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A Characteristics-Based Least Common Multiple Algorithm to Optimize Magnetic-Field-Based Indoor Localization

IEEE INTERNET OF THINGS JOURNAL 2025年第8期12卷 10463-10478页

作者： Rafique, Hamaad Patti, Davide Palesi, Maurizio La Delfa, Gaetano Carmelo Univ Catania Dept Elect Elect & Comp Engn I-95125 Catania Italy

clustering is an unsupervised learning technique that groups data based on similarity criteria. Traditional methods like K-Means and agglomerative clustering often require predefined parameters, struggle with irregular cluster shapes, and fail to classify subcluster points in magnetic fingerprint-based indoor localization. This study proposes the characteristics-based least common multiple (LCM) algorithm to address these challenges. This novel approach autonomously determines cluster number and shape while accurately classifying misclassified points based on characteristic similarities using LCM. We evaluated the proposed technique using state-of-the-art metrics and tested it in magnetic-field-based indoor localization scenarios. Comparisons were made with real-time and benchmark datasets, alongside traditional clustering methods. Results demonstrate that LCM significantly enhances localization accuracy, achieving a mean absolute error rate of 0.1 m.

关键词： Location awareness clustering algorithms Shape Fingerprint recognition Internet of Things Accuracy Wireless fidelity Real-time systems clustering methods Time series analysis Automatic clustering data clustering indoor localization least common multiple (LCM) machine learning novel clustering technique

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Collaborative clustering with Heterogeneous algorithms

Collaborative Clustering with Heterogeneous Algorithms

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International Joint Conference on Neural Networks (IJCNN)

作者： Sublime, Jeremie Grozavu, Nistor Bennani, Younes Cornuejols, Antoine AgroParisTech UMR INRA MIA 518 16 Rue Claude Bernard F-75231 Paris 5 France Univ Paris 13 Sorbonne Paris Cite UPN UMR CNRS 7030 F-93430 Villetaneuse France

ISBN: (纸本)9781479919598

The aim of collaborative clustering is to reveal the common underlying structures found by different algorithms while analyzing data. The fundamental concept of collaboration is that the clustering algorithms operate locally but collaborate by exchanging information about the local structures found by each algorithm. In this framework, the one purpose of this article is to introduce a new method which allows to reinforce the clustering process by exchanging information between several results acquired by different clustering algorithms. The originality of our proposed approach is that the collaboration step can use clustering results obtained from any type of algorithm during the local phase. This article gives the theoretical foundations of our approach as well as some experimental results. The proposed approach has been validated on several data sets and the results have shown to be very competitive.

关键词： clustering algorithms clustering process information exchange algorithms Dataset Cluster Analysis

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Research on Enterprise Digital Economy Management System Based on clustering Algorithm 24

Research on Enterprise Digital Economy Management System Bas...

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4th International Conference on Internet of Things and Machine Learning

作者： Liu, Lin Taishan Univ Sch Management & Econ Tai An Shandong Peoples R China

ISBN: (纸本)9798400710353

With the rapid advancement of the global digital economy, enterprise management systems are undergoing profound transformations. Traditional management models no longer suffice to meet modern enterprises' demands for efficiency and intelligence;consequently, data-driven management tools have increasingly become a focal point of research. clustering algorithms, as a core technology in data analysis, enhance the precision and efficiency of enterprise management through data segmentation and analysis. This study investigates the application of clustering algorithms in enterprise digital economy management systems, particularly focusing on the refinement of the K-means algorithm and its parallel processing implementation. By optimizing initial clustering centers and incorporating Lagrange multipliers and attribute weighting strategies, this paper proposes a more adaptive enterprise management solution, applied practically in modules such as employee management, financial management, and supplier management. Experimental results demonstrate that the improved algorithm significantly enhances both clustering effectiveness and computational efficiency, providing robust theoretical and technical support for digital enterprise management.

关键词： clustering algorithms digital economy enterprise management systems

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Modeling and Verification of MRSCAN Based on MapReduce Framework

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IEEE TRANSACTIONS ON RELIABILITY 2025年

作者： Zuo, Zhengkang Ke, Yuhan Hu, Ying Huang, Qing Zeng, Zhicheng Wang, Changjing Jiangxi Normal Univ Sch Comp & Informat Engn Nanchang 330022 Peoples R China Jiangxi Normal Univ Jiangxi Prov Key Lab High Performance Comp Nanchang 330022 Peoples R China Jiangxi Normal Univ State Int Sci & Technol Cooperat Base Networked Su Nanchang 330022 Peoples R China Jiangxi Normal Univ Language Intelligence Res Ctr Nanchang 330022 Peoples R China East China Univ Technol Sch Software Fuzhou 344099 Peoples R China

Network clustering (graph clustering) plays a crucial role in discovering the inherent structures within networks. MapReduce-based structural clustering algorithm for networks (MRSCANs) designed based on MapReduce's parallel computing model, efficiently handles large-scale data. However, MRSCAN can only be tested through experiments, and its correctness cannot be guaranteed. To address this issue, this article has achieved the first implementation of functional MRSCAN modeling and subjected it to rigorous mechanized verification in Isabelle. First, based on Google's MapReduce model type definition and higher-order generic functions, a general MapReduce-based algorithm functional modeling framework is constructed across the fundamental global phases of Map, Shuffle, and Reduce. Moreover, diverse strategies are devised during the Shuffle phases according to user requirements, enhancing the applicability and generality of the MapReduce functional modeling framework. Second, formalizing the definition of MRSCAN, is delineated into four key steps: similarity calculation, core calculation, dimension expansion, and structural clustering. Furthermore, the MapReduce functional modeling framework is applied to these four steps to achieve the functional modeling of MRSCAN, which improves efficiency compared to other structural clustering algorithm for networks (SCANs) algorithms. Lastly, a verification framework for MapReduce-based algorithms is proposed at both the global and shuffle stages. Based on this framework, the correctness and reliability of MRSCAN are ensured. The model framework and verification framework of MapReduce-based algorithms proposed in this article can not only address functional modeling and verification of MRSCAN but also provide a reference for a series of other MapReduce-based functional program designs and proofs.

关键词： clustering algorithms Computational modeling Reliability Programming Scalability Internet Software algorithms Loss measurement Logic Complexity theory Function algorithm MapReduce-based structural clustering algorithm for networks (MRSCANs) modeling framework proof in Isabelle verification framework

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Fuzzy Min-Cut With Soft Balancing Effects

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IEEE TRANSACTIONS ON FUZZY SYSTEMS 2025年第2期33卷 767-778页

作者： Chen, Huimin Zhang, Runxin Wang, Rong Nie, Feiping Northwestern Polytech Univ Sch Comp Sci Sch Artificial Intelligence Opt & Elect iOPEN Xian 710072 Peoples R China Northwestern Polytech Univ Key Lab Intelligent Interact & Applicat Minist Ind & Informat Technol Xian 710072 Peoples R China Northwestern Polytech Univ Sch Artificial Intelligence OPt & Elect iOPEN Xian 710072 Peoples R China

The clustering algorithm has always been a hot spot in machine learning, which has made great progress and been widely used in different scenarios. Due to the characteristics and requirements of some application scenarios, the branch of the balanced clustering algorithm has been developed. The ideal of these algorithms is to obtain clusters containing approximately the same number of samples. However, when there are data points distributed at the boundary of different clusters, resulting in different probabilities of their belonging, hard-partitioned balanced clustering may not be able to handle these boundary data well, thus limiting their performance. Motivated by this, we propose a Fuzzy Min-Cut with Soft Balancing Effects (FCBE) method in this article. Specifically, the FCBE model utilizes fuzzy constraints to simultaneously enhance the ability of the balanced algorithm to capture boundary data members and the advantage of directly obtaining the partitioning results of graph-cut problem without postprocessing. In addition, a sparse regularization is introduced to avoid trivial solutions and maintain the separability of the relationship matrix. Furthermore, the proposed FCBE method can be viewed as a flexibly adjustable generalization pattern that not only has clear interpretability but also can become special cases with clear physical meanings under different parameter values. The feasibility of FCBE has been verified on real datasets.

关键词： clustering algorithms Partitioning algorithms Fuzzy systems Vectors Sparse matrices Switches Data models clustering methods Wireless sensor networks Uncertainty Fuzzy constraints soft-balanced sparse regularization spectral clustering (SC)

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Enhancing SR-Tree for Nearest Neighbor Search

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IEEE ACCESS 2025年 13卷 63324-63333页

作者： Abduaziz, Kayumov Lee, Shincheol Shin, Ji Sun Sejong Univ Dept Comp & Informat Secur & Convergence Engn Inte Seoul 05006 South Korea Sejong Univ Dept Comp & Informat Secur Seoul 05006 South Korea

Nearest neighbor search, also known as k-nearest neighbors (kNN), has become one of the essential backbones in machine learning and data mining tasks, particularly in multidimensional dynamic and evolving data environments. While dynamic index structures have been extensively utilized for indexing dynamic data, their performance in nearest neighbor search can further be improved. This paper aims to enhance SR-Tree as it has been shown to be effective for indexing high dimensional clustered data. We propose enhancements to SR-Tree structure to better handle multidimensional data and reduce query processing time. First, we improve SR-Tree using a variance-based bulk-loading algorithm to build an efficient tree structure. Second, we apply the approximation of the smallest enclosing ball and leaf-level pruning methods to improve performance of kNN queries. Third, we adapt the dual-tree algorithm for SR-Tree that outperforms sequential queries for the all-nearest neighbors problem in low-to-medium dimensional large datasets. These improvements were evaluated using real-world datasets across varying dimensionalities by comparing against several freely available indexing structure frameworks. Experimental results indicated considerable speedup over the state-of-the-art dynamic index implementation in nearest neighbor retrieval across high-dimensional conditions. This work also provides an open-source implementation of our enhancements to SR-Tree for the Rust ecosystem.

关键词： Nearest neighbor methods Indexes Heuristic algorithms Indexing clustering algorithms Approximation algorithms Information security High dimensional data Computer security Sun Nearest-neighbor search kNN similarity indexing

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Only Pick Once: algorithms for Efficiently Picking an Exact Number of Multiple Identical Objects

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IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING 2025年 22卷 14723-14737页

作者： Ye, Zihe Frumento, Ricardo Sun, Yu Rutgers State Univ Dept Comp Sci New Brunswick NJ 08901 USA Univ South Florida USF Bellini Coll Artificial Intelligence Cybersecur & Robot Percept & Act Lab RPAL Tampa FL 33620 USA Univ South Florida USF Ctr Innovat Technol & Aging CITA Tampa FL 33620 USA

Picking up multiple objects at once is a grasping skill that makes a human worker efficient in many domains. This work tackles the problem of getting a requested number of identical objects in a shallow bin by only pick once (OPO) using a simple parallel gripper. The proposed system contains several graph-based algorithms that convert the layout of objects into a graph, cluster vertices in the graph, rank and select candidate clusters based on their topology. Our algorithm also has a multi-object picking predictor based on a convolutional neural network for estimating how many objects would be picked up with a given gripper location and orientation. This paper presents four evaluation metrics and three protocols to evaluate the proposed system. The results show that our proposed system has very high success rates for two and three objects when only picking once. Utilizing our approach can significantly outperform single object picking two to three times in terms of efficiency. The results also show our algorithm can be applied to one unseen shape (hexagon) and unseen sizes cube and cylinder during training without fine-tuning to achieve decent accuracy. Note to Practitioners-This paper is motivated by the current bottleneck in robotics picking which restricts the mass deployment of robots in tasks that are similar to batch-picking in logistics. The state of the art (SOTA) picking algorithm is fast but only focuses on picking one object at a time, which inspires us to look into picking multiple objects at once to increase the efficiency. We design a custom-sized parallel-jaw gripper based on the object sizes' need, and we train a deep-learning model for each designed gripper to predict the number of objects that a grasp pose can retrieve. Our simulation and real-life evaluations have very good results in picking 2 to 3 objects at once in a random setting. Our future work will consider more complex scenarios and rearrangements that can break some hard settings.

关键词： Robots Grippers Grasping Shape clustering algorithms Training Solid modeling Prediction algorithms Pneumatic systems Layout Intelligent robots intelligent manufacturing systems grasping

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Deep Learning-Based Feature Extraction Technique for Single Document Summarization Using Hybrid Optimization Technique

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IEEE ACCESS 2025年 13卷 24515-24529页

作者： Rautaray, Jyotirmayee Panigrahi, Sangram Nayak, Ajit Kumar Sahu, Premananda Mishra, Kaushik Siksha O Anusandhan Univ Inst Tech Educ & Res Dept Comp Sci & Engn Bhubaneswar Odisha India Siksha O Anusandhan Univ Inst Tech Educ & Res Dept Comp Sci & Informat Technol Bhubaneswar Odisha India Lovely Profess Univ Sch Comp Sci & Engn Phagwara Punjab India Manipal Acad Higher Educ Manipal Inst Technol Bengaluru Dept Comp Sci & Engn Manipal India

Presently, the exponential growth of unstructured data on the web and social networks has made it increasingly challenging for individuals to retrieve relevant information efficiently. Over the years, various text summarization techniques have been developed to address this issue. However, traditional approaches that rely on directly extracting words often lead to redundancies and fail to establish a strong connection between the summary and the original document. This paper presents a novel Deep Learning (DL)-based text summarization approach incorporating the following phases: pre-processing, feature extraction, vectorization, and summarization using a hybrid Cat Swarm Optimization (CSO) and Harris Hawk Optimization (HHO) algorithm. Initially, input documents undergo pre-processing steps, including sentence segmentation, word tokenization, stop word removal, and lemmatization, to enhance text quality. Features are then extracted using a Restricted Boltzmann Machine (RBM) to obtain nine key attributes. Vectorization is performed using Term Frequency-Inverse Document Frequency (TF-IDF) to represent sentences in vector form. The hybrid CSO-HHO algorithm is subsequently applied to generate summaries. The proposed method's efficiency was evaluated using datasets from the Document Understanding Conference (DUC), specifically DUC-2002, DUC-2003, and DUC-2005. Metrics such as sensitivity, readability, coherence, precision, BLEU score, ROUGE score, and F-score were analyzed to assess performance. The proposed approach's results were compared with existing methods, including CSO, QABC, PSO, GJO, FF, and machine learning techniques like SVM and RF. The hybrid CSO-HHO algorithm achieved an accuracy of 99.56%, demonstrating its superiority in text summarization tasks.

关键词： Optimization Feature extraction Text summarization Particle swarm optimization Genetic algorithms clustering algorithms Support vector machines Deep learning Data mining Computer science single document summarization pre-processing feature extraction vectorization hybrid CSO-HHO algorithm

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Deep-Reinforcement-Learning-Based Continuous Workflows Scheduling in Heterogeneous Environments

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IEEE INTERNET OF THINGS JOURNAL 2025年第10期12卷 14036-14050页

作者： Wang, Zhi Zhan, Wenhan Duan, Hancong Min, Geyong Huang, Hualong Univ Elect Sci & Technol China Sch Comp Sci & Engn Chengdu 611731 Peoples R China Univ Exeter Coll Engn Math & Phys Sci Dept Comp Sci Exeter EX4 4QF England

Workflow scheduling plays a critical role in optimizing completion time and throughput in distributed cloud environments, leveraging the parallelism of heterogeneous computing resources. However, existing workflow scheduling algorithms often fall short due to heuristic limitations and the challenges in adaptability within heterogeneous settings, leading to suboptimal scheduling solutions. In this article, we present a novel deep reinforcement learning (DRL) framework tailored for continuous workflow scheduling in heterogeneous environments. First, we propose an intelligent scheduler that updates the policy network through interactions with a multitenant environment, triggered by scheduling events. Next, the framework incorporates a graph attention network (GAT) and a self-attention multilayer perceptron (MLP) to preprocess the workflow topology and embed dynamic features of ready tasks and available processors into the state input at each scheduling step. Additionally, a k-dimensional tree-based k-nearest neighbors (kNN) algorithm is employed to map the output action vector to a pair of executed ready task and processor, facilitating the transition from continuous to discrete action spaces and addressing challenges associated with dynamic action spaces. Experimental results demonstrate that our method converges effectively in continuous workflow scheduling scenarios and significantly outperforms the best-known methods in terms of average makespan and load balancing efficiency.

关键词： Processor scheduling Program processors Scheduling Heuristic algorithms Costs clustering algorithms Scheduling algorithms Internet of Things Dynamic scheduling Computational efficiency Cloud computing continuous workflows deep reinforcement learning (DRL) graph attention network (GAT) workflow scheduling

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Reinforcement Learning-Based Edge Server Placement in the Intelligent Internet of Vehicles Environment

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2025年

作者： Zhou, Zhou Abawajy, Jemal Changsha Univ Sch Comp Sci & Engn Changsha 410022 Peoples R China Deakin Univ Sch Informat Technol Geelong Vic 3217 Australia

Generative AI-enabled Intelligent Transportation Systems (ITS) are revolutionizing modern transportation by improving safety, efficiency, and adaptability, fostering the development of intelligent Internet of Vehicles (IoV) ecosystems. A critical component of such systems is the deployment of edge servers, which provide localized, low-latency computing, storage, and processing capabilities near data sources. However, determining the optimal placement of edge servers to maximize safety, efficiency, and adaptability in IoV systems remains a significant challenge. Current solutions often optimize one or two metrics, such as energy efficiency or latency, but fail to account for holistic performance improvements. This paper proposes an edge server placement algorithm, ISC-QL, which combines Improved Spectral clustering (ISC) and Q-Learning to address this gap. In the first stage, the ISC algorithm clusters base stations based on their geographic distribution and the number of connected users, identifying preliminary deployment locations for edge servers. In the second stage, a Q-Learning approach is employed to dynamically refine these initial placements, ultimately determining the optimal locations for edge servers. Simulation experiments using real-world data from Shanghai Telecom validate the effectiveness of the ISC-QL algorithm. Compared to benchmark approaches, ISC-QL demonstrates significant performance improvements, including a 50% increase in load balancing, a 22% reduction in average energy consumption, and a 16% decrease in average delay.

关键词： Servers Delays Optimization Energy consumption Base stations Load management Costs Heuristic algorithms clustering algorithms Systems architecture Generative AI intelligent transportation systems (ITS) intelligent Internet of Vehicles (intelligent IoV) reinforcement learning edge server placement

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