{"name":"Transactions on Emerging Telecommunications Technologies","volume":"36 10","pages":""},"PeriodicalIF":2.5,"publicationDate":"2025-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145146837","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A WSN-Based WbCNF Algorithm to Enable Cloud Computing and Big Data Analytics in Healthcare 基于wsn的WbCNF算法实现医疗保健领域的云计算和大数据分析

IF 1.8 4区计算机科学

International Journal of Communication Systems Pub Date : 2025-09-27 DOI: 10.1002/dac.70261

S. Ramasamy, V. Baby Vennila

{"title":"A WSN-Based WbCNF Algorithm to Enable Cloud Computing and Big Data Analytics in Healthcare","authors":"S. Ramasamy, V. Baby Vennila","doi":"10.1002/dac.70261","DOIUrl":"https://doi.org/10.1002/dac.70261","url":null,"abstract":"<div>\u0000 \u0000 Cloud computing is a technology that enables the internet-based delivery of computer resources and services, giving customers access to on-demand infrastructure, platforms, and applications without the need for local hardware or direct control. Cloud computing in healthcare is the use of internet-based platforms and services for storing, managing, and processing healthcare data. Cloud computing in big data is the use of cloud-based infrastructure and services to store, manage, and analyze large datasets. This paper proposed a WSN-based Weighted Boolean Conjunctive Normal Form (WbCNF) method. This approach in cloud computing refers to the use of an optimization technique to handle large-scale, distributed problems such as resource allocation, scheduling, and decision-making in cloud environments. Cloud computing environments generate complex and dynamic constraints that can be modeled using weighted CNF formulas to indicate priorities or costs. The work allocation mechanism can be greatly improved, and job execution times can be lowered. This can be performed by employing a revolutionary whale-based convolutional neural framework technique. The Python framework is used in the proposed approach. The experimental results show that the number of tasks required for the experiment has decreased due to the computing time.\u0000 </div>","PeriodicalId":13946,"journal":{"name":"International Journal of Communication Systems","volume":"38 16","pages":""},"PeriodicalIF":1.8,"publicationDate":"2025-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145146618","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

WinDroid: A Novel Framework for Windows and Android Malware Family Classification Using Hierarchical Ensemble Support Vector Machines With Multiview Handcrafted and Deep Learning Features WinDroid: Windows和Android恶意软件家族分类的新框架，使用具有多视图手工和深度学习功能的分层集成支持向量机

IF 2.6 4区计算机科学

IET Information Security Pub Date : 2025-09-27 DOI: 10.1049/ise2/8843518

K. Sundara Krishnan, S. Syed Suhaila

{"title":"WinDroid: A Novel Framework for Windows and Android Malware Family Classification Using Hierarchical Ensemble Support Vector Machines With Multiview Handcrafted and Deep Learning Features","authors":"K. Sundara Krishnan, S. Syed Suhaila","doi":"10.1049/ise2/8843518","DOIUrl":"https://doi.org/10.1049/ise2/8843518","url":null,"abstract":"The rapid growth and diversification of malware variants, driven by advanced code obfuscation, evasion, and antianalysis techniques, present a significant threat to cybersecurity. The inadequacy of traditional methods in accurately classifying these evolving threats highlights the need for effective and robust malware classification techniques. This article presents WinDroid, a novel visualization-based framework for Windows and Android malware family (AMF) classification using hybrid features and hierarchical ensemble learning. The WinDroid system employs a multistage approach to malware classification, transforming binaries into Markov grayscale images, enhanced via contrast-limited-adaptive-histogram-equalization and gamma correction. Deep learning and handcrafted features are extracted and fuzed using graph attention networks (GATs), feeding into hierarchical support vector machines (SVMs) for accurate family classification. This framework effectively reduces information loss, enhances computational efficiency, and demonstrates outstanding performance. WinDroid delivers excellent results, achieving 99.53% accuracy on Windows and 99.65% on AMF classification, along with Cohen’s kappa coefficients of 99.01% and 99.28%, respectively, and outperforming state-of-the-art baseline methods.","PeriodicalId":50380,"journal":{"name":"IET Information Security","volume":"2025 1","pages":""},"PeriodicalIF":2.6,"publicationDate":"2025-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/ise2/8843518","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145146957","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

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