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Comparison between method of moments and entropy regularization algorithm applied to parameter estimation for mixed-Weibull distribution

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JOURNAL OF APPLIED STATISTICS 2011年第12期38卷 2709-2722页

作者： Hung, Wen-Liang Chang, Yen-Chang Natl Hsinchu Univ Educ Dept Appl Math Hsinchu Taiwan

Mixed-Weibull distribution has been used to model a wide range of failure data sets, and in many practical situations the number of components in a mixture model is unknown. Thus, the parameter estimation of a mixed-Weibull distribution is considered and the important issue of how to determine the number of components is discussed. Two approaches are proposed to solve this problem. One is the method of moments and the other is a regularization type of fuzzy clustering algorithm. Finally, numerical examples and two real data sets are given to illustrate the features of the proposed approaches.

关键词： EM algorithm entropy regularization fuzzy clustering algorithm method of moments mixed-Weibull distribution

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Review of medical image recognition technologies to detect melanomas using neural networks

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BMC BIOINFORMATICS 2020年第Sup11期21卷 270-270页

作者： Efimenko, Mila Ignatev, Alexander Koshechkin, Konstantin Sechenov Univ Digital Hlth Inst Fed State Autonomous Educ Inst Higher Educ IM Sechenov First Moscow State Med UnivMinist Hl Moscow Russia Moscow City Hlth Dept Moscow Sci & Pract Ctr Dermatol Venereol & Cosmet Moscow Russia Minist Hlth Russian Federat Fed State Budgetary Inst Informat Technol Dept Sci Ctr Expert Evaluat Med Prod Moscow Russia

BackgroundMelanoma is one of the most aggressive types of cancer that has become a world-class problem. According to the World Health Organization estimates, 132,000 cases of the disease and 66,000 deaths from malignant melanoma and other forms of skin cancer are reported annually worldwide (https://***/gho/data/?theme=main) and those numbers continue to grow. In our opinion, due to the increasing incidence of the disease, it is necessary to find new, easy to use and sensitive methods for the early diagnosis of melanoma in a large number of people around the world. Over the last decade, neural networks show highly sensitive, specific, and accurate *** study presents a review of PubMed papers including requests << melanoma neural network >> and << melanoma neural network dermatoscopy >>. We review recent researches and discuss their opportunities acceptable in clinical *** searched the PubMed database for systematic reviews and original research papers on the requests << melanoma neural network >> and << melanoma neural network dermatoscopy >> published in English. Only papers that reported results, progress and outcomes are included in this *** found 11 papers that match our requests that observed convolutional and deep-learning neural networks combined with fuzzy clustering or World Cup Optimization algorithms in analyzing dermatoscopic images. All of them require an ABCD (asymmetry, border, color, and differential structures) algorithm and its derivates (in combination with ABCD algorithm or separately). Also, they require a large dataset of dermatoscopic images and optimized estimation parameters to provide high specificity, accuracy and *** to the analyzed papers, neural networks show higher specificity, accuracy and sensitivity than dermatologists. Neural networks are able to evaluate features that might be unavailable to the naked human eye. Despite that, we need more datasets

关键词： Melanoma classification Skin cancer Deep learning neural network Convolutional neural network fuzzy clustering algorithm

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Deep Intrusion Data Mining Method Forship Monitoring Network under Cloud Computing

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JOURNAL OF COASTAL RESEARCH 2018年第Spe83期000卷 668-672页

作者： Yang, Zhiwen Zhou, Haodong Jiangxi Inst Fash Technol Nanchang 330201 Jiangxi Peoples R China Jiangxi Acad Sci Nanchang 330029 Jiangxi Peoples R China

In the cloud computing environment the network space with different type and different scale are completely different in the destructiveness of intrusion and intrusion features. The traditional intrusion mining is mainly to weight these differentiated intrusion results, and calculating a fused threshold, measuring whether it is invaded, but this method does not subdivide intrusion features in ship monitoring network, and has high false alarm rate and rate of missing report. This paper proposed data mining method of the ship monitoring network intrusion mining based on the fuzzy C mean clustering algorithm and improved support vector machine algorithm. Firstly, this paper carried out dimensionality reduction and classification processing for the different type data, and collected related data for the extraction and analysis of sample feature, and calculated the overall threshold of data, and used the improved support vector machine algorithm to normalize the collected data, and obtained the data signal with uniform format. Experimental results show that the improved algorithm in the ship monitoring network under cloud computing deepness intrusion mining can improve the accuracy of mining, reduce the false alarm rate and improve the mining efficiency.

关键词： Ship monitoring network intrusion data mining fuzzy clustering algorithm improved support vector machine algorithm

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An object-extracting approach based on non-negative matrix factorization

An object-extracting approach based on non-negative matrix f...

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IEEE International Conference on Information Reuse and Integration

作者： Kang, DZ Xu, BW Zhang, WX Lu, JJ SE Univ Dept Comp Sci & Engn Nanjing 210096 Peoples R China

ISBN: (纸本)0780388194

Previous object-extracting approaches, which are based on sub-graph merging and partition, need to compute excessive modules tightness and modules overlap. An object-extracting approach based on non-negative matrix factorization is presented. First, in order to design effective similarity measure between subprogram vectors, non-negative matrix factorization is applied to dimensionality reduction of the usage matrix. Secondly, fuzzy clustering algorithm is used to partition subprogram set and type set respectively, and several modules are generated accordingly. Last, objects are extracted by computing the modules cohesion. This object-extracting approach cannot only reduce the time for computing the modules cohesion by reducing the number of generated modules, but also extract several object sets from legacy systems for the user.

关键词： reverse engineering object extracting non-negative matrix factorization fuzzy clustering algorithm cohesion

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An improved method for accurate computation of coastal upwelling index using Sea Surface Temperature Images 9

An improved method for accurate computation of coastal upwel...

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9th International Symposium on Signal, Image, Video and Communications (ISIVC)

作者： El Aouni, Anass Minaoui, Khalid Tamim, Ayoub Daoudi, Khalid Yahia, Hussein Mohammed V Agdal Univ LRIT Associated Unit CNRST URAC 29 Rabat Morocco INRIA Bordeaux Sud Ouest GEOSTAT Team Talence France

ISBN: (纸本)9781538681732

In this paper we aim to present a new methodology to derive rigorous SST-based coastal upwelling index for the purpose of conducting a saisonal variablity of upwelling area along the Moroccan Atlantic coast. The method is based on the scientific knowledge of upwelling area and its spatial distribution provided by expert oceanographers. The latter consists in automatically identify and extract the region covered by the upwelling waters in the costal ocean of Morocco using the fuzzy c-means algorithm and finding regions of homogeneous pixels. Then Region Growing process is used to filter out the remaining noisy structures in the offshort waters. The methodology is used to provide a satistical view of the spatial and temporal variability of the Moroccan upwelling activity. The relevance of the proposed Coastal Upwelling Index (CUI) is evaluated by an oceanographer using 86 8-days sea surface temperature images and it is shown to be superior to that of the standard upwelling index.

关键词： Sea surface temperature Coastal Upwelling Index fuzzy clustering algorithm unsupervised classification

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Detection of Moroccan coastal upwelling using sea surface chlorophyll concentration 12

Detection of Moroccan coastal upwelling using sea surface ch...

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12th IEEE/ACS International Conference of Computer Systems and Applications (AICCSA)

作者： El Aouni, Anass Minaoui, Khalid Tamim, Ayoub Daoudi, Khalid Yahia, Hussein Atillah, Abderrahman Aboutajdine, Driss Mohammed V Agdal Univ LRIT Associated Unit CNRST URAC 29 Rabat Morocco INRIA Bordeaux Sud Ouest GEOSTAT Team Talence France Royal Ctr Remote Sensing CRTS Rabat Morocco

ISBN: (纸本)9781509004782

The aim of this work is to automatically identify and extract the upwelling area in the coastal ocean of Morocco using the satellite observation of chlorophyll concentration. The algorithm starts by the application of FCM algorithm for the purpose of finding regions of homogeneous concentration of the chlorophyll, resulting in c-partitioned labeled images. A regiongrowing algorithm is then used to filter out the noisy structures in the offshore waters not belonging to the upwelling regions. The proposed methodology has been validated by an oceanographer and tested over a database of 166 weekly Sea Surface chlorophyll data. The region of interst cover the southern part of Moroccan atlantic coast spanning from the years 2007 to 2012.

关键词： Upwelling Satellite chlorophyll concentration fuzzy clustering algorithm Region-growing

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A Supervised clustering algorithm Base on Alternative distance for Using multimedia software to Identify Alzheimer's disease

A Supervised Clustering Algorithm Base on Alternative distan...

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IEEE Eurasia Conference on IOT, Communication and Engineering (IEEE ECICE)

作者： Fen, Huang Shu Lin, Yuan-Horng Yih, Jeng-Ming Chihlee Univ Technol Dept Multimedia Design 1116Sec 2Zhongshan E Rd New Taipei 73658 Taiwan Natl Taichung Univ Educ Dept Math Educ 140 Minsheng Rd Taichung 73658 Taiwan Min Hwei Jr Coll Hlth Care Management 1116Sec 2Zhongshan E Rd Tainan 73658 Taiwan

ISBN: (纸本)9781728125015

fuzzy algorithms of GG and GK based on Mahalanobis distance can improve those limitations of roller bearing rchitectural classifications, but Gath and Geva clustering algorithm could only use the data with multi-dependent variable normal distribution. GK-algorithm is limited as it must know the distribution of data. The data set was set by Min-Hwei College of Health Care Management in Taiwan and is a complete test on medical staff and skill of medical care. Hence we can collect the student data in the classes where they have good abilities. This discussion is about supervised classification method constructing the fuzzy clustering by means of putting forward method. Our discussion about an updated essential value not diverging calculation method to promote the prevention of Gath-Geva or Gustafson-Kessel methods, removed the limit of the determinants of common variance of matrices in the Gustafson-Kessel methods, and alternative for the common variance matrix with the statistics of related matrix which exists in the main function. The experimental results of real data sets of using multimedia software to identify Alzheimer's disease and then to prevent aging prove that the supervised classification method constructing the fuzzy clustering by means of method obtain much better result, when data dispersion between different clusters is overlapping or the shape of clusters is not roller bearing.

关键词： fuzzy clustering algorithm Gustafson & Kessel algorithm Multimedia Software

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Workflow Task Scheduling algorithm Based on IFCM and IACO 4th

Workflow Task Scheduling Algorithm Based on IFCM and IACO

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4th International Conference on Cloud Computing and Security (ICCCS)

作者： Liu, Qin Ma, Tinghuai Li, Jian Shen, Wenhai Nanjing Univ Informat Sci Technol Sch Comp Software Nanjing 210044 Jiangsu Peoples R China Nanjing Univ Informat Sci Technol Jiangsu Engn Ctr Network Monitoring CICAEET Nanjing 210044 Jiangsu Peoples R China Natl Meteorol Informat Ctr Beijing 100080 Peoples R China

ISBN: (纸本)9783030000097;9783030000080

To solve the scheduling problem of workflow tasks in cloud computing, this paper combined the improved fuzzy c-means clustering algorithm (IFCM) and the improved ant colony optimization algorithm (IACO) and proposed a new workflow task scheduling algorithm. Firstly, the proposed algorithm used the IFCM to classify resources. Then, tasks will be sorted by their priority. Based on the results of resource clustering and the distance between resources and expect of tasks, tasks will be assigned to the appropriate resources and the scheduling will be initialized. After that, the workflow tasks will be encoded based on the initial scheduling. At last, ant colony optimization algorithm will be improved by the cross and mutation operation in genetic algorithm and used to search optimal schedules. The experiments showed that the proposed algorithm could quickly and efficiently find appropriate scheduling scheme, effectively reduce the time span of workflow tasks and increase the utilization of resources.

关键词： Cloud computing Workflow task scheduling fuzzy clustering algorithm Ant colony optimization algorithm

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Neuro-fuzzy Compliance Control for Rehabilitation Robotics

Neuro-fuzzy Compliance Control for Rehabilitation Robotics

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3rd IEEE RAS and EMBS International Conference on Biomedical Robotics and Biomechatronics

作者： Martin, Peter Emami, M. R. Univ Toronto Inst Aerosp Studies Space Mechatron Grp Toronto ON M3H 5T6 Canada

ISBN: (纸本)9781424477098

This paper presents a methodology for the design of a real-time neuro-fuzzy controller for the robotic rehabilitation of patients with upper-limb dysfunction due to neurological diseases. The approach utilizes a fuzzy-logic schema to introduce compliance into the human-robot interaction, and to allow the emulation of a wide variety of therapy techniques. It also allows for the fine-tuning of system dynamics using linguistic variables. The rule base for the system is trained using a fuzzy clustering algorithm and applied to experimental data gathered during traditional therapy sessions. The compliance rule base is combined with a hybrid neuro-fuzzy compensator to automatically tune the dynamics of the system on-line. The control algorithm is implemented as a platform-independent solution to facilitate the rehabilitation of patients using multiple manipulator configurations. Preliminary experimentation has shown promising results indicating that the proposed methodology can accurately replicate the desired compliance profiles in real-time.

关键词： diseases fuzzy clustering algorithm fuzzy logic fuzzy-logic schema handicapped aids human-robot interaction human-robot interaction hybrid neuro-fuzzy compensator linguistic variables manipulator configurations medical robotics neuro-fuzzy compliance control neurological diseases neurophysiology patient rehabilitation patient treatment platform-independent solution prosthetics real-time neuro-fuzzy controller rehabilitation robotics therapy techniques upper-limb dysfunction

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An efficient psFCM clustering algorithm

An efficient <i>ps</i>FCM Clustering Algorithm

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IEEE International Advance Computing Conference

作者： Begum, S. A. Purkayastha, B. S. Chakrabarty, A. Som, T. Assam Univ Dept Comp Sci Silchar Assam India

ISBN: (纸本)9781424429271

In this paper an efficient method for data clustering is proposed. The proposed algorithm is a modified psFCM, called the pshFCM clustering algorithm that finds better cluster centers for a given data sets as compared to the cluster centers obtained by the psFCM. The computational performance of the proposed pshFCM algorithm is comparable with the psFCM and the. FCM.

关键词： Data mining fuzzy sets fuzzy clustering algorithm

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