Complex & Intelligent Systems最新文献_第7页

Attribute grouping-based categorical outlier detection using causal coupling weight 基于属性分组的基于因果耦合权的分类离群值检测

IF 5.8 2区计算机科学

Complex & Intelligent Systems Pub Date : 2025-04-15 DOI: 10.1007/s40747-025-01869-x

Yijing Song, Jianying Liu, Jifu Zhang

{"title":"Attribute grouping-based categorical outlier detection using causal coupling weight","authors":"Yijing Song, Jianying Liu, Jifu Zhang","doi":"10.1007/s40747-025-01869-x","DOIUrl":"https://doi.org/10.1007/s40747-025-01869-x","url":null,"abstract":"For high-dimensional datasets, outlier objects can be effectively identified and extracted with the help of the coupling relationship between any two attributes. However, when all the coupling is used directly, there is a phenomenon of pseudo-correlation between attribute values that results in redundant coupling and affects the effectiveness of high-dimensional outlier detection. In this paper, a novel attribute group-based outlier detection approach for categorical data is proposed by using the attribute causal coupling weights to depict abnormal degree of the attributes. Firstly, according to the local and global correlation, all attributes are automatically divided into several groups, and all attributes in each group have a high correlation or association. Secondly, new concepts of causal pseudo-correlation are defined, and a case analysis that the pseudo-correlation is the main cause of attribute redundant coupling. By constructing attribute causality graph using the graph structure, the pseudo-correlation is effectively avoided in each attribute group. Thirdly, attribute causal coupling weight formula, which effectively characterizes the abnormal degree of attribute and reflects the causal coupling between any two attributes, is constructed from the causality graph. An attribute group-based outlier detection algorithm powered by causal coupling weight is proposed for categorical data. In the end, experimental results on the UCI and synthetic datasets validate that the algorithm has good outlier detection performance and effectively alleviates the effect of redundant coupling among attributes. Importantly, compared with the competitive methods, the algorithm bolsters the AUC index and the detection efficiency by averages of 10.97 and 42.84(%), respectively.","PeriodicalId":10524,"journal":{"name":"Complex & Intelligent Systems","volume":"7 1","pages":""},"PeriodicalIF":5.8,"publicationDate":"2025-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143832155","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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

WTI-SLAM: a novel thermal infrared visual SLAM algorithm for weak texture thermal infrared images WTI-SLAM：一种针对弱纹理热红外图像的红外视觉SLAM算法

IF 5.8 2区计算机科学

Complex & Intelligent Systems Pub Date : 2025-04-15 DOI: 10.1007/s40747-025-01858-0

Sen Li, Xiaofei Ma, Rui He, Yuanrui Shen, He Guan, Hezhao Liu, Fei Li

{"title":"WTI-SLAM: a novel thermal infrared visual SLAM algorithm for weak texture thermal infrared images","authors":"Sen Li, Xiaofei Ma, Rui He, Yuanrui Shen, He Guan, Hezhao Liu, Fei Li","doi":"10.1007/s40747-025-01858-0","DOIUrl":"https://doi.org/10.1007/s40747-025-01858-0","url":null,"abstract":"This study addresses the challenges of robotic localization and navigation in visually degraded environments, such as low illumination and adverse weather conditions, by proposing a novel thermal infrared visual SLAM (Simultaneous Localization and Mapping) algorithm. The research introduces a new infrared visual odometry that integrates feature-based methods with optical flow techniques, enhancing image processing capabilities to mitigate the issues of high time overhead and cumulative errors in traditional feature-based odometry. Additionally, an improved bag-of-words model is employed to develop a novel loop closure detection method that addresses the challenge of scale drift. The purpose of this paper is to address the shortcomings in robustness and accuracy encountered by existing visual SLAM algorithms when processing low-texture thermal infrared images. Experimental validation using the JPL, Airey, and ViViD++ thermal infrared datasets demonstrates that the proposed algorithm exhibits superior real-time performance and robustness across various environments. Compared to mainstream thermal infrared visual SLAM algorithms, WTI-SLAM significantly improves the robot localization accuracy in weak-texture thermal infrared image scenarios, reducing the localization error by approximately 46%. This research offers an innovative and effective solution for achieving stable SLAM systems for robots operating in complex and visually degraded environments.","PeriodicalId":10524,"journal":{"name":"Complex & Intelligent Systems","volume":"74 1","pages":""},"PeriodicalIF":5.8,"publicationDate":"2025-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143832157","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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

Effective defense against physically embedded backdoor attacks via clustering-based filtering 通过基于聚类的过滤有效防御物理嵌入的后门攻击

IF 5.8 2区计算机科学

Complex & Intelligent Systems Pub Date : 2025-04-15 DOI: 10.1007/s40747-025-01876-y

Mohammed Kutbi

{"title":"Effective defense against physically embedded backdoor attacks via clustering-based filtering","authors":"Mohammed Kutbi","doi":"10.1007/s40747-025-01876-y","DOIUrl":"https://doi.org/10.1007/s40747-025-01876-y","url":null,"abstract":"<h3 data-test=\"abstract-sub-heading\">Abstract</h3>Backdoor attacks pose a severe threat to the integrity of machine learning models, especially in real-world image classification tasks. In such attacks, adversaries embed malicious behaviors triggered by specific patterns in the training data, causing models to misclassify whenever the trigger is present. This paper introduces a novel, model-agnostic defense that systematically detects and removes backdoor-infected samples using a synergy of dimensionality reduction and unsupervised clustering. Unlike most existing methods that address digitally added triggers, our approach specifically targets physically embedded triggers (e.g., a bandage placed on a face), which closely resemble real-world occlusions and are therefore harder to detect. We first extract high-level features from a trusted, pre-trained model, reduce the feature dimensionality via Principal Component Analysis (PCA), and then fit Gaussian Mixture Models (GMMs) to cluster suspicious samples. By identifying and filtering out outlying clusters, we effectively isolate poisoned images without assuming knowledge of the trigger or requiring access to the victim model. Extensive experiments on face versus non-face classification demonstrate that our defense substantially reduces attack success rates while preserving high accuracy on clean data, offering a practical and robust solution against challenging backdoor scenarios.<h3 data-test=\"abstract-sub-heading\">Graphic Abstract</h3>","PeriodicalId":10524,"journal":{"name":"Complex & Intelligent Systems","volume":"108 1","pages":""},"PeriodicalIF":5.8,"publicationDate":"2025-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143832156","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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

Predicting trajectories of coastal area vessels with a lightweight Slice-Diff self attention 用轻量切片-差分自注意预测沿海地区船舶轨迹

IF 5.8 2区计算机科学

Complex & Intelligent Systems Pub Date : 2025-04-12 DOI: 10.1007/s40747-025-01877-x

Jinxu Zhang, Jin Liu, Xiliang Zhang, Lai Wei, Zhongdai Wu, Junxiang Wang

{"title":"Predicting trajectories of coastal area vessels with a lightweight Slice-Diff self attention","authors":"Jinxu Zhang, Jin Liu, Xiliang Zhang, Lai Wei, Zhongdai Wu, Junxiang Wang","doi":"10.1007/s40747-025-01877-x","DOIUrl":"https://doi.org/10.1007/s40747-025-01877-x","url":null,"abstract":"Accurate prediction of vessel trajectories in coastal areas poses a significant challenge due to the large number of irregular trajectories. Existing trajectory prediction studies predominantly employ recurrent neural network (RNN) and Transformer-based methods. However, the former often encounter challenges such as gradient vanishing or exploding, and the latter tend to focus on global temporal dependencies, making it difficult to capture local irregular trajectory features in coastal maritime areas. Recently, graph-based methods have also been used to predict trajectories, however processing graph-structured data introduces significant increase in computation. In responding to these, this paper proposes a framework based on a novel lightweight Slice-Diff self attention, which consists of several key components. Firstly, the trajectory slice difference encoder (TSDE) utilizes slice embedding (SE) to enrich the cross dimensional dependencies contained in the input sequence, and then combines Slice-Diff self attention (SDSA) and fine-grained convolution (FGC) to comprehensively capture sequence-specific positional and directional information. Additionally, an auxiliary model, stepping bidirectional long short-term memory (S-BiLSTM) is developed to capture global temporal dependencies within the whole sequence. Finally, the fine-grained trajectory features obtained from TSDE and the global temporal dependencies compensated by S-BiLSTM are combined through the fully connected layer to predict coastal vessel trajectories. Extensive experimental results on three real-world automatic identification system (AIS) datasets demonstrate the effectiveness of proposed framework against other baselines.","PeriodicalId":10524,"journal":{"name":"Complex & Intelligent Systems","volume":"14 1","pages":""},"PeriodicalIF":5.8,"publicationDate":"2025-04-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143823023","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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

Micro-expression spotting based on multi-modal hierarchical semantic guided deep fusion and optical flow driven feature integration 基于多模态分层语义引导深度融合和光流驱动特征集成的微表情识别

IF 5.8 2区计算机科学

Complex & Intelligent Systems Pub Date : 2025-04-11 DOI: 10.1007/s40747-025-01855-3

Haolin Chang, Zhihua Xie, Fan Yang

{"title":"Micro-expression spotting based on multi-modal hierarchical semantic guided deep fusion and optical flow driven feature integration","authors":"Haolin Chang, Zhihua Xie, Fan Yang","doi":"10.1007/s40747-025-01855-3","DOIUrl":"https://doi.org/10.1007/s40747-025-01855-3","url":null,"abstract":"Micro-expression (ME), as an involuntary and brief facial expression, holds significant potential applications in fields such as political psychology, lie detection, law enforcement, and healthcare. Most existing micro-expression spotting (MES) methods predominantly learn from optical flow features while neglecting the detailed information contained in RGB images. To address this issue, this paper proposes a multi-scale hierarchical semantic-guided end-to-end multimodal fusion framework based on Convolutional Neural Network (CNN)-Transformer for MES, named MESFusion. Specifically, to obtain cross-modal complementary information, this scheme sequentially constructs a Multi-Scale Feature Extraction Module (MFEM) and a Multi-scale hierarchical Semantic-Guided Fusion Module (MSGFM). By introducing an Optical Flow-Driven fusion feature Integration Module (OF-DIM), the correlation of non-scale fusion features is modeled in the channel dimension. Moreover, guided by the optical flow motion information, this approach can adaptively focus on facial motion areas and filter out interference information in cross-modal fusion. Extensive experiments conducted on the CAS(ME)2 dataset and the SAMM Long Videos dataset demonstrate that the MESFusion model surpasses competitive baselines and achieves new state-of-the-art results.","PeriodicalId":10524,"journal":{"name":"Complex & Intelligent Systems","volume":"39 1","pages":""},"PeriodicalIF":5.8,"publicationDate":"2025-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143819343","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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

Gaitformer: a spatial-temporal attention-enhanced network without softmax for Parkinson’s disease early detection Gaitformer：用于帕金森病早期检测的无软最大值的时空注意力增强网络

IF 5.8 2区计算机科学

Complex & Intelligent Systems Pub Date : 2025-04-11 DOI: 10.1007/s40747-025-01830-y

Shupei Jiao, Hua Huo, Wei Liu, Changwei Zhao, Lan Ma, Jinxuan Wang, Ningya Xu, Chen Zhang, Dongfang Li

{"title":"Gaitformer: a spatial-temporal attention-enhanced network without softmax for Parkinson’s disease early detection","authors":"Shupei Jiao, Hua Huo, Wei Liu, Changwei Zhao, Lan Ma, Jinxuan Wang, Ningya Xu, Chen Zhang, Dongfang Li","doi":"10.1007/s40747-025-01830-y","DOIUrl":"https://doi.org/10.1007/s40747-025-01830-y","url":null,"abstract":"Gait analysis is an increasingly expanding research field, characterized by the application of non-invasive sensors and machine learning techniques across various domains. Using these advanced technologies, researchers can deep dive into understanding human gait and movement patterns, providing robust support for applications such as medical diagnosis, rehabilitation, and sports optimization. In this paper, we focus primarily on analyzing the gait features of a large population and emphasize the study of representative features of gait in terms of both temporal and spatial dimensions. By analyzing parameters such as pressure distribution, gait cycles, and gait features of the foot sole, we aim to evaluate an individual’s gait function and detect and diagnose gait-related diseases such as Parkinson’s disease. By integrating spatiotemporal feature information and employing XNorm modules and sparse attention mechanisms in the spatiotemporal encoder to enhance gait feature extraction and model generalization capabilities, our experimental results show that our model achieves a classification accuracy of 92.3%. This also indicates that our Gaitformer demonstrates considerable potential in medical diagnosis models.","PeriodicalId":10524,"journal":{"name":"Complex & Intelligent Systems","volume":"106 1","pages":""},"PeriodicalIF":5.8,"publicationDate":"2025-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143819341","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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

Graph-based adaptive feature fusion neural network model for person-job fit 基于图的人-工匹配自适应特征融合神经网络模型

IF 5.8 2区计算机科学

Complex & Intelligent Systems Pub Date : 2025-04-11 DOI: 10.1007/s40747-025-01834-8

Xia Xue, Feilong Wang, Jingwen Wang, Bo Ma, Yuyang Yu, Shuling Gao, Jing Chen, Baoli Wang

{"title":"Graph-based adaptive feature fusion neural network model for person-job fit","authors":"Xia Xue, Feilong Wang, Jingwen Wang, Bo Ma, Yuyang Yu, Shuling Gao, Jing Chen, Baoli Wang","doi":"10.1007/s40747-025-01834-8","DOIUrl":"https://doi.org/10.1007/s40747-025-01834-8","url":null,"abstract":"Online recruitment services are rapidly transforming traditional hiring practices in the job market. Accurate person-job fit is crucial for intelligent recruitment. Previous studies on person-job fit fail to explore job seekers’ resume information from a multi-perspective approach, and neglect the sustainable learning of resume features. To address this, the present paper proposes a Graph-based Person-Job Fit Neural Network Fusion (GPJFNNF) model. Specifically, the model first generates local semantic representations of job requirements and resume text using the BERT model. Next, a graph structure is constructed based on historical successful recruitment records, and the constructed resume graph is input into a graph neural network to obtain a global semantic representation of the resume. Finally, the adaptive feature fusion mechanism is used to fuse the local and global semantics of the resume, and the final semantic representation of the resume, along with the semantic representation of the job requirements, being input into the person-job fit layer. Experimental results demonstrate that the proposed model achieves 94.63%, 94.15%, 95.04%, and 94.59% in the person-job fit task in terms of accuracy, precision, recall, and F1, respectively, significantly outperforming state-of-the-art baselines.","PeriodicalId":10524,"journal":{"name":"Complex & Intelligent Systems","volume":"90 1","pages":""},"PeriodicalIF":5.8,"publicationDate":"2025-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143819340","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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

Wavelet attention-based implicit multi-granularity super-resolution network 基于小波注意的隐式多粒度超分辨网络

IF 5.8 2区计算机科学

Complex & Intelligent Systems Pub Date : 2025-04-11 DOI: 10.1007/s40747-025-01862-4

Chen Boying, Shi Jie

{"title":"Wavelet attention-based implicit multi-granularity super-resolution network","authors":"Chen Boying, Shi Jie","doi":"10.1007/s40747-025-01862-4","DOIUrl":"https://doi.org/10.1007/s40747-025-01862-4","url":null,"abstract":"Image super-resolution (SR) is a fundamental challenge in the field of computer vision. Recently, Convolutional Neural Network (CNN)-based methods for image SR have achieved significant progress across various SR tasks. However, most current research focuses on designing deeper and wider architectures, often sacrificing computational burden and speed in order to improve image SR quality. To achieve more efficient SR methods, this paper proposes a Wavelet Attention Network (WANet) for image SR. Firstly, a wavelet-based attention module is proposed. Compared to existing self-attention modules, the wavelet attention module decomposes image features into different frequency components using wavelet transforms. It then applies a self-attention mechanism to capture multi-scale features, enabling a more efficient and larger receptive field to help the network capture long-range feature dependencies. Secondly, local implicit features are introduced to enhance the encoder’s ability to aggregate local neighborhood features. Finally, coarse and fine-grained interwoven pixel features are collaboratively associated to improve the performance of the implicit feature decoder. Experimental comparisons with state-of-the-art SR methods demonstrate the effectiveness and superiority of WANet in the field of image SR.","PeriodicalId":10524,"journal":{"name":"Complex & Intelligent Systems","volume":"108 1","pages":""},"PeriodicalIF":5.8,"publicationDate":"2025-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143819342","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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 two-stage algorithm based on greedy ant colony optimization for travelling thief problem 一种基于贪心蚁群优化的两阶段算法求解行窃问题

IF 5.8 2区计算机科学

Complex & Intelligent Systems Pub Date : 2025-04-11 DOI: 10.1007/s40747-025-01865-1

Zheng Zhang, Xiao-Yun Xia, Zi-Jia Wang, You-Zhen Jin, Wei-Zhi Liao, Jun Zhang

{"title":"A two-stage algorithm based on greedy ant colony optimization for travelling thief problem","authors":"Zheng Zhang, Xiao-Yun Xia, Zi-Jia Wang, You-Zhen Jin, Wei-Zhi Liao, Jun Zhang","doi":"10.1007/s40747-025-01865-1","DOIUrl":"https://doi.org/10.1007/s40747-025-01865-1","url":null,"abstract":"The travelling thief problem (TTP) combines two NP-hard problems, traveling salesman problem (TSP) and knapsack problem (KP), which is more complicated for solving. In TTP, the salesman needs to choose the travel route and select the items at the same time to maximize the profit. Consequently, the resolution of TTP essentially encompasses two stages, including route planning and item selection. In the first-stage of route planning, conventional algorithms solely on distance minimization and ignore the attributes of items. To address this issue, an item classification algorithm (ICA) is proposed to compute an optimized combination of items in advance based on their profits and distribution patterns. Furthermore, a greedy ant colony optimization (GACO) is proposed to plan travel route which places the city, where the selected item located, at the later segment of the travel route, optimizing the salesman’s profit. Hence, GACO can produce a travel route that facilitates the optimization in the second-stage item selection scheme. Experimental results illustrate that GACO is more facilitative in optimizing TTP compared to the route obtains by traditional Lin–Kernighan (LK) heuristic rules. With regard to item selection in the second-stage, the traditional algorithm’s item selection strategy requires the design of complex rules, and usually only local search is performed on items, which makes the algorithm prone to falling into local optima. This paper designs a genetic item search strategy (GISS) based on genetic algorithm (GA), GISS does not require the design of complex rules and can perform an effective global search on items. The experimental results also show the efficacy of GISS in item selection schemes for TTP, which can outperform other state-of-the-art algorithms.","PeriodicalId":10524,"journal":{"name":"Complex & Intelligent Systems","volume":"40 1","pages":""},"PeriodicalIF":5.8,"publicationDate":"2025-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143819395","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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

Fractals in $$S_b$$ -metric spaces S_b$$$度量空间中的分形

IF 5.8 2区计算机科学

Complex & Intelligent Systems Pub Date : 2025-04-03 DOI: 10.1007/s40747-025-01849-1

Fahim Ud Din, Sheeza Nawaz, Adil Jhangeer, Fairouz Tchier

{"title":"Fractals in $$S_b$$ -metric spaces","authors":"Fahim Ud Din, Sheeza Nawaz, Adil Jhangeer, Fairouz Tchier","doi":"10.1007/s40747-025-01849-1","DOIUrl":"https://doi.org/10.1007/s40747-025-01849-1","url":null,"abstract":"This study aims to discover attractors for fractals by using generalized F-contractive iterated function system, which falls within a distinct category of mappings defined on (S_b)-metric spaces. In particular, we investigate how these systems, when subjected to specific F-contractive conditions, can lead to the identification of a unique attractor. We achieve a diverse range of outcomes for iterated function systems that adhere to a unique set of generalized F-contractive conditions. Our approach includes a detailed theoretical framework that establishes the existence and uniqueness of attractors in these settings. We provide illustrative examples to bolster the findings established in this work and use the functions given in the example to construct fractals and discuss the convergence of the obtained fractals via iterated function system to an attractor. These examples demonstrate the practical application of our theoretical results, showcasing the convergence behavior of fractals generated by our proposed systems. These outcomes extend beyond the scope of various existing results found in the current body of literature. By expanding the applicability of F-contractive conditions, our findings contribute to the broader understanding of fractal geometry and its applications, offering new insights and potential directions for future research in this area.","PeriodicalId":10524,"journal":{"name":"Complex & Intelligent Systems","volume":"32 1","pages":""},"PeriodicalIF":5.8,"publicationDate":"2025-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143766536","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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