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Granular-ball fuzzy information-based outlier detector

International Journal of Approximate Reasoning 2025年 185卷

作者： Li, Qilin Yuan, Zhong Peng, Dezhong Song, Xiaomin Zheng, Huiming Su, Xinyu Marketing Service Center of State Grid Sichuan Electric Power Corporation Chengdu610045 China College of Computer Science Sichuan University Chengdu610065 China Sichuan National Innovation New Vision UHD Video Technology Co. Ltd Chengdu610095 China Tianfu Jincheng Laboratory Chengdu610093 China National Key Laboratory of Fundamental Algorithms and Models for Engineering Numerical Simulation Chengdu610207 China

Outlier detection is an important part of the process of carrying out data mining and analysis and has been applied to many fields. Existing methods are typically anchored in a single-sample processing paradigm, where the processing unit is each individual and single-granularity sample. This processing paradigm is inefficient and ignores the multi-granularity features inherent in data. In addition, these methods often overlook the uncertainty information present in the data. To remedy the above-mentioned shortcomings, we propose an unsupervised outlier detection method based on Granular-Ball Fuzzy Granules (GBFG). GBFG adopts a granular-ball-based computing paradigm, where the fundamental processing units are granular-balls. This shift from individual samples to granular-balls enables GBFG to capture the overall data structure from a multi-granularity perspective and improve the performance of outlier detection. Subsequently, we calculate the outlier factor based on the outlier degrees of the granular-ball fuzzy granules to which the sample belongs, serving as a measure of the outlier degrees of samples. The experimental results prove that GBFG has a remarkable performance compared with the existing excellent algorithms. The code of GBFG is publicly available on https://***/Mxeron/GBFG. © 2025 Elsevier Inc.

关键词： Granular computing

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LODUS: A Multi-Level Framework for Simulating Environment and Population - A Contagion Experiment on a Pandemic World

arXiv

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arXiv 2022年

作者： Silva, Gabriel Fonseca Cassol, Vinícius Neto, Amyr Borges Fortes Antonitsch, Andre Schaffer, Diogo Musse, Soraia Raupp de Marsillac Linn, Rodrigo Virtual Humans Simulation Laboratory Graduate Course in Computer Science School of Technology Pontifical Catholic University of Rio Grande do Sul Brazil Planning and Management Porto Alegre City Hall - RS Brazil

Nowadays we are experiencing a way of life that never existed before. The pandemic has sharply changed our habits, customs, and behavior. In addition, a lot of work was suddenly requested for city managers challenging them to develop strategies to try stopping the pandemic progression. Urban environments must be dynamic and managers need fast decisions when working on crisis situations. In this paper we present LODUS, a framework able to simulate urban environments on a multi-level approach, combining macro and micro simulation information in order to provide accurate information about population dynamics. Furthermore, the framework LODUS is a powerful tool when performing an urban viability study, since the simulation results are able to highlight and predict attention points prior to an urban environment to be built. © 2022, CC BY.

关键词： Managers

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A Distributed Approximation Algorithm for the Total Dominating Set Problem 1

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18th International Conference on Algorithmic Aspects in Information and Management, AAIM 2024

作者： Wang, Limin Zhang, Zhao Du, Donglei Mao, Yaping Zhang, Xiaoyan School of Mathematical Sciences Henan Institute of Science and Technology Xinxiang453003 China College of Mathematics and Computer Sciences Zhejiang Normal University Zhejiang321004 China Faculty of Management University of New Brunswick Fredericton E3B 5A3 New Brunswick Canada School of Mathematics and Statistics Qinghai Normal University Qinghai810008 China Academy of Plateau Science and Sustainability Xining Qinghai810008 China School of Mathematical Science and Institute of Mathematics Key Laboratory of Ministry of Education Numerical Simulation of Large Scale Complex Systems Nanjing Normal University Jiangsu210023 China

ISBN: (数字)9789819777983

ISBN: (纸本)9789819777976

Given a network graph G=(V,E), a subset T⊆V is said to be a total dominating set (TDS) if every v∈V is adjacent to at least one node in T. In this paper, we present a distributed algorithm for the TDS problem via the LP relaxation techniques. For a positive integer k and maximum degree Δ, the proposed algorithm outputs a fractional total dominating set of expected size O(kΔ2k)|TDSOPT|. The distributed algorithm runs in O(k2) communication rounds, and the algorithm use messages of size O(logΔ). The fractional solution is then rounded to obtain an integer total dominating set for the original problem. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Consensus algorithm

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Association of PM_(2.5)Reduction with Improved Kidney Function:A Nationwide Quasiexperiment among Chinese Adults

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Health Data Science 2022年第1期2022卷 115-123页

作者： Yiqun Han Tao Xue Frank J.Kelly Yixuan Zheng Yao Yao Jiajianghui Li Jiwei Li Chun Fan Pengfei Li Tong Zhu Environmental Research Group MRC Centre for Environment and HealthImperial College LondonLondonUK BIC-ESAT and SKL-ESPC College of Environmental Sciences and EngineeringCenter for Environment and HealthPeking UniversityBeijing 100871China Institute of Reproductive and Child Health/Ministry of Health Key Laboratory of Reproductive Health and Department of Epidemiology and Biostatistics School of Public HealthPeking UniversityBeijingChina Center of Air Quality Simulation and System Analysis Chinese Academy of Environmental Planning100012 BeijingChina China Center for Health Development Studies Peking UniversityBeijingChina College of Computer Science and Technology Zhejiang UniversityHangzhouChina Computer Center Peking University and Peng Cheng LaboratoryChina Advanced Institute of Information Technology Peking UniversityChina

*** evidence from human studies has revealed the adverse impact of ambient fine particles(PM_(2.5))on health outcomes related to metabolic disorders and distant *** exposure to ambient PM_(2.5)leads to kidney impairment remains *** rapid air quality improvement driven by the clean air actions in China since 2013 provides an opportunity for a quasiexperiment to investigate the beneficial effect of PM_(2.5)reduction on kidney *** on two repeated nationwide surveys of the same population of 5115 adults in 2011 and 2015,we conducted a difference-indifference *** in long-term exposure to ambient PM_(2.5)were associated with changes in kidney function biomarkers,including estimated glomerular filtration rate by serum creatinine(GFRscr)or cystatin C(GFRcys),blood urea nitrogen(BUN),and uric acid(UA).*** a 10μg/m^(3)reduction in PM_(2.5),a significant improvement was observed for multiple kidney functional biomarkers,including GFRscr,BUN and UA,with a change of 0.42(95%confidence interval[CI]:0.06,0.78)mL/min/1.73m^(2),-0.38(-0.64,-0.12)mg/dL,and-0.06(-0.12,-0.00)mg/dL,respectively.A lower socioeconomic status,indicated by rural residence or low educational level,enhanced the adverse effect of PM_(2.5)on kidney *** results support a significant nephrotoxicity of PM_(2.5)based on multiple serum biomarkers and indicate a beneficial effect of improved air quality on kidney function.

关键词： Kidney organs ambient

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Texture Analysis of Breast US Images Using Morphological Transforms, Hausdorff Dimension and Bagging Ensemble Method

Texture Analysis of Breast US Images Using Morphological Tra...

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Mediterranean Conference on Control and Automation (MED)

作者： Gigi Tăbăcaru Simona Moldovanu Marian Barbu Department of Automatic Control and Electrical Engineering Faculty of Automation Computers Electrical Engineering and Electronics “Dunarea de Jos” University of Galati Romania Computer Science and Information Technology Faculty of Automation Computers Electrical Engineering and Electronics “Dunarea de Jos” University of Galati The Modelling & Simulation Laboratory Dunarea de Jos University of Galati Galati Romania

ISBN: (数字)9798350395440

ISBN: (纸本)9798350395457

In this work, an attempt is made for the first time to use the measurement pattern generated by morphological transformation quantified by Hausdorff fractal dimension (HFD) and classified with ensemble learning based on bagging. The proposed work uses three morphological transformations for image preprocessing: hit-and-miss transform (HMT), white (WHT), and black top-hat (BHT). The pattern texture of US breast images is described by extracting the HFD from the regions of interest (ROI) after the ultrasound (US) images have been preprocessed. The main objective of this study was achieved by comparatively analyzing the classification performance of features using the Random Forest (RF), Extra Trees (ET) classifier, and bagging ensemble method based on XGBoot classifier. In presented study, the XGBoost classifier and BHT image processing method give an accuracy of 89.8% in a binary classification, benign versus malignant breast cancer.

关键词： Ultrasonic imaging Transforms Feature extraction Time measurement Fractals Ensemble learning Image preprocessing

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FedAG:A Federated Learning Method Based on Data Importance Weighted Aggregation

FedAG:A Federated Learning Method Based on Data Importance W...

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IEEE International Conference on Communications in China (ICCC)

作者： Mengchu Xu Yan Zeng Meiting Xue Ji-Lin Zhang Jian Wan Mingyao Zhou Yilin Wen Yukun Shi School of Computer Science and Technology Hangzhou Dianzi University Hangzhou China Key Laboratory of Complex Systems Modeling and Simulation Ministry of Education Hangzhou China Zhejiang Engineering Research Center of Data Security Governance Hangzhou China School of Cyberspace Hangzhou Dianzi University Hangzhou China Huawei Enterprise Communication Technology Co Hangzhou China

Federated learning is a distributed machine learning approach that trains models with multiple clients and data locally. However, the existing methods ignore the differences between local models caused by the data heterogeneity, and give a high aggregation weight for the low-quality local model, which leads to low accuracy of the global model. For this problem, we propose a federated learning method based on data importance weighted aggregation, FedAG. It constructs an evaluation model of data importance based on the data distribution skewness, data volume, and data quality of clients, and aggregates local models with different weights computed by the evaluation model of data importance into a global model. We make experiments on five datasets including EMNIST, MNIST, and CIFAR-10. The results show that the accuracy of FedAG is respectively improved by 6.5%, 1.8%, 1.3% and 2.1%, compared with FedAvg, FedProx, FedVar, and Scaffold algorithms.

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A Multi-objective optimization model for simulation-based acquisition 7

A Multi-objective optimization model for simulation-based ac...

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7th IEEE International Conference on Progress in Informatics and Computing, PIC 2020

作者： Zhang, Xue Xue, Junjie Zhou, Junhua Shi, Guoqiang Yu, Yan Nanjing University of Science and Technology School of Computer Science and Engineering Nanjing China Beijing Simulation Center Beijing Complex Product Advanced Manufacturing Engineering Research Center Beijing China State Key Laboratory of Intelligent Manufacturing System Technology Beijing Institute of Electronic System Engineering Beijing China Science and Technology on Space System Simulation Laboratory Beijing Simulation Center Beijing China

ISBN: (纸本)9781728170862

Large-scale equipment presents the characteristics of technology-intensive, knowledge-intensive and complex, which brings high investment and high risk to equipment acquisition. This paper analyzes the various components of equipment acquisition to fully apply modeling and simulation technology to each stage of it. On the basis of multi-objective optimization theory, the objective function of the lowest risk and lowest cost of simulation-based acquisition (SBA) is proposed. Then establishing a multi-objective optimization model for simulation-based acquisition according to the corresponding constraints. The nondominated sorting genetic algorithm II (NSGA II) and multi-objective particle swarm optimization (MOPSO) algorithms are used to optimize the model, and the majority voting method is used for integration. The comparison of simulation examples proves that the multi-objective optimization model oriented to SBA designed in this paper can effectively realize the optimization of each objective of SBA under cost constraints. And the feasibility and efficiency of optimization solution are guaranteed. © 2020 IEEE.

关键词： Multiobjective optimization

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Enhancing Digital Twins with Advances in simulation and Artificial Intelligence: Opportunities and Challenges 23

Enhancing Digital Twins with Advances in Simulation and Arti...

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Proceedings of the Winter simulation Conference

作者： Simon J. E. Taylor Charles M. Macal Andrea Matta Markus Rabe Susan M. Sanchez Guodong Shao Modelling & Simulation Group Department of Computer Science Brunel University London Uxbridge UK Decision & Infrastructure Sciences Division Argonne National Laboratory Lemont USA Department of Mechanical Engineering Politecnico di Milano Milano Italy Department IT in Production and Logistics TU Dortmund University Dortmund Germany Operations Research Department Naval Postgraduate School Monterey USA Engineering Laboratory National Institute of Standards and Technology Gaithersburg USA

ISBN: (纸本)9798350369663

simulations are used to investigate physical systems. A digital twin goes beyond this by connecting a simulation with the physical system with the purpose of analyzing and controlling that system in real-time. In the past 5 years there has been a substantial increase in research into simulation and Artificial Intelligence (AI). The combination of simulation with AI presents many possible innovations. Similarly, combining AI with simulation presents further possibilities including approaches to developing trustworthy and explainable AI methods, solutions to problems arising from sparce or no data and better methods for time series analysis. Given the progress that has been made in Digital Twins and simulation and AI, what opportunities are there from combining these two exciting research areas? What challenges need to be overcome to achieve these? This article discusses these from the perspectives of six leading members of the Modeling & simulation community.

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Applications of MXenes in human-like sensors and actuators

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Nano Research 2023年第4期16卷 5767-5795页

作者： Jinbo Pang Songang Peng Chongyang Hou Xiao Wang Ting Wang Yu Cao Weijia Zhou Ding Sun Kai Wang Mark H.Rümmeli Gianaurelio Cuniberti Hong Liu Institute for Advanced Interdisciplinary Research(iAIR) Collaborative Innovation Center of Technology and Equipment for Biological Diagnosis and Therapy in Universities of ShandongUniversity of JinanJinan 250022China Institute for Materials Science and Max Bergmann Center of Biomaterials Technische Universität DresdenDresden 01069Germany Center for Advancing Electronics Dresden Technische Universität DresdenDresden 01069Germany Dresden Center for Computational Materials Science Technische Universität DresdenDresden 01062Germany Dresden Center for Intelligent Materials(GCL DCIM) Technische Universität DresdenDresden 01062Germany High-Frequency High-Voltage Device and Integrated Circuits R&D Center Institute of MicroelectronicsChinese Academy of SciencesBeijing 100029China Key Laboratory of Microelectronic Devices&Integrated Technology Institute of MicroelectronicsChinese Academy of SciencesBeijing 100029China Shenzhen Key Laboratory of Nanobiomechanics Shenzhen Institute of Advanced TechnologyChinese Academy of SciencesShenzhen 518055China State Key Laboratory of Biobased Material and Green Papermaking Qilu University of TechnologyShandong Academy of SciencesJinan 250353China School of Bioengineering Qilu University of TechnologyShandong Academy of ScienceJinan 250353China Key Laboratory of Modern Power System Simulation and Control&Renewable Energy Technology(Ministry of Education) Northeast Electric Power UniversityJilin 132012China School of Electrical Engineering Northeast Electric Power UniversityJilin 132012China School of Electrical Engineering Weihai Innovation Research InstituteQingdao UniversityQingdao 266000China School of Electrical and Computer Engineering Jilin Jianzhu UniversityChangchun 130118China Institute for Complex Materials Leibniz Institute for Solid State and Materials Research Dresden(IFW Dresden)20 Helmholtz StrasseDresden 01069Germany College of Energy Soochow Institute for Energy and Materials Innovations Soochow UniversitySuzhou 215006China Key L

Human beings perceive the world through the senses of sight,hearing,smell,taste,touch,space,and *** first five senses are prerequisites for people to *** sensing organs upload information to the nervous systems,including the brain,for interpreting the surrounding ***,the brain sends commands to muscles reflexively to react to stimuli,including light,gas,chemicals,sound,and ***,as an emerging two-dimensional material,has been intensively adopted in the applications of various sensors and *** this review,we update the sensors to mimic five primary senses and actuators for stimulating muscles,which employ MXene-based film,membrane,and composite with other functional ***,a brief introduction is delivered for the structure,properties,and synthesis methods of ***,we feed the readers the recent reports on the MXene-derived image sensors as artificial retinas,gas sensors,chemical biosensors,acoustic devices,and tactile sensors for electronic ***,the actuators of MXene-based composite are ***,future opportunities are given to MXene research based on the requirements of artificial intelligence and humanoid robot,which may induce prospects in accompanying healthcare and biomedical engineering applications.

关键词： MXenes sensors actuators gas sensors biosensors tactile sensors sound sensors artificial retina

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A Model Average Algorithm for Housing Price Forecast with Evaluation Interpretation

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Journal of Quantum Computing 2022年第3期4卷 147-163页

作者： Jintao Fu Yong Zhou Qian Qiu Guangwei Xu Neng Wan School of Computer Science Southwest Petroleum UniversityChengdu610500China Key Laboratory on Aero-Engine Altitude Simulation Technology Sichuan Gas Turbine EstablishmentAECCMianyang621000China Aero Engine Academy of China Aero Engine Corporation of ChinaBeijing101300China

In the field of computer research,the increase of data in result of societal progress has been remarkable,and the management of this data and the analysis of linked businesses have grown in *** are numerous practical uses for the capability to extract key characteristics from secondary property data and utilize these characteristics to forecast home *** regression methods in machine learning to segment the data set,examine the major factors affecting it,and forecast home prices is the most popular method for examining pricing *** is challenging to generate precise forecasts since many of the regression models currently being utilized in research are unable to efficiently collect data on the distinctive elements that correlate y with a high degree of house price *** today’s forecasting studies,ensemble learning is a very prevalent and well-liked study *** regression integration computation of large housing datasets can use a lot of computer resources as well as computation time,and ensemble learning uses more resources and calls for more machine support in integrating diverse *** Average Model suggested in this paper uses the concept of fusion to produce integrated analysis findings from several models,combining the best benefits of separate *** Average Model has a strong applicability in the field of regression prediction and significantly increases computational *** technique is also easier to replicate and very effective in regression *** using regression processing techniques,this work creates an average of different regression models using the AM(Average Model)algorithm in a novel *** evaluating essential models with 90%accuracy,this technique significantly increases the accuracy of house price *** experimental results show that the AM algorithm proposed in this paper has lower prediction error than other comparison algorithms,and the prediction accurac

关键词： Machine learning AM algorithm price forecast regression algorithm Model evaluation

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