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A Method for Fast Feature Selection Utilizing Cross-Similarity within the Context of Fuzzy Relations

Computers, Materials & Continua 2025年第4期83卷 1195-1218页

作者： Wenchang Yu Xiaoqin Ma Zheqing Zhang Qinli Zhang School of Big Data and Artificial Intelligence Chizhou UniversityChizhou247000China Anhui Education Big Data Intelligent Perception and Application Engineering Research Center Anhui Provincial Joint Construction Key Laboratory of Intelligent Education Equipment and TechnologyChizhou247000China

Feature selection methods rooted in rough sets confront two notable limitations:their high computa-tional complexity and sensitivity to noise,rendering them impractical for managing large-scale and noisy *** primary issue stems from these methods’undue reliance on all *** overcome these challenges,we introduce the concept of cross-similarity grounded in a robust fuzzy relation and design a rapid and robust feature selection ***,we construct a robust fuzzy relation by introducing a truncation ***,based on this fuzzy relation,we propose the concept of cross-similarity,which emphasizes the sample-to-sample similarity relations that uniquely determine feature importance,rather than considering all such relations *** studying the manifestations and properties of cross-similarity across different fuzzy granularities,we propose a forward greedy feature selection algorithm that leverages cross-similarity as the foundation for information *** algorithm significantly reduces the time complexity from O(m2n2)to O(mn2).Experimental findings reveal that the average runtime of five state-of-the-art comparison algorithms is roughly 3.7 times longer than our algorithm,while our algorithm achieves an average accuracy that surpasses those of the five comparison algorithms by approximately 3.52%.This underscores the effectiveness of our *** paper paves the way for applying feature selection algorithms grounded in fuzzy rough sets to large-scale gene datasets.

关键词： Fuzzy rough sets feature selection cross-similarity fuzzy relations

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RE-SEGNN:recurrent semantic evidence-aware graph neural network for temporal knowledge graph forecasting

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Science China(Information Sciences) 2025年第2期68卷 103-119页

作者： Wenyu CAI Mengfan LI Xuanhua SHI Yuanxin FAN Quntao ZHU Hai JIN National Engineering Research Center for Big Data Technology and System Services Computing Technology and System LabCluster and Grid Computing LabSchool of Computer Science and TechnologyHuazhong University of Science and Technology

Temporal knowledge graph(TKG) reasoning, has seen widespread use for modeling real-world events, particularly in extrapolation settings. Nevertheless, most previous studies are embedded models, which require both entity and relation embedding to make predictions, ignoring the semantic correlations among different entities and relations within the same timestamp. This can lead to random and nonsensical predictions when unseen entities or relations occur. Furthermore, many existing models exhibit limitations in handling highly correlated historical facts with extensive temporal depth. They often either overlook such facts or overly accentuate the relationships between recurring past occurrences and their current counterparts. Due to the dynamic nature of TKG, effectively capturing the evolving semantics between different timestamps can be *** address these shortcomings, we propose the recurrent semantic evidenceaware graph neural network(RE-SEGNN), a novel graph neural network that can learn the semantics of entities and relations simultaneously. For the former challenge, our model can predict a possible answer to missing quadruples based on semantics when facing unseen entities or relations. For the latter problem, based on an obvious established force, both the recency and frequency of semantic history tend to confer a higher reference value for the current. We use the Hawkes process to compute the semantic trend, which allows the semantics of recent facts to gain more attention than those of distant facts. Experimental results show that RE-SEGNN outperforms all SOTA models in entity prediction on 6 widely used datasets, and 5 datasets in relation prediction. Furthermore, the case study shows how our model can deal with unseen entities and relations.

关键词： knowledge graph reasoning temporal knowledge graph Hawkes process semantic evidence

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Next timestamp prediction in business process monitoring using large language models 2

Next timestamp prediction in business process monitoring usi...

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2nd International Conference on big data, Computational Intelligence, and Applications, BDCIA 2024

作者： Xu, Yifei Fang, Huan School of Mathematics and Big Data Anhui University of Science and Technology Anhui Huainan China Anhui Province Engineering Laboratory for Big Data Analysis and Early Warning Technology of Coal Mine Safety Anhui Huainan China

ISBN: (纸本)9781510689053

Predictive business process monitoring is a critical domain within process mining, with the primary goal of forecasting characteristics of the next event or the remaining time for process executions. Large language models (LLMs) such as Generative Pretrained Transformer (GPT) and Tongyi Qianwen (Qwen) have demonstrated exceptional performance in natural language processing but face challenges when applied to process data prediction. This paper introduces LLM4NT, which leverages the decoder component of the Qwen2.5-0.5B model as its backbone. By employing a multi-head cross-attention mechanism, it reprograms process data and textual information, aligning process and text modalities to achieve precise predictions of the next timestamp in predictive monitoring. Experimental results on multiple real-life event logs demonstrate that LLM4NT achieves state-of-the-art average performance in next-timestamp prediction tasks, showcasing its powerful capabilities in business process reasoning. © 2025 SPIE.

关键词： Deep neural networks

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Finite-Time Anti-Synchronization for Memristive Neural Networks with Time-Varying Delays

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IAENG International Journal of Applied Mathematics 2025年第3期55卷 611-617页

作者： Duan, Lian Zhang, Ziyue Li, Ziyang Anhui Province Engineering Laboratory for Big Data Analysis and Early Warning Technology of Coal Mine Safety Department of Mathematics Anhui University of Science and Technology Huainan232001 China Department of Mathematics Anhui University of Science and Technology Huainan232001 China

This paper addresses the finite-time anti-synchronization issue for a type of delayed memristive neural networks. By designing a novel memoryless state-feedback controller, novel criteria on finite-time anti-synchronization of the addressed system are discovered based on drive-response framework, rigorous mathematical analysis techniques and differential inclusions theory. The established theoretical results indicate that the switching between finite-time and fixed-time anti-synchronization depends on the position of the initial functions, which are essentially different from existing switching mechanism. In addition, a simulated example is given to verify the validity of the theoretical findings. © (2025), (International Association of Engineers). All rights reserved.

关键词： State feedback

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New Results on Almost Periodic Dynamics of a Delayed Nicholson’s Blowflies Model with Feedback Control

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IAENG International Journal of Applied Mathematics 2025年第4期55卷 842-848页

作者： Sun, Nan Duan, Lian Xie, Dong Department of Mathematics Anhui University of Science and Technology Huainan232001 China Anhui Province Engineering Laboratory for Big Data Analysis Early Warning Technology of Coal Mine Safety Department of Mathematics Anhui University of Science and Technology Huainan232001 China Department of Electronic and Information Engineering Bozhou University Bozhou236800 China

This paper considers a delayed almost periodic Nicholson’s blowflies model with feedback control. Through employing the almost periodic differential equations theory and Lyapunov functional approach, some principles are set up for ensuring the persistence, existence and global asymptotic stability of positive almost periodic solutions of the considered model. Furthermore, a numerical example is provided for validating the theoretical analysis. The achieved results are novel and serve as an addition to the existing ones. © (2025), (International Association of Engineers). All rights reserved.

关键词： Lyapunov functions

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SAMFNet: Scene-aware sampling and multi-stage fusion for multimodal 3D object detection

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Alexandria engineering Journal 2025年 126卷 90-104页

作者： Wang, Baotong Xia, Chenxing Gao, Xiuju Ge, Bin Li, Kuan-Ching Fang, Xianjin Zhang, Yan Yang, Yuan College of Computer Science and Engineering Anhui University of Science and Technology Huainan232001 China College of Electrical and Information Engineering Anhui University of Science and Technology Huainan232001 China Department of Computer Science and Information Engineering Providence University Taichung43301 China The School of Electronics and Information Engineering Anhui University Hefei230039 China School of Mathematics and Big Data Anhui University of Science and Technology Huainan232001 China

Recently, multimodal 3D object detection (M3OD) that fuses the complementary information from LiDAR data and RGB images has gained significant attention. However, the inherent structural differences between point clouds and images pose fusion challenges, significantly hindering the exploration of correlations within multimodal data. To address this issue, this paper introduces an enhanced multimodal 3D object detection framework (SAMFNet), which leverages virtual point clouds generated from depth completion. Specifically, we design a scene-aware sampling module (SASM) that employs tailored sampling strategies for different bins based on the density distribution of point clouds. This effectively alleviates the detection bias problem while ensuring the key information of virtual points, significantly reducing the computational cost. In addition, we introduce a multi-stage feature fusion module (MSFFM) that embeds point-level and regional-adaptive feature fusion strategies to generate more informative multimodal features by fusing features with different granularities. To further improve the accuracy of model detection, we also introduce a confidence prediction branch unit (CPBU), which improves the detection accuracy by predicting the confidence of feature classification in the intermediate stage. Extensive experiments on the challenging KITTI dataset demonstrate the validity of our model. © 2025 The Authors

关键词： Image fusion

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A Helmet Detection Algorithm Based on Transformers with Deformable Attention Module

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Chinese Journal of Electronics 2025年第1期34卷 229-241页

作者： Songle Chen Hongbo Sun Yuxin Wu Lei Shang Xiukai Ruan Engineering Research Center of Post Big Data Technology and Application of Jiangsu Province Nanjing University of Posts and Telecommunications State Key Lab for Novel Software Technology Nanjing University Institute of Intelligent Locks Wenzhou University

Wearing a helmet is one of the effective measures to protect workers' safety. To address the challenges of severe occlusion, multi-scale, and small target issues in helmet detection, this paper proposes a helmet detection algorithm based on deformable attention transformers. The main contributions of this paper are as follows. A compact end-to-end network architecture for safety helmet detection based on transformers is proposed. It cancels the computationally intensive transformer encoder module in the existing detection transformer(DETR) and uses the transformer decoder module directly on the output of feature extraction for query decoding, which effectively improves the efficiency of helmet detection. A novel feature extraction network named Swin transformer with deformable attention module(DSwin transformer) is proposed. By sparse cross-window attention, it enhances the contextual awareness of multi-scale features extracted by Swin transformer, and keeps high computational efficiency simultaneously. The proposed method generates the query reference points and query embeddings based on the joint prediction probabilities, and selects an appropriate number of decoding feature maps and sparse sampling points for query decoding, which further enhance the inference capability and processing speed. On the benchmark safety-helmet-wearing-dataset(SHWD), the proposed method achieves the average detection accuracy mAP@0.5 of 95.4% with 133.35G floating-point operations per second(FLOPs) and 20 frames per second(FPS), the state-of-the-art method for safety helmet detection.

关键词： Head Accuracy Network architecture Benchmark testing Transformers Feature extraction Safety Decoding Computational efficiency Detection algorithms

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Resilient multi-objective mission planning for UAV formation:A unified framework integrating task pre-and re-assignment

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Defence technology(防务技术) 2025年第3期45卷 203-226页

作者： Xinwei Wang Xiaohua Gao Lei Wang Xichao Su Junhong Jin Xuanbo Liu Zhilong Deng Department of Engineering Mechanics State Key Laboratory of Structural AnalysisOptimization and CAE Software for Industrial EquipmentDalian University of TechnologyDalianLiaoning 116024China School of Mathematics and Big Data Anhui University of Science and TechnologyHuainanAnhui 232001China School of Mathematical Science Dalian University of TechnologyDalianLiaoning 116024China Department of Airborne Vehicle Engineering Naval Aeronautical and Astronautical UniversityYantai 264001China

Combat effectiveness of unmanned aerial vehicle(UAV)formations can be severely affected by the mission execution *** the practical execution phase,there are inevitable risks where UAVs being destroyed or targets failed to be *** improve the mission reliability,a resilient mission planning framework integrates task pre-and re-assignment modules is developed in this *** the task pre-assignment phase,to guarantee the mission reliability,probability constraints regarding the minimum mission success rate are imposed to establish a multi-objective optimization *** an improved genetic algorithm with the multi-population mechanism and specifically designed evolutionary operators is used for efficient *** in the task-reassignment phase,possible trigger events are first analyzed.A real-time contract net protocol-based algorithm is then proposed to address the corresponding emergency *** the dual objective used in the former phase is adapted into a single objective to keep a consistent combat *** cases of different scales demonstrate that the two modules cooperate well with each *** the one hand,the pre-assignment module can generate high-reliability mission schedules as an elaborate mathematical model is *** the other hand,the re-assignment module can efficiently respond to various emergencies and adjust the original schedule within a *** corresponding animation is accessible at ***/video/BV12t421w7EE for better illustration.

关键词： Cooperative mission planning UAV formation Mission reliability Evolutionary algorithm Contract net protocol

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CBIR: a novel identification approach for college students in need based on consumer behavior psychology theory

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Neural Computing and Applications 2025年第6期37卷 4663-4677页

作者： Liu, Xinze Liu, Shixi Hu, Xiaojing Zhang, Yudong Fang, Xianwen School of Computer and Information Engineering Chuzhou University Chuzhou239000 China School of Computer Science and Engineering Southeast University Nanjing210096 China School of Mathematics and Big Data Anhui University of Science and Technology Huainan232001 China

The accurate identification of students in need is crucial for governments and colleges to allocate resources more effectively and enhance social equity and educational fairness. Existing approaches to identifying students in need rely on manual operations that include manually extracting consumption behavior information, statistical consumption characteristics and principal component analysis. However, this issue may lead to low prediction accuracy and inefficiency in identifying students in need. We design a three-stage framework to accurately identify college students in need from the perspective of consumer behavior psychology. The consumption behavior information is first obtained from the student consumption records using the consumption behavior clustering approach. The consumption behavior matrix is then built by extracting consumption and spatiotemporal information in different periods. Finally, a novel consumption behavior identification ResNeSt (CBIR) model is proposed to identify college students in need accurately. The experimental results on real datasets show that the CBIR model has higher prediction accuracy than the baseline models. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Social psychology

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Study on Classification Models Utilizing the Multilayer Perceptron Feature Selection and Ensemble Voting Classifier 24

Study on Classification Models Utilizing the Multilayer Perc...

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5th International Conference on Computer Science and Management technology, ICCSMT 2024

作者： Zhu, Xiaohang Fang, Huan Zhao, Xinli School of Mathematics and Big Data Anhui University of Science and Technology Anhui Huainan China School of Petroleum and Natural Gas Engineering Changzhou University Jiangsu Changzhou China

ISBN: (纸本)9798400709999

The existing classification prediction models increase in training time and cost with the increase in the number of features, significantly increase in computational complexity, and irrelevant or redundant features interfere with the model's learning process and affect the accuracy of classification prediction *** this study, a model combining the multilayer perceptron (MLP) feature selection with an ensemble voting classifier (MRS) is *** MLP feature selection method demonstrates superiority in screening important features, which can effectively extract key information from the original ***, the classification prediction performance of the model is further improved by feeding the filtered features into an ensemble voting classifier that incorporates Random Forest (RF) and Support Vector Machine (SVM).The model enhances the processing efficiency of classification problems in the field of data mining, especially in the context of confronting multi-featured and high-dimensional data, and more accurately performs classification ***, the accuracy of the model exceeds that of many other models is proved by the experiments, demonstrating its great potential in solving the classification and prediction problems in the field of data mining. © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Random forests

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