Concurrency and Computation-Practice & Experience最新文献

{"title":"A Novel Ensemble Machine Learning Approach for Interpretable Modeling, Feature Extraction and Selection With Applications to Medical and Biomedical Signals and Data","authors":"Bo Sun,&nbsp;Hua-Liang Wei","doi":"10.1002/cpe.70697","DOIUrl":"https://doi.org/10.1002/cpe.70697","url":null,"abstract":"<p>Feature extraction and selection are crucial in biomedical data analysis to address high dimensionality, reduce computational complexity, and enhance model interpretability. However, traditional methods often focus on individual feature importance, overlooking complex inter-feature relationships, especially when processing and modeling dynamic and time-series data. In this study, we propose a novel framework that integrates Feature Co-occurrence Networks (FCN) with global importance scoring via the PageRank algorithm, which is built on a parametric Nonlinear AutoRegressive with eXogenous inputs (NARX) model structure to better capture temporal dependencies in sequential data. The proposed NARX-FCN-PageRank approach combines the strengths of multiple feature selection strategies while leveraging network analysis to identify stable and representative feature subsets. Extensive evaluations across diverse biomedical datasets, including both static and dynamic scenarios, demonstrate that our method effectively reduces feature dimensionality without compromising predictive performance. Moreover, the network visualizations provide valuable insights into the interdependencies and centrality of selected features, supporting model interpretability and enhancing trustworthiness. The NARX-FCN-PageRank framework thus offers a versatile and interpretable solution for feature selection in biomedical data analysis, with the potential to facilitate more efficient and reliable modeling in clinical and medical research applications.</p>","PeriodicalId":55214,"journal":{"name":"Concurrency and Computation-Practice & Experience","volume":"38 8","pages":""},"PeriodicalIF":1.5,"publicationDate":"2026-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/cpe.70697","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147686203","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}

{"title":"Additional-Processing-Free Multiparty Reversible Data Hiding Over Encrypted Domain","authors":"Bing Chen,&nbsp;Ranran Yang,&nbsp;Bingwen Feng,&nbsp;Xiuye Zhan,&nbsp;Jun Cai","doi":"10.1002/cpe.70708","DOIUrl":"https://doi.org/10.1002/cpe.70708","url":null,"abstract":"<div>\u0000 \u0000 <p>Multiparty reversible data hiding over encrypted domain (MRDH-ED) provides a safeguard mechanism that enables the restoration of the original cover even if some of the data hiders are potentially compromised. Existing MRDH-ED methods are accompanied by additional processing in the cover encryption procedure, which results in high computational consumption. In this paper, an additional-processing-free MRDH-ED method with compatibility is given. The original cover is encrypted into multiple encrypted covers by using a composite-order secret sharing. Additive operation over the composite-order finite field is employed to embed secret data into each encrypted cover. Since the original cover can be encrypted without additional processing in the cover encryption procedure, the computational consumption is reduced. And the embedding capacity is improved, regardless of the distribution of the covers. With the help of the composite-order secret sharing, the proposed method is compatible with other bit-level covers. The proposed scheme's superiority is demonstrated through the presentation of experimental results.</p>\u0000 </div>","PeriodicalId":55214,"journal":{"name":"Concurrency and Computation-Practice & Experience","volume":"38 8","pages":""},"PeriodicalIF":1.5,"publicationDate":"2026-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147686099","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}

{"title":"CG-YOLOv11: A Smoke-Removal-Enhanced Target Detection Method for Indoor Smoke Scenes","authors":"Maoyue Li,&nbsp;Jinhai Zhang,&nbsp;Siqi Liu,&nbsp;Dongpeng Liu","doi":"10.1002/cpe.70709","DOIUrl":"https://doi.org/10.1002/cpe.70709","url":null,"abstract":"<div>\u0000 \u0000 <p>To address the challenges of low detection accuracy, missed detections, and false alarms in indoor fire scenarios caused by smoke, diverse postures of trapped individuals, partial occlusions by obstacles, and cluttered backgrounds, this paper proposes CG-YOLOv11, a smoke-removal-enhanced target detection method for indoor smoke scenes. Firstly, CAA-CycleGAN is employed to remove smoke and enhance image visibility. Specifically, a Color Attenuation Attention (CAA) sub-network is designed, and cyclic perceptual consistency loss together with Color Attenuation Prior (CAP) loss is introduced to improve smoke removal performance for non-uniform smoke images. Secondly, to enhance YOLOv11's feature representation and multi-scale fusion capability under occlusion and small-target conditions, we integrate the Multi-Scale Edge Enhancement Feature (MSEF) module into the original C3k2 module of YOLOv11 to form the C3k2-MSEF module, and further design a Multi-Scale Edge-Enhanced Feature Pyramid Network (MSEFFPN) to improve multi-scale feature fusion. Finally, CAA-CycleGAN and MSEF-MSEFFPN-Modified YOLOv11 (MM-YOLOv11) are cascaded to form the complete CG-YOLOv11 method, thereby further improving overall target detection performance in indoor smoke scenes. Experimental results demonstrate that CG-YOLOv11 achieves a precision of 83.0%, [email protected] of 75.5%, and a detection speed of 106.2 FPS, satisfying the accuracy and real-time requirements for rescue target detection in indoor smoke environments and validating the effectiveness of the proposed method.</p>\u0000 </div>","PeriodicalId":55214,"journal":{"name":"Concurrency and Computation-Practice & Experience","volume":"38 8","pages":""},"PeriodicalIF":1.5,"publicationDate":"2026-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147686002","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}

{"title":"NOA-RAC: An Enhanced Nutcracker Optimization Algorithm for Optimization Tasks","authors":"Haoran Chen,&nbsp;Yukun Wang,&nbsp;Wansheng Cheng,&nbsp;Tianwei Shi","doi":"10.1002/cpe.70717","DOIUrl":"https://doi.org/10.1002/cpe.70717","url":null,"abstract":"<div>\u0000 \u0000 <p>Most real-world problems are constrained continuous variable problems and discrete variable problems. In order to develop an algorithm that can solve these two types of problems in a balanced way, this paper proposes NOA-RAC, an enhanced variant of the Nutcracker Optimization Algorithm (NOA). To address the limitations of NOA, including the exploration-exploitation imbalance and tendency to fall into local optima in certain cases, three enhancement strategies were implemented. First, the implementation of a random subgroup strategy to better balance exploration-exploitation trade-offs. Second, the development of an adaptive fitness update mechanism that enhances population diversity. Finally, incorporation of a retractable transformable cruise strategy improves the algorithm's ability to jump out of local optima. A comprehensive experimental analysis, including effectiveness analysis of improvement strategies, qualitative analysis, non-parametric statistical test, and so forth, was conducted to validate the results of the algorithmic improvements from multiple perspectives. NOA-RAC was quantitatively compared with well-known algorithms of various types proposed in recent years in three tests (CEC2017 benchmark suite, 30 engineering problems, and 12 feature selection problems). Experimental results demonstrate that NOA-RAC exhibits strong competitiveness in solving both discrete-variable and constrained continuous-variable optimization problems, it serves as an effective tool for addressing real-world optimization problems.</p>\u0000 </div>","PeriodicalId":55214,"journal":{"name":"Concurrency and Computation-Practice & Experience","volume":"38 8","pages":""},"PeriodicalIF":1.5,"publicationDate":"2026-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147685973","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}

{"title":"A Hough Transform Algorithm Implementation Using Dynamic Parallelism With CUDA","authors":"Angel S. Pomol-Poot,&nbsp;José L. López-Martínez,&nbsp;Francisco A. Madera-Ramirez,&nbsp;Joel A. Trejo-Sánchez,&nbsp;Francisco Moo-Mena","doi":"10.1002/cpe.70701","DOIUrl":"https://doi.org/10.1002/cpe.70701","url":null,"abstract":"<div>\u0000 \u0000 <p>The Compute Unified Device Architecture (CUDA) parallel programming model has become very popular in image processing methods for detecting shapes. Dynamic parallelism with CUDA allows nesting and recursion algorithms to be implemented by using multiple child kernels created dynamically from their parent kernels. This technique allows the computational resources associated with the Graphics Processing Units (GPUs) to be used more efficiently, providing homogeneous workloads between threads and blocks. This paper proposes the use of dynamic parallelism applied to the image decomposition of the Hough transform to detect straight lines. The comparison results with another parallel algorithm published in the literature, which does not use dynamic parallelism, are presented in terms of time, reducing the total execution time and obtaining a performance of up to 1.7 times faster.</p>\u0000 </div>","PeriodicalId":55214,"journal":{"name":"Concurrency and Computation-Practice & Experience","volume":"38 8","pages":""},"PeriodicalIF":1.5,"publicationDate":"2026-04-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147686097","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}

{"title":"A Complexity Calculation Method for Large Scale Optimization With Evolutionary Algorithms and Metaheuristics","authors":"Zeynep Sari,&nbsp;Mehmet Yildirim","doi":"10.1002/cpe.70702","DOIUrl":"https://doi.org/10.1002/cpe.70702","url":null,"abstract":"<div>\u0000 \u0000 <p>This study proposes a general-purpose computational complexity method for evaluating the success of metaheuristics and evolutionary algorithms used in solving problems of very large-scale global optimization (LSGO) problems. The contribution of this study is not to propose a new asymptotic complexity theory; rather, it aims to define how objective function evaluations (FE) in metaheuristic and evolutionary optimization algorithms can be calculated in a standard, explicit, and reproducible manner. This reveals the impact of design variations such as decomposition, weighting, special operators, and additional trials on total FE consumption, allowing for a fairer budget comparison between different methods. The frequently used big-O computational complexity method is not directly applicable to comparing metaheuristics or evolutionary algorithms. Therefore, researchers have compared the results of their studies with the results of other studies presented in the literature or with benchmark functions to test the performance of their methods, strategies, and improvements. They typically compare solution times or fitness values obtained for a given number of iterations, decision variable sizes, and population size. However, the internal complexity of algorithms, the speed of the computer used, the skill of the programmer, and so on factors often affect the solution time and these cannot be isolated in the comparisons. The proposed method is designed to get rid of the effects of population size, number of iterations, hardware specifications, programmer/implementation differences, and whether a decomposition is used, enabling fair, simple, and efficient comparisons between algorithms. The study also experimentally demonstrates how these parameters affect comparison results and presents a fair evaluation method that balances these effects. This method aims to increase the reliability of methodological comparisons in the context of LSGO and to facilitate more consistent interpretations of performance reports in the literature.</p>\u0000 </div>","PeriodicalId":55214,"journal":{"name":"Concurrency and Computation-Practice & Experience","volume":"38 8","pages":""},"PeriodicalIF":1.5,"publicationDate":"2026-04-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147686094","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}

{"title":"DynaGATNet: A Lightweight Dynamic Graph Attention Network for Multimodal Fusion in Industrial PTFE Blend Ratio Prediction","authors":"Zhengjie Chen,&nbsp;Jiao Tian","doi":"10.1002/cpe.70657","DOIUrl":"https://doi.org/10.1002/cpe.70657","url":null,"abstract":"<div>\u0000 \u0000 <p>Accurate estimation of blend ratios in PTFE suspension processing is important for maintaining stable product quality. But the nonlinear and time-changing behavior of real industrial mixing reduces the usefulness of traditional single-modal soft sensing methods. This study introduces DynaGATNet, a lightweight dynamic graph-based multimodal fusion network that uses image, light, and flow-rate signals for blend ratio prediction. The model uses CNN and GRU encoders to extract features, and it builds dynamic modality graphs based on feature similarity. Then it applies graph attention to achieve adaptive cross-modal fusion, so the model can capture time-changing dependencies that static fusion methods cannot show. Experiments on an industrial dataset show that DynaGATNet reaches strong performance (MAE = 0.071, <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <msup>\u0000 <mrow>\u0000 <mi>R</mi>\u0000 </mrow>\u0000 <mrow>\u0000 <mn>2</mn>\u0000 </mrow>\u0000 </msup>\u0000 </mrow>\u0000 <annotation>$$ {R}^2 $$</annotation>\u0000 </semantics></math> = 0.934, Accuracy = 0.938) while using only 3.6 million parameters and 2.1 ms inference time.</p>\u0000 </div>","PeriodicalId":55214,"journal":{"name":"Concurrency and Computation-Practice & Experience","volume":"38 7","pages":""},"PeriodicalIF":1.5,"publicationDate":"2026-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147585225","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}

{"title":"DynaGATNet: A Lightweight Dynamic Graph Attention Network for Multimodal Fusion in Industrial PTFE Blend Ratio Prediction","authors":"Zhengjie Chen,&nbsp;Jiao Tian","doi":"10.1002/cpe.70657","DOIUrl":"10.1002/cpe.70657","url":null,"abstract":"<div>\u0000 \u0000 <p>Accurate estimation of blend ratios in PTFE suspension processing is important for maintaining stable product quality. But the nonlinear and time-changing behavior of real industrial mixing reduces the usefulness of traditional single-modal soft sensing methods. This study introduces DynaGATNet, a lightweight dynamic graph-based multimodal fusion network that uses image, light, and flow-rate signals for blend ratio prediction. The model uses CNN and GRU encoders to extract features, and it builds dynamic modality graphs based on feature similarity. Then it applies graph attention to achieve adaptive cross-modal fusion, so the model can capture time-changing dependencies that static fusion methods cannot show. Experiments on an industrial dataset show that DynaGATNet reaches strong performance (MAE = 0.071, <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <msup>\u0000 <mrow>\u0000 <mi>R</mi>\u0000 </mrow>\u0000 <mrow>\u0000 <mn>2</mn>\u0000 </mrow>\u0000 </msup>\u0000 </mrow>\u0000 <annotation>$$ {R}^2 $$</annotation>\u0000 </semantics></math> = 0.934, Accuracy = 0.938) while using only 3.6 million parameters and 2.1 ms inference time.</p>\u0000 </div>","PeriodicalId":55214,"journal":{"name":"Concurrency and Computation-Practice & Experience","volume":"38 7","pages":""},"PeriodicalIF":1.5,"publicationDate":"2026-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147585224","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}

{"title":"A Multi-Strategy Enhanced Dung Beetle Optimization Approach for Cooperative Path Planning in Multi-UAVs Systems","authors":"Lieping Zhang,&nbsp;Xinpeng Zheng,&nbsp;Jinming Liu,&nbsp;Ming Zhang,&nbsp;Zhentao Yu,&nbsp;Xuefeng Pei,&nbsp;Kaike Tu","doi":"10.1002/cpe.70681","DOIUrl":"10.1002/cpe.70681","url":null,"abstract":"<div>\u0000 \u0000 <p>Cooperative path planning for multi-unmanned aerial vehicles (UAVs) is a challenging and computationally intensive optimization task, which requires the coordinated generation of feasible and high-quality trajectories under complex environmental constraints. The dung beetle optimization algorithm (DBO) provides a viable approach to addressing this problem; however, its global search capability is relatively limited and it is prone to being trapped in local optima. To address these challenges, this paper proposes a multi-strategy enhanced DBO (MSDBO) approach for cooperative path planning in multi-UAVs systems. First, a mathematical model incorporating multiple constraints, including flight distance, threat avoidance, and spatial cooperation, is established to transform the complex multi-UAVs cooperative path planning problem into a multi-constrained optimization problem. Subsequently, MSDBO is developed by integrating four innovative strategies: (1) Latin hypercube sampling is employed to enhance population diversity during initialization; (2) bidirectional guided search strategy is introduced into the rolling phase to strengthen global exploration and improve the ability to escape local optima; (3) adaptive spiral search strategy is incorporated into the foraging phase to better balance global exploration and local convergence; and (4) longitudinal crossover–mutation strategy is applied in the later stage, performing cross-dimensional perturbations among different dimensions of elite individuals to alleviate high-dimensional variable coupling and maintain population diversity. Through the synergistic interaction of these strategies, DBO achieves a more effective dynamic balance between exploration and exploitation, thereby significantly improving its overall optimization performance. The performance of MSDBO is evaluated using the CEC2017 and CEC2022 benchmark functions and compared against nine competing algorithms. Experimental results indicate that MSDBO achieves superior solution accuracy, convergence speed, and stability in numerical optimization tasks. Further experiments conducted in four distinct multi-UAVs path planning environments demonstrate that MSDBO obtains lower total costs and generates higher-quality cooperative trajectories, reflecting strong robustness and practical engineering applicability.</p>\u0000 </div>","PeriodicalId":55214,"journal":{"name":"Concurrency and Computation-Practice & Experience","volume":"38 7","pages":""},"PeriodicalIF":1.5,"publicationDate":"2026-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147683959","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}

{"title":"A Lightweight and Efficient Insulator Defect Detection Model for Unmanned Aerial Vehicle Inspection","authors":"A. Xin Fang,&nbsp;B. Shaobo Yan,&nbsp;C. Jian Ding,&nbsp;D. Lan Shen,&nbsp;E. Jingxin Li,&nbsp;F. Liangzhi Su,&nbsp;Fangxing Lyu","doi":"10.1002/cpe.70682","DOIUrl":"10.1002/cpe.70682","url":null,"abstract":"<div>\u0000 \u0000 <p>Insulator defect detection is crucial for ensuring the safety of power grids, but it faces challenges such as complex environmental interference, small-scale target recognition, and edge device deployment. This paper proposes an improved lightweight model based on YOLOv11. The model takes Faster-Net as the backbone and uses partial convolution to reduce computational costs. The neck network is constructed through hierarchical feature fusion blocks to enhance multiscale feature fusion. Introduce a Parallel Patch-aware Attention (PPA) module into the detection head to enable the model to adaptively focus on the key defect areas in the image. Meanwhile, a Lightweight Adaptive Extraction (LAE) module is integrated to optimize the feature calculation process. Experiments show that this model achieves a mAP50 accuracy rate of 93.2% and a precision of 93.7% on public datasets, with the parameter count and computational load being only 2.3 M and 4.6 GFLOPs respectively. The ablation experiment verified the effectiveness of the module, and cross-dataset tests confirmed the excellent generalization performance. The model was successfully deployed on the Jetson Xavier NX platform and the M350 Unmanned Aerial Vehicle (UAV), achieving real-time end-to-end detection. This research provides a complete solution for intelligent power inspection in complex environments that is highly accurate, lightweight and easy to deploy.</p>\u0000 </div>","PeriodicalId":55214,"journal":{"name":"Concurrency and Computation-Practice & Experience","volume":"38 7","pages":""},"PeriodicalIF":1.5,"publicationDate":"2026-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147585141","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}