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A Novel Method for News Recommendation on Websites Using the Clustered-Vectors Optimization Algorithm

IEEE ACCESS 2025年 13卷 6685-6703页

作者： Chaipornkaew, Piyanuch Banditwattanawong, Thepparit Kasetsart Univ Fac Sci Dept Comp Sci Bangkok 10900 Thailand

Recommendation systems have been extensively implemented across various sectors, including e-commerce, social media, and banking. Numerous studies have employed established machine learning techniques to enhance the performance of recommendation models to a certain degree. This research introduces a recommendation model for news content utilizing a novel machine learning technique known as the "Clustered-Vectors Optimization (CVO)" algorithm. The proposed algorithm aims to optimize the clustering of news titles to improve the performance of news recommendations. Once the news titles are effectively clustered, a recommendation list is generated for website visitors based on their experiences. The recommendation list for each visitor is selected from news titles within the same cluster that previous visitors have accessed. This study utilized two sources of news datasets. The primary dataset, collected from a back-end website, was provided by a private digital television company in Thailand, while additional datasets were sourced from publicly available data on Kaggle. Experimental results indicated that the proposed CVO algorithm outperformed five well-known algorithms, which were TF-IDF, Word2Vec, Doc2Vec, Bag-of-Words (BoW), and Transformer, in terms of predictive performance. For instance, on the Thai news dataset, the CVO algorithm achieved an accuracy of 97.56%, a precision of 94.59%, a recall of 97.36%, and an F1-score of 97.46%. Similarly, the English language dataset, such as Indian news, also demonstrated high performance, with the CVO algorithm achieving an accuracy of 99.90%, a precision of 99.41%, a recall of 99.62%, and an F1-score of 99.53%.

关键词： clustering algorithms Vectors Recommender systems Accuracy Prediction algorithms Machine learning algorithms Feature extraction Text categorization Support vector machines Deep learning clustering news titles optimization recommendation system website

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Deep Reinforcement Learning-Based Multi-AUV Task Allocation Algorithm in Underwater Wireless Sensor Networks

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IEEE SENSORS JOURNAL 2025年第2期25卷 3909-3922页

作者： Liu, Zhibin Liu, Chunfeng Qu, Wenyu Qiu, Tie Zhao, Zhao Hu, Yansheng Dong, Huiyong Tianjin Univ Coll Intelligence & Comp Tianjin 300350 Peoples R China

Autonomous underwater vehicle (AUV)-based data collection can bring significant advantages to underwater wireless sensor networks (UWSNs). Collaborative collection based on multi-AUV task allocation is an effective way to reduce delay. However, the existing research work seldom considers the real current environment in the task allocation, which leads to the large delay and yaw of AUVs. The introduction of ocean currents makes the existing task allocation algorithms no longer applicable due to the poor solving ability and long convergence time. Therefore, we propose an efficient task allocation algorithm named genetic algorithm N-step reinforcement learning improved DQN (GA-NDQN) by combining the genetic algorithm (GA) and N-step reinforcement learning (RL) nature of DQN to minimize the data collection delay. In our work, to minimize the impact of ocean currents on the AUV's travel, the specific trajectory optimization problem between adjacent nodes is considered and modeled as a minimum weight sum problem (MWSP). To complete the entire data collection process, we performed path planning for AUVs and modeled it as an asymmetric traveling salesman problem (ATSP). A* algorithm and the Lin-Kernighan-Helsgaun (LKH) algorithm are designed to solve these problems, which are further nested in GA-NDQN to optimize the task allocation strategy for data collection. Finally, the effectiveness of the proposed scheme is verified by extensive simulation results.

关键词： Sensors Resource management Data collection Delays Wireless sensor networks Sea surface Heuristic algorithms clustering algorithms Reliability Energy consumption Data collection deep reinforcement learning (DRL) multiautonomous underwater vehicle (AUV) task allocation underwater wireless sensor networks (UWSNs)

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A Joint Adaptive Neuro-Fuzzy Inference System and Binary Quantum-Based Avian Navigation Algorithm for Optimal Resource Monitoring, Task Scheduling and Migration in Cloud-based System

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IEEE ACCESS 2025年 13卷 43109-43126页

作者： Priyadarshini, Aliva Pradhan, Sateesh Kumar Mishra, Kaushik Utkal Univ PG Dept Comp Sci Applicat Bhubaneswar 751004 India Manipal Acad Higher Educ Manipal Inst Technol Bengaluru Dept Comp Sci & Engn Manipal 560064 India

A vast amount of data is generated daily from all aspects of individual lives due to the increasing proliferation of devices that are connected to the internet. These internet-connected gadgets lack the necessary capacity, resources, and storage to effectively process and retain large volumes of data with accuracy and reliability. Therefore, it is vital to regulate and process the unpredictable data created by a computing paradigm with robust resource specifications. Cloud computing has been considered an appealing solution for effectively analyzing and storing this data within a specific timeframe. Furthermore, the existence of multiple conflicting factors and the classification of the problem as NP-hard make resource management and task consolidation major obstacles in Cloud-based systems. This research suggests a hybrid Resource Monitoring, Task Scheduling and Migration technique that combines an Adaptive Neuro-Fuzzy Inference System (ANFIS) with a Binary Quantum-based Avian Navigation Optimizer technique (BQANA) to tackle these challenges. The BQANA algorithm is utilized to enhance the control parameters of the ANFIS system. Additionally, a load balancing technique is proposed to provide an even distribution of workloads among Cloud Virtual Machines (VMs) to optimize resource management. Furthermore, a task migration strategy has also been adopted to offload the overloaded tasks to under-utilized VMs. The proposed approach presented in this study has been thoroughly validated using extensive simulations on real-world benchmark datasets, specifically for Quality of Service (QoS) characteristics. The simulation results demonstrate that the proposed methodology outperforms previous methods with regard to makespan, resource utilization, response time, energy consumption, and load balancing, with respective enhancements of 24.7%, 15.4%, 16.9%, 4.51%, and 23.4%.

关键词： Cloud computing Resource management Metaheuristics Heuristic algorithms Quality of service Load modeling Load management Time factors Dynamic scheduling clustering algorithms Adaptive neuro-fuzzy inference system cloud computing quantum-based avian navigation optimizer resource management task migration task scheduling

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Time-Varying Formation H∞ Tracking Control and Optimization for Delayed Multi-Agent Systems With Exogenous Disturbances

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IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING 2025年 22卷 5637-5647页

作者： Peng, Xiao-Jie He, Yong Liu, Zhouzhou You, Le Li, Hongyi Southwest Univ Coll Elect & Informat Engn Chongqing 400715 Peoples R China Southwest Univ Chongqing Key Lab Gener Technol & Syst Serv Robots Chongqing 400715 Peoples R China China Univ Geosci Sch Automat Wuhan 430074 Peoples R China Hubei Key Lab Adv Control & Intelligent Automat Co Wuhan 430074 Peoples R China Minist Educ Engn Res Ctr Intelligent Technol Geoexplorat Wuhan 430074 Peoples R China

This paper presents a novel time-varying formation (TVF) H-infinity tracking controller for multi-agent systems (MASs) under exogenous disturbances and time-varying delays. In order to address the issue that most of the existing formation controllers are designed under the predetermined small time delay, a formation controller which can tolerate a large delay upper bound is designed. This is achieved by employing the Lyapunov functional method. At the same time, in order to suppress the influence of exogenous disturbances on the formation process, the robustness of the formation controller is improved through optimizing the H-infinity performance index. To derive the gain matrix of the formation protocol, a mere two linear matrix inequalities (LMIs) need to be resolved. On this basis, differential evolution (DE) algorithm is employed to further adjust and optimize the parameters of the formation controller. Finally, it is proved that the obtained results can be directly applied to resolving the leader-follower consensus problem. Two simulation experiments are carried out to verify the superiority and effectiveness of the proposed scheme, where four followers achieve the H-infinity formation tracking following the leader. Note to Practitioners-The motivation of this article is to address the TVF H-infinity tracking control problem for MAS under limited network communication resources. The TVF model is extensively applied in the flocking control domain, such as robot formation control, spacecrafts formation flight and so on. In the engineering application scenarios, the communication between agents usually has the following problems: 1) The information interaction among the agents is often influenced by exogenous disturbances and time delays;2) The existing formation tracking controllers are only suitable for handling predetermined small time delay. Thus, this paper presents a TVF H-infinity tracking controller with excellent robustness to deal with these problems, an

关键词： Delays Delay effects Optimization Linear matrix inequalities Formation control Symmetric matrices clustering algorithms Formation tracking control time delay multi-agent system exogenous disturbance Lyapunov functional

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A Proactive Caching Approach to Boost Network Offloading in Urban Areas

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IEEE ACCESS 2025年 13卷 63841-63856页

作者： Hassan, Ahmed Metwally, Khaled Mekkawy, Tamer Mahran, Ashraf Mil Tech Coll Commun Dept Cairo 11331 Egypt Mil Tech Coll Comp Engn & Artificial Intelligent Dept Cairo 11331 Egypt Mil Tech Coll Avion Dept Cairo 11331 Egypt

The rapid growth of 6G networks and the Internet of Things (IoT) has increased the need for advanced and adaptive data management. In response, our research brings together multiple devices, including smartphones, bicycles, and in-vehicle systems, under a single user preference model to propose a novel proactive caching framework that surpasses traditional single-device approaches that give each device a unique set of preferences. Exploiting the advantages of both Device-to-Device (D2D) and Vehicle-to-Vehicle (V2V) communications, our proposed approach minimizes core network congestion and increases spectrum efficiency. At its core is the Cognitive File Caching Algorithm (CFCA), a proactive, scalable, and adaptive approach designed to address complex challenges involving varying file popularity in cities and user movement patterns. Our proposed CFCA improves caching efficiency by clustering users based on shared zones of interest and behavioral data, achieving a 27% improvement in network offloading and a 24% increase in cache hit ratio over existing benchmarks. Furthermore, the study explores the economic implications of proactive caching, establishing a strategic balance that ensures long-term profitability while minimizing high-cost caching inefficiencies. This study marks a paradigm advancement in proactive caching, setting the groundwork for future-ready 6G networks by combining user-centric data models, scalable algorithms, and adaptive file exchange mechanisms to address ever-increasing traffic needs.

关键词： Device-to-device communication User preference 6G mobile communication Resource management Internet of Things Backhaul networks Costs clustering algorithms Throughput Smart phones Device-to-device (D2D) vehicle-to-vehicle (V2V) network offloading net profit margin proactive caching

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Dynamic Multi-Objective Operation Optimization of Municipal Solid Waste Incineration Process Based on Transfer Learning

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IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING 2025年 22卷 9338-9352页

作者： Qiao, Junfei Cui, Yingying Meng, Xi Beijing Univ Technol Sch Informat Sci & Technol Beijing 100124 Peoples R China Minist Educ Engn Res Ctr Intelligent Percept & Autonomous Cont Beijing 100124 Peoples R China

To achieve optimal performance of municipal solid waste incineration (MSWI) process with nonstationary time-varying dynamics, a dynamic multi-objective operation optimization method (DSE-TrMOPSO), based on transfer learning, is proposed in this paper. First, the operation optimization models are established using data stream ensemble learning, where incremental updating and selective ensemble strategies are adopted to cope with changing working conditions. Second, a dynamic multi-objective particle swarm optimization algorithm based on transfer learning (CTrDMOPSO) is designed for optimization calculation. In this algorithm, the hierarchical clustering-based transfer learning strategy is proposed to construct the initial population with high quality. Afterwards, the knee point-based decision making is performed to determine the final setpoints of manipulated variables for index optimization. Then, the feasibility of the designed algorithm CTrDMOPSO is verified on the benchmark problems. Finally, the proposed DSE-TrMOPSO is applied to the MSWI process. The results demonstrate that the proposed method can achieve satisfactory operation performance in terms of combustion efficiency and nitrogen oxides emissions. Note to Practitioners-This study aims to develop an optimal operation method for the MSWI process with nonstationary time-varying dynamics. To achieve this goal, an operation optimization method based on transfer learning is proposed, which includes two key points, the operation optimization modeling and the dynamic multi-objective optimization. In practice, practitioners can implement the proposed method utilizing real-time data streams. The optimization objective models are constructed by data stream ensemble learning, and the time-varying dynamics can be captured with incremental updating and selective ensemble strategies. After that, the optimal setpoints of manipulated variables are derived by the designed dynamic multi-objective particle swarm optimizatio

关键词： Optimization Heuristic algorithms Transfer learning Adaptation models Particle swarm optimization Combustion Waste materials Incineration Employee welfare clustering algorithms Municipal solid waste incineration dynamic multi-objective operation optimization data stream ensemble learning transfer learning particle swarm optimization

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A Fuzzy Identification Method Based on the Likelihood Function and Noise clustering Algorithm

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International Journal of Fuzzy Systems 2023年第1期25卷 136-144页

作者： Tsai, Shun-Hung Chen, Yi-Ting Department of Electrical Engineering National Sun Yat-sen University Lien-hai Rd. Kaohsiung804 Taiwan Graduate Institute of Automation Technology National Taipei University of Technology Zhongxiao E. Rd. Taipei106 Taiwan

In this paper, based on the modified fuzzy c-regression model and noise clustering algorithm, a fuzzy identification method is proposed. Firstly, by considering the relations for the real model, the established model, and the outliers, a modified objective function with noise is proposed to alleviate the affection of noise. Additionally, the consequent parameters of the fuzzy model can be obtained by the iterative formula which obtained by the Lagrangian formula. Furthermore, a modified membership function, which is involved the likelihood function, is propounded to get a more suitable multivariate normal distribution for the data points. Lastly, two examples are illustrated to show the validity and effectiveness of the proposed results. © 2022, The Author(s) under exclusive licence to Taiwan Fuzzy Systems Association.

关键词： clustering algorithms

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Improved K-Means Algorithm for Nearby Target Localization

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IEEE ACCESS 2025年 13卷 14872-14880页

作者： Yuan, Zongwen Wang, Xingdi Chen, Fuyang Ma, Xicheng Chaohu Univ Sch Elect Engn Hefei 238024 Peoples R China Nanjing Univ Aeronaut & Astronaut Coll Automat Engn Nanjing 210016 Peoples R China Beijing Measurement & Control Technol Co Ltd Beijing 471000 Peoples R China

In a multi-source localization system, direction of arrival (DOA) estimation of angles always suffers from errors due to noise interference, sensor position inaccuracies, and other factors. When the distance between target sources is much smaller than the distance between sensors and target sources, the accuracy of traditional localization algorithms based on direction finding and cross-fixing deteriorates. In this paper, we propose a localization algorithm based on K-means clustering. To tackle the problem of unknown initial positions of target sources, we employ a grid density peak clustering(DPC) method for initial localization. In the K-means algorithm, we integrate a quartile range anomaly detection algorithm to address interference signal issues. Finally, we propose an invalid compensation algorithm to filter out invalid signals, thereby compensating for the estimation errors in angles. Through the collection of real-world data, we compare the performance of the traditional direction finding and cross-fixing algorithms with the proposed algorithm in the localization of nearby target points. Experimental results demonstrate that the proposed algorithm significantly improves localization accuracy.

关键词： clustering algorithms Interference Location awareness Autonomous aerial vehicles Accuracy Prediction algorithms Direction-of-arrival estimation Target tracking Trajectory Direction of arrival passive location K-means clustering

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Hyperspectral and Multispectral Image Fusion for Remotely Sensed Target Detection: A New Cloud-Edge Collaborative Approach

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IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2025年 63卷

作者： Wu, Zebin Liu, Chenxin Sun, Jin Hu, Jun Wei, Zhihui Plaza, Javier Plaza, Antonio Nanjing Univ Sci & Technol Sch Comp Sci & Engn Nanjing 210094 Jiangsu Peoples R China Univ Extremadura Dept Technol Comp & Commun Hyperspectral Comp Lab Caceres 10071 Spain

Hyperspectral target detection (HTD) can provide detailed information about the objects and materials within a scene and holds significant importance in remote sensing image analysis. Traditional HTD methods often suffer from low detection accuracy when applied to hyperspectral images (HSIs) with low spatial resolution, where the target only occupies a few pixels. This can be addressed by exploiting the higher spatial resolution of multispectral images (MSIs). In addition, many cloud-based HTD methods, which rely on the distributed processing capability of cloud computing to cope with large-scale datasets, may result in long transmission delays that cannot meet real-time requirements. This article suggests a cloud-edge collaborative HTD approach based on the fusion of remotely sensed HSIs and MSIs. We first introduce an HTD algorithm that employs low-rank matrix decomposition and hierarchical constraint energy minimization (hCEM) to fuse a low-resolution HSI (LR-HIS) and a high-resolution MSI (HR-MSI). Aiming at a continuous shooting scenario, we further present a cloud-edge implementation of the HTD algorithm through the collaboration of a cloud cluster and edge servers deployed close to data acquisition devices. The overall processing flow of remotely sensed data fusion in the cloud-edge environment is formulated as a flowshop scheduling-like optimization problem. We develop a co-optimization scheduling algorithm to explore the best resource allocation solutions to the formulated problem. Experimental results on both general-purpose and real-world datasets show that the newly proposed HTD algorithm leads to significant improvements in detection accuracy over traditional methods, and the cloud-edge collaborative approach further enhances computational efficiency.

关键词： Cloud computing Matrix decomposition Collaboration Spatial resolution Data integration Servers Detectors Object detection Hyperspectral imaging clustering algorithms Cloud-edge collaborative computing hyperspectral target detection (HTD) multiobjective optimization

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Bridge Deformation Prediction Using KCC-LSTM With InSAR Time Series Data

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IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 2025年 18卷 9582-9592页

作者： Bai, Zechao Shen, Chang Wang, Yanping Lin, Yun Li, Yang Shen, Wenjie North China Univ Technol Sch Informat Sci & Technol Beijing 100144 Peoples R China

As a crucial component of the transportation infrastructure, the health of bridge plays a direct role in the traffic safety. Over time, gradual structural deformation can compromise a bridge's stability and safety. Therefore, accurately predicting bridge deformation is essential for analyzing its causes and detecting potential safety hazards in a timely manner. Satellite-based synthetic aperture radar interferometry (InSAR) technology, which detects deformation at millimeter-scale precision over large areas, offers significant advantages in monitoring bridge deformation. However, most existing time-series deformation prediction methods based on InSAR data primarily focus on land subsidence. Given that bridge is complex, singular structures with unique spatial-temporal characteristics, existing methods designed for land subsidence are not directly applicable to bridge deformation prediction. To address this challenge, we propose a novel K-shape and complete linkage hierarchical cluster long short-term memory (KCC-LSTM) approach for predicting bridge deformation based on time-series InSAR data. The approach initially combines two machine learning based clustering algorithms, K-Shape for better capturing shape features of time series and complete linkage hierarchical clustering combined with spatial geographic location captures the spatial characteristics of time series to derive clusters with unique spatiotemporal deformation behavior, improving clustering accuracy and spatiotemporal correlation. clustering results generated from this unsupervised machine learning approach are later used as training labels to develop long short-term memory (LSTM) networks. We validate the proposed approach using time-series data from 100 X-band TerraSAR-X images, acquired from 13 April 2010 to 13 December 2019. Our results demonstrate that compared to standard LSTM, the proposed approach reduces root mean square error of Bridge 1 from 3.6 to 0.5 mm and Bridge 2 from 3.6 to 1.3 mm, imp

关键词： Deformation Bridges Time series analysis Long short term memory Predictive models Deformable models Spatiotemporal phenomena Monitoring clustering algorithms Accuracy Bridge deformation prediction, long short-term memory (LSTM) spatiotemporal clustering time-series synthetic aperture radar interferometry (InSAR)

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