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12th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSIGNALS)

作者： Alah, Roaa Safi Abed Bilgin, Gokhan Albayrak, Abdulkadir Yildiz Tech Univ Dept Comp Engn TR-34220 Istanbul Turkey Yildiz Tech Univ Signal & Image Proc Lab SIMPLAB TR-34220 Istanbul Turkey

ISBN: (纸本)9789897583537

Analysis of cells in histopathological images with conventional manual methods is relatively expensive and time-consuming work for pathologists. Recently, computer aided and facilitated researches for the diagnostic algorithms have obtained a high significance to assist the pathologists to extract cellular structures. In this paper, we are compering the conventional fuzzy c-means (fcm) clustering method with the proposed automated detection system based on Tiny-Convolutional Neural Network (Tiny-CNN) to detect center of nucleus in histopathological images, Also, in this study, we are tried to find center of nucleus by combined unsupervised method (fcm) with supervised method (Tiny-CNN). Briefly, First step, nuclei centers are detected with fcm algorithm which is applied as a clustering-segmentation method to perform segmentation of nucleus cellular and nucleus non-cellular structure to find the correct center of nuclei. Second step, the deep learning method is used to detect center of nucleus based automated method. Afterward, combined each of these individual methods to evaluate our model for extracting the center of nucleus on two different data set the University of California Santa Barbara's UCSB-58 data set and data set University of Warwick's CRC-100 data set.

关键词： Nuclei Detection Histopathological Images fcm algorithm Convolution Neural Networks Deep Learning

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Analysis for Customer Lifetime Value Categorization with RFM Model 5

Analysis for Customer Lifetime Value Categorization with RFM...

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5th Information Systems International Conference (ISICO) on Role of Information Systems in Industry 4.0

作者： Monalisa, Siti Nadya, Putri Novita, Rice UIN Suska Riau HR Soebrantas St Pekanbaru 28293 Indonesia

The same treatment of all customers will cause customers who are not so valuable will be value destroyer rather than a value creator. Giving discounts and promos to all customers has not provided benefits to a company in retaining customers. These two things are currently being experienced by LWC Company so that changes are needed in evaluating the strategy taken to keep the relationship with customers, especially potential customers, by forming customer portfolios. Customers portfolios can be analyzed from customer transactions. With customer transactions data, the company can find out which potential customers and the customers who do give less value for the company. The way to analyze customer transaction data is by clustering customers with Fuzzy C-means algorithm by using RFM model. Forming the number of clusters is validated by PCI model and ranking is done by multiplying AHP weight to find the life value of the customer so that it can be known which customer group gives high value to the company. The result of this research is that the customers of LWC Company categorized as superstar customer, typical customer and dormant customer. Based on the portfolio, LWC Company can carry out strategy in managing their customer according to the type of the portfolio. (C) 2019 The Authors. Published by Elsevier B.V.

关键词： fcm algorithm PCI AHP CLV Categorization

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Utilization of Fuzzy C-means algorithm as a novel predictive tool for estimation of interfacial tension of hydrocarbon and brine

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PETROLEUM SCIENCE AND TECHNOLOGY 2018年第15期36卷 1107-1112页

作者： Rouhibakhsh, Karim Darvish, Houman Shiraz Univ Sch Chem Petr & Gas Engn Dept Petr Engn Shiraz Iran Islamic Azad Univ Marvdasht Branch Dept Petr Engn Marvdasht Iran

The interfacial tension that exists between brine and hydrocarbon is known as one of major properties in petroleum industries because it extremely affects oil trapping in reservoirs and consequently oil recovery. Due to aforementioned reasons the importance of investigation of this parameter has been highlighted. In the present study, Fuzzy C-means (fcm) algorithm was developed to predict interfacial tension between hydrocarbon and brine as function of different parameters such as pressure, temperature, carbon number of hydrocarbon and ionic strength of brine. The obtained results of predicting algorithm expressed its low relative error and deviation from the experimental data which gathered from the literature. Also the coefficients of determination (R-2) for training and testing data were calculated 0.9508 and 0.9309 respectively. This predictive tool is simple and user friend to utilize and can be helpful for petroleum engineers to estimate interfacial tension between hydrocarbons and brine.

关键词： EOR fcm algorithm interfacial tension hydrocarbon predicting model

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An Improved LEACH Routing algorithm for Wireless Sensor Network

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INTERNATIONAL JOURNAL OF WIRELESS INFORMATION NETWORKS 2018年第3期25卷 323-331页

作者： Huang, Wenwei Ling, Yun Zhou, Weilong Hunan Univ Technol Sch Elect & Informat Engn Zhuzhou Peoples R China Key Lab Elect Drive Control & Intelligent Equipme Zhuzhou Peoples R China

Optimization of energy consumption is major concern for the design and planning of wireless sensor networks (WSNs). Recent research has demonstrated that organizing nodes in clusters has higher energy efficiency. LEACH is the most popular routing protocol for cluster-based in WSNs, and fcm algorithm is used for the optimum number of the clusters and their location. Aiming at the shortcomings of LEACH and fcm-LEACH, which including inaccurate cluster centers, unreasonable clustering and sole data transmission mode. This paper proposes a new energy efficient routing algorithm (NF-LEACH). In the new algorithm, There are many factors have considered to prolong the network life cycle that they are the degree of membership, residual energy, base station distance and data transmission mode. Finally, the comparison among LEACH, fcm-LEACH, and NF-LEACH has been done. The results show that the NF-LEACH has the longest lifetime and the most evenly distributed amongst three algorithms.

关键词： LEACH protocol fcm algorithm Wireless sensor network Energy-efficient routing algorithm

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Application of Fuzzy c-means algorithm for the estimation of Asphaltene precipitation

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PETROLEUM SCIENCE AND TECHNOLOGY 2018年第3期36卷 239-243页

作者： Taherpour, Alireza Sefidi, Alireza Cheshmeh Bemani, Amin Hamule, Touba Petr Univ Technol Dept Petr Engn Ahvaz *** Iran Petr Univ Technol Dept Basic Sci Abadan Iran

One of problematic topics in petroleum engineering is Asphaltene precipitation issue which causes problems such as tubing plugging and formation damage due to temperature, pressure and composition changes so the notability of this issue increases. In the present investigation a novel Fuzzy c-means (fcm) algorithm was developed to predict precipitated asphaltene as function of dilution ratio, carbon number of precipitants and temperature for solving the problem. The results showed that this novel approach has great ability to predict precipitated asphaltene in terms of aforementioned parameters. The coefficients of determination (R-2) for training and testing steps are calculated as 0.9828 and 0.9387 respectively. This great degree of accuracy expresses that the predicting algorithm has potential to be utilized as software for prediction of asphaltene behavior.

关键词： asphaltene fcm algorithm precipitation predicting model deposition

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Analysis for Customer Lifetime Value Categorization with RFM Model

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Procedia Computer Science 2019年 161卷 834-840页

作者： Siti Monalisa Putri Nadya Rice Novita UIN Suska Riau HR.Soebrantas street Pekanbaru and 28293 Indonesia

关键词： fcm algorithm PCI AHP CLV Categorization

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An Approach to Fault Diagnosis Using Fuzzy Clustering Techniques 4th

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10th Conference of the European-Society-for-Fuzzy-Logic-and-Technology (EUSFLAT) / 16th International Workshop on Intuitionistic Fuzzy Sets and Generalized Nets (IWIFSGN)

作者： Rodriguez Ramos, Adrian Bernal de Lazaro, Jose Manuel da Silva Neto, Antonio J. Corona, CarlosCruz Luis Verdegay, Jose Llanes-Santiago, Orestes Univ Tecnol La Habana CUJAE Automat & Comp Dept CP 19390 Havana Cuba Univ Estado Rio de Janeiro Inst Politecn Nova Friburgo Brazil Univ Granada DECSAI Granada Spain

ISBN: (纸本)9783319668277;9783319668260

In this paper a novel approach to design data driven based fault diagnosis systems using fuzzy clustering techniques is presented. In the proposal, the data was first pre-processed using the Noise Clustering algorithm. This permits to eliminate outliers and reduce the confusion as a first part of the classification process. Secondly, the Kernel Fuzzy C-means algorithm was used to achieve greater separability among the classes, and reduce the classification errors. Finally, it can be implemented a step for optimizing the parameters of the NC and Kfcm algorithms. The proposed approach was validated using the iris benchmark data sets. The obtained results indicate the feasibility of the proposal.

关键词： Fault diagnosis Fuzzy clustering fcm algorithm NC algorithm Kfcm algorithm Metaheuristics

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QoS-Based Channel and Power Optimization algorithm in D2D System 18

QoS-Based Channel and Power Optimization Algorithm in D2D Sy...

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18th IEEE International Conference on Communication Technology (IEEE ICCT)

作者： Tao, Jing Zhu, Qi Hu, Han Nanjing Univ Posts & Telecommun Jiangsu Key Lab Wireless Commun Nanjing Jiangsu Peoples R China

ISBN: (纸本)9781538676356

This paper considers the scenario of D2D users sharing cellular user uplink resources, and proposes an optimal resource allocation algorithm in order to improve the system capacity. The algorithm first uses fuzzy C-means (fcm) algorithm to cluster according to the geographic location of the D2D user, so that users in the cluster can not share the channel to restrain the interference between D2D pairs, then use the Kuhn-Munkras (KM) algorithm to allocate cellular users' uplink channel for D2D users in the cluster, which means optimally match the D2D user with the cellular user. Finally, Considering the system capacity, the users' quality of service (Qos) requirements and the D2D users' transmit power, a non-cooperative game model is constructed to optimize the transmit power of D2D users and the existence of its Nash equilibrium is proved. Simulation results turn out that it can effectively promote the system capacity while guaranteeing the communication quality of each user.

关键词： device-to-device capacity fcm algorithm KM algorithm game theory

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fcm Application Research of Tax Decision Support System

FCM Application Research of Tax Decision Support System

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International Conference on Business, Economics, Management Science

作者： Yinghui Jia School of Public Finance and Taxation Hebei University of Economics and Business

ISBN: (纸本)9781510888050

in order to improve the management of tax decisions, tax decision support system has been added to the current tax decisions. Its purpose is mainly to analyze the basic data generated in tax decisions, so as to summarize the operation rules and provide more auxiliary information. This paper will study the application of FMC algorithm in the tax decision support system, summarize the connotation of tax decision and FMC algorithm, summarize its significance, and combine the mathematical model and classification of fuzzy clustering analysis, so as to obtain the specific application of FMC algorithm in the tax decision support system.

关键词： tax decision Support system fcm algorithm

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A New PV Array Fault Diagnosis Method Using Fuzzy C-Mean Clustering and Fuzzy Membership algorithm

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ENERGIES 2018年第1期11卷 238页

作者： Zhao, Qiang Shao, Shuai Lu, Lingxing Liu, Xin Zhu, Honglu North China Elect Power Univ Sch Control & Comp Engn Beijing 102206 Peoples R China North China Elect Power Univ Sch Renewable Energy Beijing 102206 Peoples R China North China Elect Power Univ State Key Lab Alternate Elect Power Syst Renewabl Beijing 102206 Peoples R China

Photovoltaic (PV) power station faults in the natural environment mainly occur in the PV array, and the accurate fault diagnosis is of particular significance for the safe and efficient PV power plant operation. The PV array's electrical behavior characteristics under fault conditions is analyzed in this paper, and a novel PV array fault diagnosis method is proposed based on fuzzy C-mean (fcm) and fuzzy membership algorithms. Firstly, clustering analysis of PV array fault samples is conducted using the fcm algorithm, indicating that there is a fixed relationship between the distribution characteristics of cluster centers and the different fault, then the fault samples are classified effectively. The membership degrees of all fault data and cluster centers are then determined by the fuzzy membership algorithm for the final fault diagnosis. Simulation analysis indicated that the diagnostic accuracy of the proposed method was 96%. Field experiments further verified the correctness and effectiveness of the proposed method. In this paper, various types of fault distribution features are effectively identified by the fcm algorithm, whether the PV array operation parameters belong to the fault category is determined by fuzzy membership algorithm, and the advantage of the proposed method is it can classify the fault data from normal operating data without foreknowledge.

关键词： PV array fcm algorithm cluster analysis fault diagnosis membership algorithm

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