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IEEE International conference on Innovative Mechanisms for Industry Applications (IcIMIA

作者： Vikas, A. Sreenivasan, A. Sudha, K. L. DSCE Bangalore 78 Karnataka India Dept ECE Bangalore Karnataka India

ISBN: (纸本)9781509059607

This paper proposes the fuzzy c-means and criminisi algorithm Based Shadow removal scheme for the Side Scan Sonar Images. Side Scan Sonar is widely used in the underwater ocean investigations like mining, pipelining, object detection, underwater communications etc. This paper make use of fuzzy c-means clustering algorithm for shadow Region segmentation and criminisi algorithm for filling the shadow region. Thus one can get clear view of detected object.

关键词： fuzzy c-means algorithm criminisi algorithm Side Scan Sonar Ocean investigations

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clustering algorithm Based on fuzzy c-means and Artificial Fish Swarm

Clustering Algorithm Based on Fuzzy C-means and Artificial F...

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International Workshop on Information and Electronics Engineering (IWIEE) / International conference on Information, computing and Telecommunications (IcIcT)

作者： Zhu, Weiling Jiang, Jingqing Song, chuyi Bao, Lanying Inner Mongolia Univ Nationalities Coll Math Tongliao 028000 Peoples R China

clustering algorithm has applied in many fields such as data mining, statistics and machine learning. But the clustering number and the initial clustering center affect the accuracy of clustering. In this paper, the average information entropy and density function are used to determine the clustering number and the initial clustering center respectively based on fuzzy c-means clustering algorithm. And then the new bionic optimization algorithm---artificial fish swarm is applied to cluster. Artificial fish swarm algorithm is simple and easy to implement. The experimental results show the efficiency of the proposed clustering algorithm. (c) 2011 Published by Elsevier Ltd. Selection and/or peer-review under responsibility of Harbin University of Science and Technology

关键词： Artificial fish swarm algorithm Information entropy fuzzy c-means algorithm

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Multilevel color Image segmentation using Modified Genetic algorithm (MfGA) inspired fuzzy c-means clustering 2

Multilevel Color Image segmentation using Modified Genetic A...

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2nd IEEE International conference on Research in computational Intelligence and communication Networks (IcRcIcN)

作者： Das, Sunanda De, Sourav Univ Inst Technol Dept Comp Sci & Engn Burdwan W Bengal India Cooch Behar Govt Engn Coll Dept Comp Sci & Engn Cooch Behar W Bengal India

ISBN: (纸本)9781509010479

convergence to local minima point is one of the major disadvantages of conventional fuzzy c-means (FcM). Due to this drawback, segmentation result may hamper for not selecting the cluster centroids properly. To overcome this, a modified genetic (MfGA) algorithm is proposed to improve the performance of FcM. The optimized class levels derived from the MfGA are employed as initial input to FcM for finding global optimal solutions in a large search space. An extensive performance comparison of the proposed MfGA inspired conventional FcM and GA based FcM on two multilevel color images establishes the superiority of the proposed approach.

关键词： Segmentation clustering algorithm fuzzy c-means algorithm Genetic algorithm Image quality evaluation metrics

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An Efficient Genetic algorithm with fuzzy c-means clustering for Traveling Salesman Problem

An Efficient Genetic Algorithm with Fuzzy c-Means Clustering...

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IEEE congress on Evolutionary computation (cEc)

作者： Yoon, Jong-Won cho, Sung-Bae Yonsei Univ Dept Comp Sci Seoul 120749 South Korea

ISBN: (纸本)9781424478354

Genetic algorithms (GA) are one of effective approaches to solve the traveling salesman problem (TSP). When applying GA to the TSP, it is necessary to use a large number of individuals in order to increase the chance of finding optimal solutions. However, this incurs high evaluation costs which make it difficult to obtain fitness values of all the individuals. To overcome this limitation we propose an efficient genetic algorithm based on fuzzy clustering which reduces evaluation costs with minimizing loss of performance. It works by evaluating only one representative individual for each cluster of a given population, and estimating the fitness values of the others from the representatives indirectly. A fuzzy c-means algorithm is used for grouping the individuals and the fitness of each individual is estimated according to membership values. The experiments were conducted with randomly generated cities, and the performance of the method was evaluated by comparing to other GAs. The results showed the usefulness of the proposed method on the TSP.

关键词： genetic algorithm fitness evaluation fuzzy clustering fuzzy c-means algorithm

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An intelligent low power consumption routing protocol to extend the lifetime of wireless sensor networks based on fuzzy c-means plus plus clustering algorithm

An intelligent low power consumption routing protocol to ext...

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5th International Symposium on Intelligent Systems Technologies and Applications (ISTA) / International conference on Applied Soft computing and communication Networks (AcN)

作者： chandrawanshi, Veervrat Singh Tripathi, Rajiv Kumar Pachauri, Rahul Jaypee Univ Engn & Technol Dept Elect & Commun Engn Guna 473226 MP India Natl Inst Technol Dept Elect & Commun Engn Delhi India Jaypee Univ Engn & Technol Dept Comp Sci Engn Guna MP India

The design of a wireless sensor network (WSN) faces many constraints. Mostly, WSN is energy constraint because the sensor nodes are battery operated. Available power expenditure in WSN largely depends on the efficient use of limited resources and appropriate routing of the data packets. The power consumption can be minimized by balancing the energy consumption between the sensor nodes and selecting the minimum power consumption route for the data packets. clustering is one of the most effective technique that not only uniformly distributes the energy among all the sensor nodes but also play a vital role in the designing of routing protocols. So based on these advantages, a low power consumption routing protocol is proposed that makes use of fuzzy c-means++ algorithm. The proposed approach minimizes the power consumption of the sensor network by the excellent management of the WSN and also raises the lifespan. The simulation result illustrates the effectiveness of the proposed routing method when compared with the recently developed protocols based on k-means and fuzzy c-means algorithms.

关键词： Wireless sensor networks clustering cluster head k-means k-means plus fuzzy c-means algorithm fuzzy c-means plus algorithm energy efficient network

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A powerful hybrid clustering method based on modified stem cells and fuzzy c-means algorithms

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ENGINEERING APPLIcATIONS OF ARTIFIcIAL INTELLIGENcE 2013年第5-6期26卷 1493-1502页

作者： Taherdangkoo, Mohammad Bagheri, Mohammad Hadi Taba Med Imaging Ctr Shiraz *** Iran Harvard Univ Sch Med Brigham & Womens Hosp Ctr Evidence Based ImagingDept Radiol Brookline MA USA Shiraz Univ Med Sci Med Imaging Res Ctr Shiraz Iran

One of the simple techniques for Data clustering is based on fuzzy c-means (FcM) clustering which describes the belongingness of each data to a cluster by a fuzzy membership function instead of a crisp value. However, the results of fuzzy clustering depend highly on the initial state selection and there is also a high risk for getting the best results when the datasets are large. In this paper, we present a hybrid algorithm based on FcM and modified stem cells algorithms, we called it Sc-FcM algorithm, for optimum clustering of a dataset into K clusters. The experimental results obtained by using the new algorithm on different well-known datasets compared with those obtained by K-means algorithm, FcM, Genetic algorithm (GA), Particle Swarm Optimization (PSO), Ant colony Optimization (AcO), Artificial Bee colony (ABc) algorithm demonstrate the better performance of the new algorithm. (c) 2013 Elsevier Ltd. All rights reserved.

关键词： Data clustering Stem cells algorithm (ScA) fuzzy c-means algorithm Sc-FcM algorithm

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Intuitionistic fuzzy local information c-means algorithm for image segmentation

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INFORMATION ScIENcES 2024年 681卷

作者： cui, Hanshuai Xie, Zheng Zeng, Wenyi Ma, Rong Zhang, Yinghui Yin, Qian Xu, Zeshui Beijing Normal Univ Sch Artificial Intelligence Beijing 100875 Peoples R China Sichuan Univ Business Sch Chengdu 610064 Peoples R China

Image segmentation allows us to separate an image into distinct, non-overlapping parts by utilizing specific features such as hue, texture, and shape. The technique is prevalent in different domains, including target detection, medical imaging, and pattern recognition owing to its importance in analyzing the image. The fuzzy c-means (FcM) algorithm is a popular method for image segmentation and pattern recognition. However, uncertainty and unknown noise in the data impair the effectiveness of the algorithm. Alternatively, uncertainty in real world can be addressed by the intuitionistic fuzzy set (IFS). This article presents a new approach to image representation using IFS and local information about the image. We introduce the concept of filtering into the intuitionistic fuzzy set and utilize a specially designed exponential distance for IFS. We propose the intuitionistic fuzzy local information c-means (IFLIcM) algorithm. The goal of IFLIcM is to increase the tolerance to noise and the maintain the details in image better than existing FcM variants. We test the performance of our algorithm on a public dataset and compare it with existing FcM methods and Double Deep-Image-Prior (Double-DIP). The experimental results demonstrate that IFLIcM is highly effective in image segmentation and outperforms existing methods.

关键词： clustering fuzzy c-means algorithm Distance measure Image segmentation Intuitionistic fuzzy set

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clustering algorithm Based on fuzzy c-means and Artificial Fish Swarm

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Procedia Engineering 2012年 29卷 3307-3311页

作者： Weiling Zhu Jingqing Jiang chuyi Song Lanying Bao College of Mathematics Inner Mongolia University for NationalitiesTongliao 028000 China College of Computer Science and Technology Inner Mongolia University for NationalitiesTongliao 028000 China

clustering algorithm has applied in many fields such as data mining, statistics and machine learning. But the clustering number and the initial clustering center affect the accuracy of clustering. In this paper, the average information entropy and density function are used to determine the clustering number and the initial clustering center respectively based on fuzzy c-means clustering algorithm. And then the new bionic optimization algorithm----artificial fish swarm is applied to cluster. Artificial fish swarm algorithm is simple and easy to implement. The experimental results show the efficiency of the proposed clustering algorithm.

关键词： Artificial fish swarm algorithm Information entropy fuzzy c-means algorithm

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A hierarchical prediction model for lane-changes based on combination of fuzzy c-means and adaptive neural network

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EXPERT SYSTEMS WITH APPLIcATIONS 2019年第0期130卷 265-275页

作者： Tang, Jinjun Yu, Shaowei Liu, Fang chen, Xinqiang Huang, Helai Cent S Univ Sch Traff & Transportat Engn Smart Transport Key Lab Hunan Prov Changsha 410075 Hunan Peoples R China Changan Univ Minist Educ China Mobile Commun Corp Joint Lab Internet Vehicles Xian 710064 Shaanxi Peoples R China Changsha Univ Sci & Technol Sch Transportat Engn Changsha 410205 Hunan Peoples R China Shanghai Maritime Univ Inst Logist Sci & Engn Shanghai 2013066 Peoples R China

Lane changing behavior generally expresses uncertainty due to the impact of environmental factors, and unreasonable lane changes can cause serious collisions. High precision prediction of lane changing intent is helpful to enhance proactivity in driving safety protection. This study proposed a lane-changing prediction model based on fuzzy c-means clustering algorithm and adaptive Neural Network (FcMNN), which introduced a new prediction process: (1) Unsupervised learning method: categorize original dataset into different clusters according to their distribution features;(2) Supervised learning method: optimize sub Neural Network structures and weighting parameters for each cluster or pattern. Through comparing with several traditional methods under different simulation scenarios, the proposed model effectively improve the prediction performance and stability. The results obtained in this study will be helpful to deeply analyze the intent recognition of driving behavior, improve the safety of lane-changing behavior, and provide key technology in driving prediction of Advanced Driver Assistance System (ADAS). (c) 2019 Elsevier Ltd. All rights reserved.

关键词： Lane changes fuzzy c-means algorithm Neural network Driving simulation Driving prediction

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A fuzzy c-means approach for regionalization using a bivariate homogeneity and discordancy approach

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JOURNAL OF HYDROLOGY 2011年第3-4期401卷 231-239页

作者： Sadri, S. Burn, D. H. Univ Waterloo Dept Civil & Environm Engn Waterloo ON N2L 3G1 Canada

In stochastic analysis for droughts, such as frequency or trend analysis, the absence of lengthy records typically limits the reliability of statistical estimates. To address this issue, "regional" or "pooled" analysis approach is often used. The main contribution of this study is to create regions based on bivariate criteria rather than univariate ones;the two variables are severity and duration. The methodology is applied to hydrological records of 36 unregulated flow monitoring sites in the canadian "prairie" provinces of Alberta, Saskatchewan and Manitoba. Our criteria for a hydrological "region" to be suitable are that it should be homogeneous, that it should not be discordant, and that it should not be too small. Tests for homogeneity and non-discordancy are traditionally based on univariate L-moment statistics;for example there have been several applications of univariate L-moments to bivariate drought analysis by simply ignoring one of the variables. Instead, we use multivariate L-moments, also known as L-comoments. The approach uses site characteristics and a fuzzy clustering approach, called fuzzy c-means (FcM), to form the initial regions (clusters) and adjusts initial clusters based on partial or fuzzy membership of each site to other clusters to form final clusters that meet the criteria of homogeneity, lack of discordancy, and sufficient size. We also estimate return periods using a bivariate copula method. (c) 2011 Elsevier B.V. All rights reserved.

关键词： Drought Regional drought frequency analysis Bivariate homogeneity Bivariate discordancy fuzzy c-means algorithm Regionalization

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